Patent Grant
10652835
The subject disclosure is related to a method and apparatus for increasing performance of communication paths for communication nodes.
In most communication environments involving short range or long range wireless communications, interference from unexpected wireless sources can impact the performance of a communication system leading to lower throughput, dropped calls, reduced bandwidth which can cause traffic congestion, or other adverse effects, which are undesirable.
Some service providers of wireless communication systems have addressed interference issues by adding more communication nodes, policing interferers, or utilizing antenna steering techniques to avoid interferers.
Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
The subject disclosure describes, among other things, illustrative embodiments for detecting and mitigating interference signals. Other embodiments are included in the subject disclosure.
One embodiment of the subject disclosure includes a system having a memory to store instructions, and a processor coupled to the memory. Upon execution of the instructions by the processor, the processor can perform operations including measuring signals generated by communication devices directed to the cell site and measuring noise levels for a plurality of paths. The processor can also perform operations including measuring interference signals for the plurality of paths according to the adaptive inter-cell interference threshold and, in turn, determining Signal to Interference plus Noise Ratio (SINR) measurements for the plurality of paths according to the signals, the noise levels and the interference signals measured for the plurality of paths. The processor can further perform operations including identifying a SINR measurement from the SINR measurements that is below SINR threshold and, in turn, initiating a corrective action to improve the SINR measurement of an affected path of the plurality of paths falling below the SINR threshold.
One embodiment of the subject disclosure includes a machine-readable storage medium, comprising instructions, which when executed by a processor, can cause the processor to perform operations including obtaining a resource block schedule for each of a plurality of paths and, in turn, identifying resource blocks in the plurality of paths that are not in use. The instructions can also cause the processor to perform operations including measuring for each of the plurality of paths an energy of the resource blocks not in use to determine an average thermal noise level for each path and, in turn, determining for each of the plurality of paths an adaptive inter-cell interference threshold according to the average thermal noise level of each path. The instructions can further cause the processor to perform operations including measuring interference signals according to the adaptive inter-cell interference threshold for each of the plurality of paths, measuring signals and noise levels for each of the plurality of paths, and, in turn, determining Signal to Interference plus Noise Ratio (SINR) measurements for the plurality of paths according to the signals, the noise levels and the interference signals measured for the plurality of paths. The instructions can cause the processor to perform operations including identifying a SINR measurement that is below a SINR threshold and, in turn, initiating a corrective action to improve the SINR measurement of an affected path of the plurality of paths falling below the SINR threshold.
One embodiment of the subject disclosure includes a method, performed by a system comprising a processor, including obtaining performance measurements. The performance measurements can be determined from measurements associated with signals generated by communication devices, noise levels in a spectral portion used by communication devices to transmit the signals, and interference signals exceeding an adaptive inter-cell interference threshold. The method can also include identifying a performance measurement from the performance measurements that is below performance threshold and, in turn, initiating corrective action to improve the performance measurement of an affected network element falling below the performance threshold.
One embodiment of the subject disclosure includes a method for receiving, by a circuit, a signal generated by a communication device, detecting, by the circuit, an interference in the signal, the interference generated by one or more transmitters, and the interference determined from signal characteristics associated with a signaling protocol used by the one or more transmitters, wherein the one or more transmitters are unassociated with the communication device generating the signal, and performing, by the circuit, signal conditioning on the signal to reduce the interference.
One embodiment of the subject disclosure includes a device having an antenna, and a circuit coupled to the antenna. The circuit can facilitate operations including receiving, via the antenna, a signal generated by a communication device, detecting an interference in the signal, the interference generated by one or more transmitters, and the interference determined from signal characteristics associated with a signaling protocol used by the one or more transmitters, wherein the one or more transmitters are unassociated with the communication device generating the signal, and performing signal conditioning on the signal to reduce the interference.
One embodiment of the subject disclosure includes a machine-readable storage medium, comprising executable instructions that, when executed by a circuit, facilitate performance of operations. The operations can include receiving, via an antenna, a signal generated by a communication device, detecting an interference in the signal, the interference generated by one or more transmitters, and the interference determined from signal characteristics associated with a signaling protocol used by the one or more transmitters, wherein the one or more transmitters are unassociated with the communication device generating the signal, and performing signal conditioning on the signal to reduce the interference.
As shown in
During operation, the mobile units 12, 13A, 13B, 13C, and 13D exchange voice, data or other information with one of the base stations 14, 16, each of which is connected to a conventional land line communication network. For example, information, such as voice information, transferred from the mobile unit 12 to one of the base stations 14, 16 is coupled from the base station to the communication network to thereby connect the mobile unit 12 with, for example, a land line telephone so that the land line telephone may receive the voice information. Conversely, information, such as voice information may be transferred from a land line communication network to one of the base stations 14, 16, which in turn transfers the information to the mobile unit 12.
The mobile units 12, 13A, 13B, 13C, and 13D and the base stations 14, 16 may exchange information in either narrow band or wide band format. For the purposes of this description, it is assumed that the mobile unit 12 is a narrowband unit and that the mobile units 13A, 13B, 13C, and 13D are wideband units. Additionally, it is assumed that the base station 14 is a narrowband base station that communicates with the mobile unit 12 and that the base station 16 is a wideband digital base station that communicates with the mobile units 13A, 13B, 13C, and 13D.
Narrow band format communication takes place using, for example, narrowband 200 kilohertz (KHz) channels. The Global system for mobile phone systems (GSM) is one example of a narrow band communication system in which the mobile unit 12 communicates with the base station 14 using narrowband channels. Alternatively, the mobile units 13A, 13B, 13C, and 13D communicate with the base stations 16 using a form of digital communications such as, for example, code-division multiple access (CDMA), Universal Mobile Telecommunications System (UMTS), 3GPP Long Term Evolution (LTE), or other next generation wireless access technologies. CDMA digital communication, for instance, takes place using spread spectrum techniques that broadcast signals having wide bandwidths, such as, for example, 1.2288 megahertz (MHz) bandwidths.
The switching station 18 is generally responsible for coordinating the activities of the base stations 14, 16 to ensure that the mobile units 12, 13A, 13B, 13C, and 13D are constantly in communication with the base station 14, 16 or with some other base stations that are geographically dispersed. For example, the switching station 18 may coordinate communication handoffs of the mobile unit 12 between the base stations 14 and another base station as the mobile unit 12 roams between geographical areas that are covered by the two base stations.
One particular problem that may arise in the telecommunication system 10 is when the mobile unit 12 or the base station 14, each of which communicates using narrowband channels, interferes with the ability of the base station 16 to receive and process wideband digital signals from the digital mobile units 13A, 13B, 13C, and 13D. In such a situation, the narrowband signal transmitted from the mobile unit 12 or the base station 14 may interfere with the ability of the base station 16 to properly receive wideband communication signals.
As will be readily appreciated, the base station 16 may receive and process wideband digital signals from more than one of the digital mobile units 13A, 13B, 13C, and 13D. For example, the base station 16 may be adapted to receive and process four CDMA carriers 40A-40D that fall within a multi-carrier CDMA signal 40, as shown in
As disclosed in detail hereinafter, a system and/or a method for multiple channel adaptive filtering or interference suppression may be used in a communication system. In particular, such a system or method may be employed in a communication system to protect against, or to report the presence of, interference, which has deleterious effects on the performance of the communication system. Additionally, such a system and method may be operated to eliminate interference in CDMA carriers having other CDMA carriers adjacent thereto.
The foregoing system and methods can also be applied to other protocols such as AMPS, GSM, UMTS, LTE, VoLTE, 802.11xx, 5G, next generation wireless protocols, and so on. Additionally, the terms narrowband and wideband referred to above can be replaced with sub-bands, concatenated bands, bands between carrier frequencies (carrier aggregation), and so on, without departing from the scope of the subject disclosure. It is further noted that the term interference can represent emissions within band (narrowband or wideband), out-of-band interferers, interference sources outside cellular (e.g., TV stations, commercial radio or public safety radio), interference signals from other carriers (inter-carrier interference), interference signals from user equipment (UEs) operating in adjacent base stations, and so on. Interference can represent any foreign signal that can affect communications between communication devices (e.g., a UE served by a particular base station).
As shown in
It will be readily understood that the illustrations of
Referring back to
Each of the components 50-60 of the base station 16 shown in
During operation of the base station 16, the antenna 50 receives CDMA carrier signals that are broadcast from the mobile unit 13A, 13B, 13C and 13D and couples such signals to the amplifier 52, which amplifies the received signals and couples the amplified signals to the diplexer 54. The diplexer 54 splits the amplified signal from the amplifier 52 and essentially places copies of the amplified signal on each of its output lines. The adaptive front end module 56 receives the signal from the diplexer 54 and, if necessary, filters the CDMA carrier signal to remove any undesired interference and couples the filtered CDMA carrier signal to the receiver B 59.
As noted previously,
However, as noted previously, it is possible that the CDMA carrier signal transmitted by the mobile units 13A-13D and received by the antenna 50 has a frequency spectrum as shown in
The filtered multi-carrier CDMA signal 43 has the interferer 46 removed, as shown by the notch 46A. The filtered multi-carrier CDMA signal 43 is then coupled from the adaptive front end module 56 to the receiver B 59, so that the filtered multi-carrier CDMA signal 43 may be demodulated. Although some of the multi-carrier CDMA signal 42 was removed during filtering by the adaptive front end module 56, sufficient multi-carrier CDMA signal 43 remains to enable the receiver B 59 to recover the information that was broadcast by mobile unit(s). Accordingly, in general terms, the adaptive front end module 56 selectively filters multi-carrier CDMA signals to remove interference therefrom. Further detail regarding the adaptive front end module 56 and its operation is provided below in conjunction with
As shown in
By providing tuning coefficient data to the filter stage 78, the adaptive front end controller 68 acts to pre-filter the received RF signal before the signal is sent to the RF tuner 72, which analyzes the filtered RF signal for integrity and/or for other applications such as cognitive radio applications. After filtering, the radio tuner 72 may then perform channel demodulation, data analysis, and local broadcasting functions. The RF tuner 72 may be considered the receiver side of an overall radio tuner, while RF tuner 72′ may be considered the transmitter side of the same radio tuner. Prior to sending the filtered RF signal, the sampler 76 may provide an indication of the filtered RF signal to the controller 80 in a feedback manner for further adjusting of the adaptive filter stage 78.
In some examples, the adaptive front-end controller 68 is synchronized with the RF tuner 72 by sharing a master clock signal communicated between the two. For example, cognitive radios operating on a 100 μs response time can be synchronized such that for every clock cycle the adaptive front end analyzes the input RF signal, determines an optimal configuration for the adaptive filter stage 78, filters that RF signal into the filtered RF signal and communicates the same to the radio tuner 72 for cognitive analysis at the radio. By way of example, cellular phones may be implemented with a 200 μs response time on filtering. By implementing the adaptive front end controller 68 using a field programmable gate array configuration for the filter stage, wireless devices may identify not only stationary interference, but also non-stationary interference, of arbitrary bandwidths on that moving interferer.
In some implementations, the adaptive front-end controller 68 may filter interference or noise from the received incoming RF signal and pass that filtered RF signal to the tuner 72. In other examples, such as cascaded configurations in which there are multiple adaptive filter stages, the adaptive front-end controller 68 may be configured to apply the filtered signal to an adaptive band pass filter stage to create a passband portion of the filtered RF signal. For example, the radio tuner 72 may communicate information to the controller 80 to instruct the controller that the radio is only looking at a portion of an overall RF spectrum and thus cause the adaptive front-end controller 68 not to filter certain portions of the RF spectrum and thereby band pass only those portions. The integration between the radio tuner 72 and the adaptive front-end controller 68 may be particularly useful in dual-band and tri-band applications in which the radio tuner 72 is able to communicate over different wireless standards, such as GSM, UMTS, or LTE standards.
The algorithms that may be executed by the controller 80 are not limited to interference detection and filtering of interference signals. In some configurations the controller 80 may execute a spectral blind source separation algorithm that looks to isolate two sources from their convolved mixtures. The controller 80 may execute a signal to interference noise ratio (SINR) output estimator for all or portions of the RF signal. The controller 80 may perform bidirectional transceiver data link operations for collaborative retuning of the adaptive filter stage 78 in response to instructions from the radio tuner 72 or from data the transmitter stage 64. The controller 80 can determine filter tuning coefficient data for configuring the various adaptive filters of stage 78 to properly filter the RF signal. The controller 80 may also include a data interface communicating the tuning coefficient data to the radio tuner 72 to enable the radio tuner 72 to determine filtering characteristics of the adaptive filter 78.
In one embodiment the filtered RF signal may be converted from a digital signal to an analog signal within the adaptive front-end controller 68. This allows the controller 80 to integrate in a similar manner to conventional RF filters. In other examples, a digital interface may be used to connect the adaptive front-end controller 68 with the radio tuner 72, in which case the ADC 70 would not be necessary.
The above discussion is in the context of the receiver stage 62. Similar elements are shown in the transmitter stage 64, but bearing a prime. The elements in the transmitter stage 64 may be similar to those of the receiver 62, with the exception of the digital to analog converter (DAC) 70′ and other adaptations to the other components shown with a prime in the reference numbers. Furthermore, some or all of these components may in fact be executed by the same corresponding structure in the receiver stage 62. For example, the RF receiver tuner 72 and the transmitter tuner 72′ may be performed by a single tuner device. The same may be true for the other elements, such as the adaptive filter stages 78 and 78′, which may both be implemented in a single FPGA, with different filter elements in parallel for full duplex (simultaneous) receive and transmit operation.
A DSP 110 is coupled to the FPGA 108 and executes signal processing algorithms that may include a spectral blind source separation algorithm, a signal to interference noise ratio (SINR) output estimator, bidirectional transceiver data line operation for collaborative retuning of the adaptive filter stage in response to instructions from the tuner, and/or an optimal filter tuning coefficients algorithm.
FPGA 108 is also coupled to a PCI target 112 that interfaces the FPGA 108 and a PCI bus 114 for communicating data externally. A system clock 118 provides a clock input to the FPGA 108 and DSP 110, thereby synchronizing the components. The system clock 118 may be locally set on the adaptive front-end controller, while in other examples the system claim 118 may reflect an external master clock, such as that of a radio tuner. The FPGA 108, DSP 110, and PCI target 112, designated collectively as signal processing module 116, will be described in greater detail below. In the illustrated example, the adaptive front-end controller 100 includes a microcontroller 120 coupled to the PCI bus 114 and an operations, alarms and metrics (OA&M) processor 122. Although they are shown and described herein as separate devices that execute separate software instructions, those having ordinary skill in the art will readily appreciate that the functionality of the microcontroller 120 and the OA&M processor 122 may be merged into a single processing device. The microcontroller 120 and the OA&M processor 122 are coupled to external memories 124 and 126, respectively. The microcontroller 120 may include the ability to communicate with peripheral devices, and, as such, the microcontroller 120 may be coupled to a USB port, an Ethernet port, or an RS232 port, among others (though none shown). In operation, the microcontroller 120 may locally store lists of channels having interferers or a list of known typically available frequency spectrum bands, as well as various other parameters. Such a list may be transferred to a reporting and control facility or a base station, via the OA&M processor 122, and may be used for system diagnostic purposes.
The aforementioned diagnostic purposes may include, but are not limited to, controlling the adaptive front-end controller 100 to obtain particular information relating to an interferer and re-tasking the interferer. For example, the reporting and control facility may use the adaptive front-end controller 100 to determine the identity of an interferer, such as a mobile unit, by intercepting the electronic serial number (ESN) of the mobile unit, which is sent when the mobile unit transmits information on the channel. Knowing the identity of the interferer, the reporting and control facility may contact infrastructure that is communicating with the mobile unit (e.g., the base station) and may request the infrastructure to change the transmit frequency for the mobile unit (i.e., the frequency of the channel on which the mobile unit is transmitting) or may request the infrastructure to drop communications with the interfering mobile unit altogether.
Additionally, in a cellular configuration (e.g., a system based on a configuration like that of
The FPGA 108 can provide a digital output coupled to a digital to analog converter (DAC) 128 that converts the digital signal to an analog signal which may be provided to a filter 130 to generate a filtered RF output to be broadcast from the base station or mobile station. The digital output at the FPGA 108, as described, may be one of many possible outputs. For example, the FPGA 108 may be configured to output signals based on a predefined protocol such as a Gigabit Ethernet output, an open base station architecture initiative (OBSAI) protocol, or a common public radio interface (CPRI) protocol, among others.
It is further noted that the aforementioned diagnostic purposes may also include creating a database of known interferers, the time of occurrence of the interferers, the frequency of occurrence of the interferers, spectral information relating to the interferers, a severity analysis of the interferers, and so on. The identity of the interferers may be based solely on spectral profiles of each interferer that can be used for identification purposes. Although the aforementioned illustrations describe a mobile unit 12 as an interferer, other sources of interference are possible. Any electronic appliance that generates electromagnetic waves such as, for example, a computer, a set-top box, a child monitor, a wireless access point (e.g., WiFi, ZigBee, Bluetooth, etc.) can be a source of interference. In one embodiment, a database of electronic appliances can be analyzed in a laboratory setting or other suitable testing environment to determine an interference profile for each appliance. The interference profiles can be stored in a database according to an appliance type, manufacturer, model number, and other parameters that may be useful in identifying an interferer. Spectral profiles provided by, for example, the OA&M processor 108 to a diagnostic system can be compared to a database of previously characterized interferers to determine the identity of the interference when a match is detected.
A diagnostic system, whether operating locally at the adaptive front end controller, or remotely at a base station, switching station, or server system, can determine the location of the interferer near the base station (or mobile unit) making the detection, or if a more precise location is required, the diagnostic system can instruct several base stations (or mobile units) to perform triangulation analysis to more precisely locate the source of the interference if the interference is frequent and measureable from several vantage points. With location data, interference identity, timing and frequency of occurrence, the diagnostic system can generate temporal and geographic reports showing interferers providing field personnel a means to assess the volume of interference, its impact on network performance, and it may provide sufficient information to mitigate interference by means other than filtering, such as, for example, interference avoidance by way of antenna steering at the base station, beam steering, re-tasking an interferer when possible, and so on.
The I and Q components from the digital down converter 152 are provided to the DSP 110 which implements a detection algorithm and in response provides the tunable IIR/FIR filter 154 with tuning coefficient data that tunes the IIR and/or FIR filters 154 to specific notch (or band stop) and/or band pass frequencies, respectively, and specific bandwidths. The tuning coefficient data, for example, may include a frequency and a bandwidth coefficient pair for each of the adaptive filters, which enables the filter to tune to a frequency for band pass or band stop operation and the bandwidth to be applied for that operation. The tuning coefficient data corresponding to a band pass center frequency and bandwidth may be generated by the detection algorithm and passed to a tunable FIR filter within the IIR/FIR filter 154. The filter 154 may then pass all signals located within a passband of the given transmission frequency. Tuning coefficient data corresponding to a notch (or band stop) filter may be generated by the detection algorithm and then applied to an IIR filter within the IIR/FIR filter 154 to remove any interference located within the passband of the band pass filter. The tuning coefficient data generated by the detection algorithm are implemented by the tunable IIR/FIR filters 154 using mathematical techniques known in the art. In the case of a cognitive radio, upon implementation of the detection algorithm, the DSP 110 may determine and return coefficients corresponding to a specific frequency and bandwidth to be implemented by the tunable IIR/FIR filter 154 through a DSP/PCI interface 158. Similarly, the transfer function of a notch (or band stop) filter may also be implemented by the tunable IIR/FIR filter 154. Of course other mathematical equations may be used to tune the IIR/FIR filters 154 to specific notch, band stop, or band pass frequencies and to a specific bandwidth.
After the I and Q components are filtered to the appropriate notch (or band stop) or band pass frequency at a given bandwidth, a digital up converter 156, such as a polyphase interpolator, converts the signal back to the original data rate, and the output of the digital up converter is provided to the DAC 128.
A wireless communication device capable to be operated as a dual- or tri-band device communicating over multiple standards, such as over UMTS and LTE may use the adaptive digital filter architecture embodiments as described above. For example, a dual-band device (using both LTE and UMTS) may be preprogrammed within the DSP 110 to transmit first on LTE, if available, and on UMTS only when outside of a LTE network. In such a case, the IIR/FIR filter 154 may receive tuning coefficient data from the DSP 110 to pass all signals within a LTE range. That is, the tuning coefficient data may correspond to a band pass center frequency and bandwidth adapted to pass only signals within the LTE range. The signals corresponding to a UMTS signal may be filtered, and any interference caused by the UMTS signal may be filtered using tuning coefficients, received from the DSP 110, corresponding to a notch (or band stop) frequency and bandwidth associated with the UMTS interference signal.
Alternatively, in some cases it may be desirable to keep the UMTS signal in case the LTE signal fades quickly and the wireless communication device may need to switch communication standards rapidly. In such a case, the UMTS signal may be separated from the LTE signal, and both passed by the adaptive front-end controller. Using the adaptive digital filter, two outputs may be realized, one output corresponding to the LTE signal and one output corresponding to a UMTS signal. The DSP 110 may be programmed to again recognize the multiple standard service and may generate tuning coefficients corresponding to realize a filter, such as a notch (or band stop) filter, to separate the LTE signal from the UMTS signal. In such examples, an FPGA may be programmed to have parallel adaptive filter stages, one for each communication band.
To implement the adaptive filter stages, in some examples, the signal processing module 116 is pre-programmed with general filter architecture code at the time of production, for example, with parameters defining various filter types and operation. The adaptive filter stages may then be programmed, through a user interface or other means, by the service providers, device manufactures, etc., to form the actual filter architecture (parallel filter stages, cascaded filter stages, etc.) for the particular device and for the particular network(s) under which the device is to be used. Dynamic flexibility can be achieved during runtime, where the filters may be programmed to different frequencies and bandwidths, each cycle, as discussed herein.
One method of detecting a signal having interference is by exploiting the noise like characteristics of a signal. Due to such noise like characteristics of the signal, a particular measurement of a channel power gives no predictive power as to what the next measurement of the same measurement channel may be. In other words, consecutive observations of power in a given channel are un-correlated. As a result, if a given measurement of power in a channel provides predictive power over subsequent measurements of power in that particular channel, thus indicating a departure from statistics expected of a channel without interference, such a channel may be determined to contain interference.
Specifically, the PDFs 204 include probability distribution of power in a given channel, which is the likelihood p(x) of measuring a power x in a given channel, for a DSSS signal carrying one mobile unit (212), for a DSSS signal carrying ten mobile units (214), and for a DSSS signal carrying twenty mobile units (210). For example, for the PDF 212, representing a DSSS signal carrying one mobile unit, the distribution p(x) is observed to be asymmetric, with an abbreviated high power tail. In this case, any channel having power higher than the high power tail of the PDF 212 may be considered to have an interference signal.
The CCDFs 206 denote the likelihood that a power measurement in a channel will exceed a given mean power α, by some value α/σ, wherein σ is standard deviation of the power distribution. Specifically, the CCDFs 206 include an instance of CCDF for a DSSS signal carrying one mobile unit (220), an instance of CCDF for a DSSS signal carrying ten mobile units (222), and an instance of CCDF for a DSSS signal carrying twenty mobile units (224). Thus, for example, for a DSSS signal carrying one mobile unit, the likelihood of any channel having the ratio α/σ of 10 dB or more is 0.01%. Therefore, an optimal filter can be tuned to such a channel having excess power.
One method of detecting such a channel having interference is by exploiting the noise like characteristic of a DSSS signal. Due to such noise like characteristic of DSSS signal, a particular measurement of a channel power gives no predictive power as to what the next measurement of the same measurement channel may be. In other words, consecutive observations of power in a given channels are un-correlated. As a result, if a given measurement of power in a channel provides predictive power over subsequent measurements of power in that particular channel, thus indicating a departure from statistics expected of a channel without interference, such a channel may be determined to contain interference.
At block 304 the adaptive front end controller can determine the number of sequences m of a DSSS signal that may be required to be analyzed to determine channels having interference. A user may provide such a number m based on any pre-determined criteria. For example, a user may provide m to be equal to four, meaning that four consecutive DSSS signals need to be analyzed to determine if any of the channels within that DSSS signal spectrum includes an interference signal. As one of ordinary skill in the art would appreciate, the higher is the selected value of m, the more accurate will be the interference detection. However, the higher the number m is, the higher is the delay in determining whether a particular DSSS signal had an interference present in it, subsequently, resulting in a longer delay before a filter is applied to the DSSS signal to remove the interference signal.
Generally, detection of an interference signal may be performed on a rolling basis. That is, at any point in time, m previous DSSS signals may be used to analyze presence of an interference signal. The earliest of such m interference signals may be removed from the set of DSSS signals used to determine the presence of an interference signal on a first-in-first-out basis. However, in an alternate embodiment, an alternate sampling method for the set of DSSS signals may also be used.
At block 306 the adaptive front end controller can select x channels having the highest signal strength from each of the m most recent DSSS signals scanned at the block 302. The number x may be determined by a user. For example, if x is selected to be equal to three, the block 306 may select three highest channels from each of the m most recent DSSS signals. The methodology for selecting x channels having highest signal strength from a DSSS signal is described in further detail in
After having determined the x channels having the highest signal strengths in each of the m DSSS signals, at block 308 the adaptive front end controller can compare these x channels to determine if any of these highest strength channels appear more than once in the m DSSS signals. In case of the example above, the adaptive front end controller at block 308 may determine that the channels 15 and 27 are present among the highest strength channels for each of the last three DSSS signals, while channel 35 is present among the highest strength channels for at least two of the last three DSSS signals.
Such consistent appearance of channels having highest signal strength over subsequent DSSS signals indicate that channels 15 and 27, and probably the channel 35, may have an interference signal super-imposed on them. At block 310 the adaptive front end controller may use such information to determine which channels may have interference. For example, based on the number of times a given channel appears in the selected highest signal strength channels, the adaptive front end controller at block 310 may determine the confidence level that may be assigned to a conclusion that a given channel contains an interference signal.
Alternatively, at block 310 the adaptive front end controller may determine a correlation factor for each of the various channels appearing in the x selected highest signal strength channels and compare the calculated correlation factors with a threshold correlation factor to determine whether any of the x selected channels has correlated signal strengths. Calculating a correlation factor based on a series of observations is well known to those of ordinary skill in the art and therefore is not illustrated in further detail herein. The threshold correlation factor may be given by the user of the interference detection program 300.
Note that while in the above illustrated embodiment, the correlation factors of only the selected highest signal strength channels are calculated, in an alternate embodiment, correlation factors of all the channels within the DSSS signals may be calculated and compared to the threshold correlation factor.
Empirically, it may be shown that when m is selected to be equal to three, for a clean DSSS signal, the likelihood of having at least one match among the higher signal strength channels is 0.198, the likelihood of having at least two matches among the higher signal strength channels is 0.0106, and the likelihood of having at least three matches among the higher signal strength channels is 9.38×10−5. Thus, the higher the number of matches, the lesser is the likelihood of having a determination that one of the x channels contains an interference signal (i.e., a false positive interference detection). It may be shown that if the number of scans m is increased to, say four DSSS scans, the likelihood of having such matches in m consecutive scans is even smaller, thus providing higher confidence that if such matches are found to be present, they indicate presence of interference signal in those channels.
To identify the presence of interference signals with even higher level of confidence, at block 312 the adaptive front end controller may decide whether to compare the signal strengths of the channels determined to have an interference signal with a threshold. If at block 312 the adaptive front end controller decides to perform such a comparison, at block 314 the adaptive front end controller may compare the signal strength of each of the channels determined to have an interference with a threshold level. Such comparing of the channel signal strengths with a threshold may provide added confidence regarding the channel having an interference signal so that when a filter is configured according to the channel, the probability of removing a non-interfering signal is reduced. However, a user may determine that such added confidence level is not necessary and thus no such comparison to a threshold needs to be performed. In which case, at block 316 the adaptive front end controller stores the interference signals in a memory.
After storing the information about the channels having interference signals, at block 318 the adaptive front end controller selects the next DSSS signal from the signals scanned and stored at block 302. At block 318 the adaptive front end controller may cause the first of the m DSSS signals to be dropped and the newly added DSSS signal is added to the set of m DSSS signals that will be used to determine presence of an interference signal (first-in-first-out). Subsequently, at block 306 the process of determining channels having interference signals is repeated by the adaptive front end controller. Finally, at block 320 the adaptive front end controller may select and activate one or more filters that are located in the path of the DSSS signal to filter out any channel identified as having interference in it.
Now referring to
At block 352 the adaptive front end controller may sort signal strengths of each of the n channels within a given DSSS signal. For example, if a DSSS signal has 41 channels, at block 352 the adaptive front end controller may sort each of the 41 channels according to its signal strengths. Subsequently, at block 354 the adaptive front end controller may select the x highest strength channels from the sorted channels and store information identifying the selected x highest strength channels for further processing. An embodiment of the high strength channels detection program 350 may simply use the selected x highest strength channels from each scan of the DSSS signals to determine any presence of interference in the DSSS signals. However, in an alternate embodiment, additional selected criteria may be used.
Subsequently, at block 356 the adaptive front end controller can determine if it is necessary to compare the signal strengths of the x highest strength channels to any other signal strength value, such as a threshold signal strength, etc., where such a threshold may be determined using the average signal strength across the DSSS signal. For example, at block 356 the adaptive front end controller may use a criterion such as, for example: “when x is selected to be four, if at least three out of four of the selected channels have also appeared in previous DSSS signals, no further comparison in necessary.” Another criterion may be, for example: “if any of the selected channels is located at the fringe of the DSSS signal, the signal strengths of such channels should be compared to a threshold signal strength.” Other alternate criteria may also be provided.
If at block 356 the adaptive front end controller determines that no further comparison of the signal strengths of the selected x channels is necessary, at block 358 the adaptive front end controller stores information about the selected x channels in a memory for further processing. If at block 356 the adaptive front end controller determines that it is necessary to apply further selection criteria to the selected x channels, the adaptive front end controller returns to block 360. At block 360 the adaptive front end controller may determine a threshold value against which the signal strengths of each of the x channels are compared based on a predetermined methodology.
For example, in an embodiment, at block 360 the adaptive front end controller may determine the threshold based on the average signal strength of the DSSS signal. The threshold signal strength may be the average signal strength of the DSSS signal or a predetermined value may be added to such average DSSS signal to derive the threshold signal strength.
Subsequently, at block 362 the adaptive front end controller may compare the signal strengths of the selected x channels to the threshold value determined at block 360. Only the channels having signal strengths higher than the selected threshold are used in determining presence of interference in the DSSS signal. Finally, at block 364 the adaptive front end controller may store information about the selected x channels having signal strengths higher than the selected threshold in a memory. As discussed above, the interference detection program 300 may use such information about the selected channels to determine the presence of interference signal in the DSSS signal.
The interference detection program 300 and the high strength channel detection program 350 may be implemented by using software, hardware, firmware or any combination thereof. For example, such programs may be stored on a memory of a computer that is used to control activation and deactivation of one or more notch filters. Alternatively, such programs may be implemented using a digital signal processor (DSP) which determines the presence and location of interference channels in a dynamic fashion and activates/de-activates one or more filters.
The interference detection program 370 may start scanning various DSSS signals 372-374 starting from the first DSSS signal 372. As discussed above at block 304 the adaptive front end controller determines the number m of the DSSS signals 372-374 that are to be scanned. Because the interference signal 378 causes the signal strength of a particular channel to be consistently higher than the other channels for a number of consecutive scans of the DSSS signals 372-374 at block 210 the adaptive front end controller identifies a particular channel having an interference signal present. Subsequently, at block 320 the adaptive front end controller will select and activate a filter that applies the filter function as described above, to the channel having interference.
The graph 370 also illustrates the average signal strengths of each of the DSSS signals 372-374 by a line 376. As discussed above, at block 362 the adaptive front end controller may compare the signal strengths of each of the x selected channels from the DSSS signals 372-374 with the average signal strength, as denoted by line 376, in that particular DSSS signal.
Now referring to
The foregoing interference detection and mitigation embodiments can further be adapted for detecting and mitigating interference in long-term evolution (LTE) communication systems.
LTE transmission consists of a combination of Resource Blocks (RB's) which have variable characteristics in frequency and time. A single RB can be assigned to a user equipment, specifically, a 180 KHz continuous spectrum utilized for 0.5-1 msec. An LTE band can be partitioned into a number of RBs which could be allocated to individual communication devices for specified periods of time for LTE transmission. Consequently, an LTE spectrum has an RF environment dynamically variable in frequency utilization over time.
LTE utilizes different media access methods for downlink (orthogonal frequency-division multiple access; generally, referred to as OFDMA) and uplink (single carrier frequency-division multiple access; generally, referred to as SC-FDMA). For downlink communications, each RB contains 12 sub-carriers with 15 KHz spacing. Each sub-carrier can be used to transmit individual bit information according to the OFDMA protocol. For uplink communications, LTE utilizes a similar RB structure with 12 sub-carriers, but in contrast to downlink, uplink data is pre-coded for spreading across 12 sub-carriers and is transmitted concurrently on all 12 sub-carriers.
The effect of data spreading across multiple sub-carriers yields a transmission with spectral characteristics similar to a CDMA/UMTS signal. Hence, similar principles of interference detection can be applied within an instance of SC-FDMA transmission from an individual communication device—described herein as user equipment (UE). However, since each transmission consists of unknown RB allocations with unknown durations, such a detection principle can only be applied separately for each individual RB within a frequency and specific time domain. If a particular RB is not used for LTE transmission at the time of detection, the RF spectrum will present a thermal noise which adheres to the characteristics of a spread spectrum signal, similar to a CDMA/UMTS signal.
Co-channel, as well as other forms of interference, can cause performance degradation to SC-FDMA and OFDMA signals when present.
Time averaging system (TAS) can be achieved with a boxcar (rolling) average, in which the TAS is obtained as a linear average of a Q of previous spectrum samples, with Q being a user-settable parameter. The Q value determines the “strength” of the averaging, with higher Q value resulting in a TAS that is more strongly smoothed in time and less dependent on short duration transient signals. Due to the frequency-hopped characteristic of SC-FDMA/OFDMA signals, which are composed of short duration transients, the TAS of such signals is approximately flat. It will be appreciated that TAS can also be accomplished by other methods such as a forgetting factor filter.
In one embodiment, an adaptive threshold can be determined by a method 500 as depicted in
The adaptive front end module 56 repeats these steps until the spectrum of interest has been fully scanned for Q cycles, thereby producing the following power level sample sets:
The adaptive front end module 56 in step 504, calculates averages for each of the power level sample sets as provided below:
In one embodiment, the adaptive front end module 56 can be configured to determine at step 506 the top “m” averages (e.g., the top 3 averages) and dismiss these averages from the calculations. The variable “m” can be user-supplied or can be empirically determined from field measurements collected by one or more base stations utilizing an adaptive front end module 56. This step can be used to avoid skewing a baseline average across all frequency increments from being too high, resulting in a threshold calculation that may be too conservative. If step 506 is invoked, a baseline average can be determined in step 508 according to the equation: Baseline Avg=(a1+a2+ . . . +az−averages that have been dismissed)/(x−m). If step 506 is skipped, the baseline average can be determined from the equation: Baseline Avg=(a1+a2+ . . . +az)/x. Once the baseline average is determined in step 508, the adaptive front end module 56 can proceed to step 510 where it calculates a threshold according to the equation: Threshold=ydB offset+Baseline Avg. The ydB offset can be user defined or empirically determined from field measurements collected by one or more base stations utilizing an adaptive front end module 56.
Once a cycle of steps 502 through 510 have been completed, the adaptive front end module 56 can monitor at step 512 interference per frequency increment of the spectrum being scanned based on any power levels measured above the threshold 602 calculated in step 510 as shown in
Method 500 can utilize any of the embodiments in the illustrated flowcharts described above to further enhance the interference determination process. For example, method 500 of
When one or more interferers are detected in step 512, the adaptive front end module 56 can mitigate the interference at step 514 by configuring one or more filters to suppress the one or more interferers as described above. When there are limited resources to suppress all interferers, the adaptive front end module 56 can use a prioritization scheme to address the most harmful interference as discussed above.
In one embodiment, the adaptive front end module 56 can submit a report to a diagnostic system that includes information relating to the interferers detected. The report can including among other things, a frequency of occurrence of the interferer, spectral data relating to the interferer, an identification of the base station from which the interferer was detected, a severity analysis of the interferer (e.g., bit error rate, packet loss rate, or other traffic information detected during the interferer), and so on. The diagnostic system can communicate with other base stations with other operable adaptive front end module 56 to perform macro analysis of interferers such as triangulation to locate interferers, identity analysis of interferers based on a comparison of spectral data and spectral profiles of known interferers, and so on.
In one embodiment, the reports provided by the adaptive front end module 56 can be used by the diagnostic system to in some instance perform avoidance mitigation. For example, if the interferer is known to be a communication device in the network, the diagnostic system can direct a base station in communication with the communication device to direct the communication device to another channel so as to remove the interference experienced by a neighboring base station. Alternatively, the diagnostic system can direct an affected base station to utilize beam steering and or mechanical steering of antennas to avoid an interferer. When avoidance is performed, the mitigation step 514 can be skipped or may be invoked less as a result of the avoidance steps taken by the diagnostic system.
Once mitigation and/or an interference report has been processed in steps 514 and 516, respectively, the adaptive front end module 56 can proceed to step 518. In this step, the adaptive front end module 56 can repeat steps 502 thru 510 to calculate a new baseline average and corresponding threshold based on Q cycles of the resource blocks. Each cycle creates a new adaptive threshold that is used for interference detection. It should be noted that when Q is high, changes to the baseline average are smaller, and consequently the adaptive threshold varies less over Q cycles. In contrast, when Q is low, changes to the baseline average are higher, which results in a more rapidly changing adaptive threshold.
Generally speaking, one can expect that there will be more noise-free resource blocks than resource blocks with substantive noise. Accordingly, if an interferer is present (constant or ad hoc), one can expect the aforementioned algorithm described by method 500 will produce an adaptive threshold (i.e., baseline average+offset) that will be lower than interferer's power level due to mostly noise-free resource blocks driving down baseline average. Although certain communication devices will have a high initial power level when initiating communications with a base station, it can be further assumed that over time the power levels will be lowered to a nominal operating condition. A reasonably high Q would likely also dampen disparities between RB's based on the above described embodiments.
It is further noted that the aforementioned algorithms can be modified while maintaining an objective of mitigating detected interference. For instance, instead of calculating a baseline average from a combination of averages a1(f1) through ax(fx) or subsets thereof, the adaptive front end controller 56 can be configured to calculate a base line average for each resource block according to a known average of adjacent resource blocks, an average calculated for the resource block itself, or other information that may be provided by, for example, a resource block scheduler that may be helpful in calculating a desired baseline average for each resource block or groups of resource blocks. For instance, the resource block schedule can inform the adaptive front end module 56 as to which resource blocks are active and at what time periods. This information can be used by the adaptive front end module 56 determine individualized baseline averages for each of the resource blocks or groups thereof. Since baseline averages can be individualized, each resource block can also have its own threshold applied to the baseline average of the resource block. Accordingly, thresholds can vary between resource blocks for detecting interferers.
It is further noted that the aforementioned mitigation and detection algorithms can be implemented by any communication device including cellular phones, smartphones, tablets, small base stations, macro base stations, femto cells, WiFi access points, and so on. Small base stations (commonly referred to as small cells) can represent low-powered radio access nodes that can operate in licensed and/or unlicensed spectrum that have a range of 10 meters to 1 or 2 kilometers, compared to a macrocell (or macro base station) which might have a range of a few tens of kilometers. Small base stations can be used for mobile data offloading as a more efficient use of radio spectrum.
Referring back to step 702, the interference occurring in the resource block(s) can be detected by a mobile communication device utilizing the adaptive thresholds described in the subject disclosure. The mobile communication device can inform the first communication system (herein referred to as first base station) that it has detected such interference. The interference can also be detected by a base station that is in communication with the mobile communication device. The base station can collect interference information in a database for future reference. The base station can also transmit the interference information to a centralized system that monitors interference at multiple base stations. The interference can be stored and organized in a system-wide database (along with the individual databases of each base station) according to time stamps when the interference occurred, resource blocks affected by the interference, an identity of the base station collecting the interference information, an identity of the mobile communication device affected by the interference, frequency of occurrence of the interference, spectral information descriptive of the interference, an identity of the interferer if it can be synthesized from the spectral information, and so on.
At step 704, a determination can be made as to the traffic utilization of resource blocks affected by the interference and other resource blocks of the first base station that may be unaffected by interference or experiencing interference less impactful to communications. In this step a determination can be made as to the availability of unused bandwidth for redirecting data traffic of the mobile communication device affected by the interference to other resource blocks. Data traffic can represent voice only communications, data only communications, or a combination thereof. If other resource blocks are identified that can be used to redirect all or a portion of the data traffic with less interference or no interference at all, then a redirection of at least a portion of the data traffic is possible at step 706.
At step 708 a further determination can be made whether interference suppression by filtering techniques described in the subject disclosure can be used to avoid redirection and continued use of the resource blocks currently assigned to the mobile communication device. Quality of Service (QoS), data throughput, and other factors as defined by the service provider or as defined in a service agreement between a subscriber of the mobile communication device and the service provider can be used to determine whether noise suppression is feasible. If noise suppression is feasible, then one or more embodiments described in the subject disclosure can be used in step 710 to improve communications in the existing resource blocks without redirecting data traffic of the mobile communication device.
If, however, noise suppression is not feasible, then the mobile communication device can be instructed to redirect at least a portion of data traffic to the available resource blocks of the first base station identified in step 706. The first base station providing services to the mobile communication device can provide these instructions to the mobile communication device. However, prior to instructing the mobile communication device to redirect traffic, the base station can retrieve interference information from its database to assess the quality of the available resource blocks identified in step 706. If the available resource blocks have less interference or no interference at all, then the base station can proceed to step 712. If, however, there are no available resource blocks at step 706, or the available resource blocks are affected by equal or worse noise, then method 700 continues at step 714.
In one embodiment, steps 702, 704, 706, 708, 710, and 712 can be performed by a base station. Other embodiments are contemplated.
In step 714, a second communication system (referred to herein as second base station) in a vicinity of the mobile communication device can be detected. Step 714 can represent the base station that detected the interference in step 702 informing a central system overlooking a plurality of base stations that filtering or redirection of traffic of the affected mobile communication device is not possible. The detection of the second communication system can be made by the mobile communication device, or a determination can be made by the central system monitoring the location of the affected mobile communication device as well as other mobile communication devices according to coordinate information provided by a GPS receiver of the mobile communication devices, and knowledge of a communication range of other base stations. At step 716, resource blocks of the second base station can be determined to be available for redirecting at least a portion of the data traffic of the mobile communication device. At step 718, interference information can be retrieved from a system-wide database that stores interference information provided by base stations, or the interference information can be retrieved from or by the second base station from its own database. At step 720 a determination can be made from the interference information whether the resource blocks of the second base station are less affected by interference than the interference occurring in the resource blocks of the first base station. This step can be performed by a central system that tracks all base stations, or by the affected mobile communication device which can request the interference information from the central system, access the system-wide database, or access the database of the second base station.
If the interference information indicates the interference in the resource blocks of the second base station tend to be more affected by interference than the resource blocks of the first base station, then method 700 can proceed to step 714 and repeat the process of searching for an alternate base station in a vicinity of the mobile communication device, determining availability of resource blocks for transporting at least a portion of the data traffic of the mobile communication device, and determining whether noise in these resource blocks is acceptable for redirecting the traffic. It should be noted that the mobile communication device can perform noise suppression as described in step 710 on the resource blocks of the second base station. Accordingly, in step 720 a determination of whether the interference is acceptable in the resource blocks of the second base station can include noise suppression analysis based on the embodiments described in the subject disclosure. If an alternate base station is not found, the mobile communication device can revert to step 710 and perform noise suppression on the resource blocks of the first base station to reduce packet losses and/or other adverse effects, and if necessary increase error correction bits to further improve communications.
If, on the other hand, at step 722 the interference in the resource blocks of the second base station is acceptable, then the mobile communication device can proceed to step 724 where it initiates communication with the second base station and redirects at least a portion (all or some) of the data traffic to the resource blocks of the second base station at step 726. In the case of a partial redirection, the mobile communication device may be allocating a portion of the data traffic to some resource blocks of the first base station and the rest to the resource blocks of the second base station. The resource blocks of the first base station may or may not be affected by the interference detected in step 702. If the resource blocks of the first base station being used by the mobile communication device are affected by the interference, such a situation may be acceptable if throughput is nonetheless increased by allocating a portion of the data traffic to the resource blocks of the second base station.
It should be further noted that a determination in step 720 of an acceptable interference level can be the result of no interference occurring in the resource blocks of the second base station, or interference being present in the resource blocks of the second base station but having a less detrimental effect than the interference experienced in the resource blocks of the first base station. It should be also noted that the resource blocks of the second base station may experience interference that is noticeably periodic and not present in all time slots. Under such circumstances, the periodicity of the interference may be less harmful than the interference occurring in the resource blocks of the first base station if such interference is more frequent or constant in time. It is further noted, that a resource block scheduler of the second base station may assign the resource blocks to the mobile communication device according to a time slot scheme that avoids the periodicity of the known interference.
It is contemplated that the steps of method 700 can be rearranged and/or individually modified without departing from the scope of the claims of the subject disclosure. Consequently, the steps of method 700 can be performed by a mobile communication device, a base station, a central system, or any combination thereof.
Operating a wireless network can require a significant amount of effort to deploy and maintain it successfully. An additional complication involves the addition of new cell sites, sector splits, new frequency bands, technology evolving to new generations, user traffic patterns evolving and growing, and customer expectations for coverage and accessibility increasing. Such complexities in network design, optimization and adaptation are illustrated by way of example in
All of these challenges which can impact the operations of a network combine to make it harder for users to make calls, transfer data, and enjoy wireless applications. Wireless customers do not necessarily understand the complexity that makes a communication network work properly. They just expect it to always work. The service provider is left having to design the best network it can, dealing with all of the complexity described above. Tools have been developed to manage in part this complexity, but the wireless physical link requires special expertise. The underlying foundation of the performance of the wireless network is the physical link, the foundation that services rely upon. Typically, networks are designed to use the OSI seven layer model (shown in
The RF link is characterized at a cell site deployment stage when cell sites are selected and antenna heights and azimuths are determined. Dimensioning and propagation along with user traffic distribution are a starting point for the RF link. Once a cell site is built and configured, further optimization falls into two major categories: RF optimization/site modifications (e.g., involving adjusting azimuth or tilting antennas, adding low noise amplifiers or LNAs, etc.), and real-time link adaptation (the way an eNodeB and user equipment (UE) are constantly informing each other about link conditions and adjusting power levels, modulation schemes, etc).
The network design along with RF optimization/site modifications are only altered occasionally and most changes are expensive. Real-time link adaptation, on the other hand, has low ongoing costs and to the extent possible can be used to respond in real-time to changes experienced by an RF link (referred to herein as the “link condition”). The aspects of designing, optimizing and running a network are vital and a priority for network operators and wireless network equipment makers. Between network design and real-time adaptation a wide variety of manual and autonomous changes take place as part of network optimization and self-organizing networks.
In addition to the issues described above, there is an unsolved problem impacting the RF link that is not being addressed well with today's solutions, which in turn impacts network performance and the resulting customer experience. The subject disclosure addresses this problem by describing embodiments for improving the RF physical layer autonomously without relying on traditional cell site modifications. The subject disclosure also describes embodiments for monitoring link conditions more fully and over greater time windows than is currently performed. Currently, Service Overlay Networks (SON) focus only on downlink conditioning. The systems and methods of the subject disclosure can be adapted to both uplink and the downlink conditioning. Improvements made to an uplink by a base station, for example, can be shared with the SON network to perform downlink conditioning and thereby improve downlink performance. For example, if the performance of an uplink is improved, the SON can be notified of such improvements and can be provided uplink performance data. The SON network can use this information to, for example, direct the base station to increase coverage by adjusting a physical position of an antenna (e.g., adjust tilt of the antenna).
Additionally, the systems and methods of the subject disclosure can be adapted to demodulate a transmit link (downlink) to obtain parametric information relating to the downlink (e.g., a resource block or RB schedule, gain being used on the downlink, tilt position of the antenna, etc.). In an embodiment, the systems and methods of the subject disclosure can be adapted to obtain the downlink parametric information without demodulation (e.g., from a functional module of the base station). The systems and methods of the subject disclosure can in turn use the downlink parametric information to improve uplink conditioning. In an embodiment, systems and methods of the subject disclosure can use gain data associated with a downlink, a tilt position or adjustments of the downlink antenna, to improve uplink conditioning. In an embodiment, the systems and methods of the subject disclosure can be adapted to use the RB schedule to determine which RB's are to be observed/measured (e.g., RB's in use by UE's) and which RB's are to be ignored (e.g., RB's not in use by UE's) when performing uplink conditioning.
Additionally, in a closed-loop system, the embodiments of the subject disclosure can be adapted to balance performance between an uplink and downlink contemporaneously or sequentially. For example, when an antenna is physically adjusted (e.g., tilted) the embodiments of the subject disclosure can be adapted to determine how such an adjustment affects the uplink. If the adjustment is detrimental to the uplink, it can be reversed in whole or in part. If the adjustment has a nominal adverse impact on the uplink, the adjustment can be preserved or minimally adjusted. If the adjustment has an adverse impact on the uplink that is not detrimental but significant, changes to the uplink (e.g., increasing gain, filter scheme on uplink, requesting UEs to change MCS, etc.) can be identified and initiated to determine if the adjustment to the antenna can be preserved or should be reversed in whole or in part. In an embodiment, a combination of a partial reversal to the adjustment of the antenna and adjustments to the uplink can be initiated to balance a performance of both the uplink and downlink. Closed-loop concepts such as these can also be applied to the uplink. In an embodiment, for example, the downlink can be analyzed in response to changes to the uplink, and adjustments can be performed to the downlink and/or the uplink if the effects are undesirable.
In an embodiment, closed-loop system(s) and method(s) that perform link conditioning on both the uplink and downlink can be adapted to identify a balanced (“sweet spot”) performance between the uplink and the downlink such that neither the uplink nor the downlink is at optimal (or maximum) performance. In an embodiment, a closed-loop system and method can be performed by the SON network by receiving conditioning information relating to an uplink and/or a downlink from cell sites and by directing a number of such cell sites to perform corrective actions on the uplink, the downlink, or both to balance performance therebetween. In an embodiment, a closed-loop system and method for balancing performance between uplinks and downlinks can be performed by cell sites independently, UEs independently, cell sites cooperating with UEs, cell sites cooperating among each other, UEs cooperating among each other, or combinations thereof with or without assistance of a SON network by analyzing link conditioning performed on the uplinks and/or downlinks.
In one embodiment, the subject disclosure describes embodiments for improving network performance by analyzing information collected across several RF links to holistically improve communications between eNodeBs and UEs. In one embodiment, the subject disclosure describes embodiments for obtaining a suite of spectral KPIs (key performance indicators) which better capture the conditions of an RF environment. Such data can be used in self-optimizing networks to tune the RF link that supports the UE/eNodeB relationship. In addition, measurements and adjustments can be used to provide self-healing capabilities that enable the RF link of a UE/eNodeB RF to be adapted in real-time.
In one embodiment, signal to interference plus noise ratio (SINR) is an indicator that can be used to measure a quality of wireless communications between mobile and stationary communication devices such as base station(s). A base station as described in the subject disclosure can represent a communication device that provides wireless communication services to mobile communication devices. A base station can include without limitation a macro cellular base station, a small cell base station, a micro cell base station, a femtocell, a wireless access point (e.g., WiFi, Bluetooth), a Digital Enhanced Cordless Telecommunications (DECT) base station, and other stationary or non-portable communication services devices. The term “cell site” and base station may be used interchangeably. A mobile or portable communication device can represent any computing device utilizing a wireless transceiver for communicating with a base station such as a cellular telephone, a tablet, a laptop computer, a desktop computer, and so on.
For illustration purposes only, the embodiments that follow will be described in relation to cellular base stations and mobile cellular telephones. It is submitted, however, that the embodiments of the subject disclosure can be adapted for use by communication protocols and communication devices that differ from cellular protocols and cellular communication devices.
In communication systems such as LTE networks, achieving a target SINR may enable the coverage area of a cell site to achieve its design goals and allow the cell site to utilize higher modulation and coding schemes (MCS), which can result in higher spectral density—a desirable goal for LTE networks. Delivering desirable throughput rates in LTE systems can require higher SINR than in 3G systems. Performance of LTE systems can suffer as SINR falls, whether due to lower signal and/or higher interference and noise.
In one embodiment, SINR can be improved by collecting information from each cell site (e.g., on a sector and/or resource block basis), compiling an estimated SINR from such information, and adjusting RF parameters of the RF link to improve an overall network performance of the cell site. In one embodiment, SINR can be described according to the following equation:
where S is the received signal level, N is the thermal noise, and Nc is in-band co-channel interference, Nadj is the adjacent band noise in guard bands or the operator's other carriers, Ncomp is interference in the same overall frequency band from other operators, Nout is the out-of-band noise, and ΣI is the summation of the inter-cell interference contributed from surrounding cell sites. Some prior art systems consider in-band co-channel interference Nc, the adjacent interference noise Nadj, the competitors' transmissions Ncomp, and the out of band noise Nout to be very small. This assumption is generally not accurate, particularly for cell sites where performance is a challenge. In practice, I is proportional to the quality and strength of the signal (S) from neighboring sites; particularly, in dense networks or near cell edges, where one site's signal is another site's interference.
By describing SINR in its constituent parts as depicted in the above equation, specific actions can be taken to improve SINR and consequently performance of one or more RF links, which in turn improves performance of the network. An RF signal received by a cell site can be improved in a number of ways such as by selective filtering, adding gain or amplification, increasing attenuation, tilting antennas, and adjusting other RF parameters. RE parameters of an RF link can be modified in ways that improves overall network performance within a specific cell site and in some cases across multiple inter-related cell sites.
To achieve improvements in one or more cell sites, a matrix of SINRs can be created that includes an estimate for SINR at a path level for each node (cell site) or sector in the network. Optimization scenarios can be achieved by analyzing a network of cell sites collectively using linear programming for matrix optimization. By making adjustments to an uplink, one can create a weighted maximization of the SINR matrix with element δ added to each SINR element. Each point in the matrix with index i and j can consist of SINRi,j+δi,j for a particular node. In one embodiment, SINR can be optimized for each cell site, within an acceptable range of SINRi,j±δi,j, where δi,j is lower than some specified Δ. The term δi,j can represent a threshold range of performance acceptable to a service provider. A SINR outside of the threshold range can be identified or flagged as an undesirable SINR. The threshold range δi,j can be the same for all base stations, paths, sectors, or clusters thereof, or can be individualized per base station, path, sector, or clusters thereof. The term Δ can represent a maximum threshold range which the threshold range δi,j cannot exceed. This maximum threshold range Δ can be applied the same to all base stations, sectors, paths, or clusters thereof. Alternatively, the term Δ can differ per base station, sector, path, or cluster thereof. In one embodiment, the objective may not necessarily be to optimize SINR of a particular cell site. Rather the objective can be to optimize SINR of multiple nodes (cell sites and/or sectors) in a network. Below is an equation illustrating a matrix for optimizing SINR of one or more nodes (cell sites).
In one embodiment, for a particular cell site and sector i,j, SINR can be estimated on a resource-block level basis as SINRi,j,k (where i, and j are the site and sector indices that refer to the site location with respect to the surrounding sites and k is the index that refers to a particular resource block within the LTE system). The overall channel SINRi,j, can be calculated by averaging the SINRi,j,k over all the resource blocks, SINRi,j=Σk=1NSINRi,j,k, where N can be, for example, 50.
Improving SINR of one or more the nodes in a network using the above analysis, can in turn improve the throughput and capacity of the network, thereby enabling higher modulation and coding schemes (MCS) as shown in
In one embodiment, a closed loop process can be used for adjusting the condition of an RF link of a node (or cell site) to improve performance of one or more other nodes in a network. Such a process is depicted in
Together the steps of
Measurement.
Understanding the current conditions of an RF link is an important step to improving network performance. Today's networks make available a variety of KPIs that reflect network performance, many focused on specific layer(s) of the OSI model shown in
There can be several aspects of the RF spectrum that can impact an RF link, as shown in
Each of four different RF categories measured during link conditioning (enumerated as 1-4 in
Co-channel signals in an operating band can be filtered using the filtering techniques described earlier in the subject disclosure.
A network is a dynamic entity that changes continuously due to software upgrades, traffic volumes and pattern changes, seasonality and environmental conditions, just to name a few. Monitoring these variations and then adjusting the RF link to accurately compensate for such variations enables cell sites to consistently operate with a desired performance. In addition to monitoring and adjusting variations in an RF link, in one embodiment, nominal spectral values and RF statistics can be recorded in an ongoing basis (daily, hourly, according to moving averages, etc.).
Occasionally there can be significant differences between real-time, short-term averages and longer-term design parameters that can cause degradation of cell site metrics, which may negatively impact customer experience, and which can result in lost revenue for a service provider if not counteracted. When such issues are identified a next step can be to understand why the issues arose by analyzing spectral insights gained through the analysis of signals impacting SINR.
In one embodiment, link conditioning can be performed based on a number of metrics that can include without limitation:
To gain a better understanding of the above metrics, the reference numbers 1-4 used in the above listing can be cross-referenced with the reference numbers 1-4 in
Analysis.
As variations of RSSI and SINR data are collected RF statistics relating to these metrics can be generated and used to mine a data set for trends, outliers, and abnormalities across cell sites and frequency bands. By analyzing such information, a network and its corresponding cell sites can be monitored for changes over time, and corresponding mitigation steps can be taken when necessary.
Recall equation EQ1 above,
where S is the received signal level, N is the thermal noise, and Nc is in-band co-channel interference, Nadj is the adjacent band noise, Ncomp is interference from other operators, Nout is the out-of-band noise, and ΣI is the summation of the inter-cell interference contributed from all the surrounding cells. If SINR of a given sector or node is lower than expected a number of causes and solutions can be applied, based on a deeper understanding of the RF environment and its contribution to SINR. Below are non-limiting illustrations and corresponding recommended solutions to improve SINR.
The above listing provides illustrations for initiating mitigating actions based on spectral analysis, which can be implemented with closed loop control so that ongoing performance improvements can be maintained.
Mitigation (Do).
RF link mitigation can be initiated from an analysis of spectral data that leads to a set of specific recommended actions. There are many aspects of the RF link that can be modified as part of a link mitigation strategy, including without limitation:
Check and Reporting.
As changes are made to the network parameters based on any of the mitigation actions described above, the changes can be verified to determine whether such mitigation actions in fact improved network performance. Additionally, the SON network can be informed of these changes on the uplink for possible use in downlink conditioning as previously described. In addition, relevant data can be logged to guide future enhancement cycles.
As noted earlier, verification of the changes to the RF link can be implemented by way of a closed loop confirmation process which can provide input to the SON network to ensure that the network as a whole is operating according to up-to-date settings and the same or similar RF data. Reports generated in the verification step may include information relating to external interference that was detected, resource block utilization, multiple channel power measurements, etc.
As part of the ongoing adaptation of the link conditioning cycle, all changes can be logged, statistics can be updated and metadata can be generated and/or assigned to logged changes to ensure all changes can be understood and analyzed by personnel of a service provider. Such reports can also be used by future applications which can be adapted to “learn” from historical data generated from many cycles of the process described above. Implementing a link conditioning process based on real-world conditions as described above provides an enhanced and optimized RF physical layer performance. Ongoing link conditioning also enables operators to rely less on designing cell sites to worst-case conditions or anticipated network coverage.
The embodiments of the subject disclosure provide a unique focus on the RF physical layer according to a collective analysis of RF links across multiple cell sites. These embodiments enable systems to extract insight from spectral information, historical trends and network loading, while simultaneously optimizing RF parameters of multiple sites with live network traffic, thereby improving communications between eNodeBs and the UEs.
For illustration purposes only, method 800 will now be described according to the centralized system 832 of
If, however, the minimum SINR measurement of a particular cell site, sector or path is below the threshold, then corrective action can be taken by the centralized system 832 at step 808 to improve the SINR measurement of the cell site, sector or path in question. The corrective action can include, without limitation, filtering adjacent signals, adding gain, attenuating high signal power, filtering interference signals according to the embodiments of the subject disclosure, utilizing diversity optimization, utilizing 3G service optimization, adjusting mobile transmit parameters, tilting antennas to reshape cell site coverage, or any combination thereof.
Once corrective action has been executed by a cell site and/or UE, a determination can be made by the centralized system 832 at step 810 as to whether the SINR of the cell site, sector or path in question has improved. If there's no improvement, the corrective action can be reversed in whole or in part by the centralized system 832 at step 812, and measurements of SINR per cell site, sector and/or path can be repeated beginning from step 802. If, however, the corrective action did improve the SINR of the cell site, sector or path in question, then a determination can be made by the centralized system 832 at step 814 as to whether the corrective action implemented by the cell site and/or UE has had an adverse effect on other paths or sectors of the same cell site or neighboring cell sites.
In one embodiment, this determination can be made by the centralized system 832 by requesting SINR measurements from all cell sites, sectors, and/or paths after the corrective action has been completed. The centralized system 832 can then be configured to determine an average of the SINR's for all the cell sites, sectors, and/or paths for which the corrective action of step 808 was not applied. For ease of description, the cell site that initiated corrective action will be referred to as the “corrected” cell site, while cell sites not participating in the corrective action will be referred to as the “uncorrected” cell sites.
With this in mind, at step 816, the centralized system 832 can determine whether the SINR averages from the uncorrected cell sites, sectors or paths are the same or similar to the SINR averages of the uncorrected cell sites, sectors, and/or paths prior to the corrective action. If there's no adverse effect or a nominal adverse effect, then the centralized system 832 can be configured to maintain the corrective action initiated by the corrected cell site, sector and/or path and proceed to step 802 to repeat the process previously described. If, on the other hand, the average of the SINR's of the uncorrected cell sites, sectors or paths for which corrective action was not taken has been reduced below the SINR averages of these sites, sectors or paths prior to the corrective action (or below the threshold at step 806 or different threshold(s) established by the service provider), then the corrective action initiated by the corrective cell site, sector or path can be reversed in whole or in part by the centralized system 832 at step 812.
In another embodiment, step 816 can be implemented by establishing a minimum SINR values that are unique to each cell site, sector, and/or path. If after the corrective action the SINR measurements of the corrected cell site, sector and/or path has improved at step 810 and the SINR measurements of the uncorrected cell sites, sectors and/or paths are above the unique SINR values established therefor, then the corrective action can be maintained by the centralized system 832 and the process can be reinitiated at step 802. If, on the other hand, the SINR measurement of the corrected cell site, sector or path has not improved after the corrective action, or the SINR measurements of one or more uncorrected cell sites, sectors, and/or paths are below the unique SINR values established therefor, then the corrective action taken can be reversed in whole or in part by the centralized system 832 at step 812.
Method 800 can be adapted to use different sampling rates for SINR, and/or different thresholds. The sampling rates and/or thresholds can be temporally dependent (e.g., time of day profiles—morning, afternoon, evening, late evening, early morning, etc.). SINR profiles can be used to account for anomalous events (e.g., a sporting event, a convention, etc.) which may impact traffic conditions outside the norm of regular traffic periods. Thresholds used by method 800 can include without limitation: minimum thresholds used for analyzing SINRs of cell sites, sectors and/or paths prior to corrective action; corrective thresholds used for analyzing SINRs of corrected cell sites, sectors and/or paths, consistency thresholds used for analyzing SINRs from uncorrected cell sites, sectors and/or paths after corrective action, and so on. Method 800 can also be adapted to use other KPIs such as dropped calls, data throughput, data rate, accessibility and retain-ability, RSSI, density of user equipment (UEs), etc. Method 800 can also be adapted to ignore or exclude control channels when determining SINR measurements. That power levels from control channels can be excluded from SINR measurements. Method 800 can also be adapted to perform closed-loop methods for balancing uplink and downlink performance as described earlier for SON networks, cell sites, UEs, or combinations thereof. Method 800 can be adapted to obtain the noise components of SINR (EQ1) from power measurements described in the subject disclosure. Referring to
As noted earlier, method 800 can also be adapted to the architectures of
In the case of
It is further noted that method 800 can be adapted to combine one or more of the foregoing embodiments for performing link conditioning in any one of the embodiments
In one or more embodiments, in step 910, the system and methods of the subject disclosure can be adapted to measure signal energy levels during the unused resource blocks. In one embodiment, the resource blocks that are scheduled for use in carrying data information will bear RF signals during particular portions of frequency/time identified by the resource block schedule. By comparison, when the resource blocks are not scheduled to carry data information, the resource blocks should not bear RF signals during particular portions of frequency/time identified by the resource block schedule.
Put another way, no active transmission power should be allocated to the time-frequency signal space by a transmitting LTE UE during the unused resource blocks, while active transmission power is expected to be allocated to the time-frequency signal space by a transmitting LTE UE during “used” resource blocks. Therefore, during the unused resource blocks, a wireless signal path should exhibit a lower energy level than during the “in use” resource blocks. In one embodiment, signal energy levels in unused resource blocks can be measured for one or more wireless signal paths, one or more sectors, or one or more sectors. Generally, the energy measured in the unused resource blocks should be thermal noise only. However, if inter-cell (i.e., adjacent cell site) interference is present, the energy measured in one or more unused resource blocks may be above an expected thermal noise level.
In one or more embodiments, in step 912 the system and methods of the subject disclosure can be adapted to determine an average thermal noise level from the measured signal energy levels of the wireless signal path(s) during unused resource blocks. In one embodiment, the average system noise can be determined per resource block of a particular path. In one embodiment, the average system noise can be determined across all unused resource blocks of the particular path. In one embodiment, the average system noise can be determined across a subset of unused resource blocks of the particular path. In one embodiment, the average system noise can be determined for unused resource blocks across all paths of a particular sector. In one embodiment, the average system noise can be determined for unused resource blocks across multiple sectors. In one embodiment, the average system noise can be determined for unused resource blocks across multiple cell sites. Based on the foregoing illustrations, any combination of averaging of energy measurements across one or more unused resource blocks is possible for determining an average thermal noise at step 912.
A sample of unused resource blocks can be selected so that measurements and thermal noise averaging is distributed over a time period or can be selected based on another organized factor. For example, the sample of unused resource blocks could be based on the relative traffic loading, where greater or lesser numbers of unused resource blocks could be selected for measurement and thermal noise averaging can be based on the data traffic load. In another example, the sample of unused resource blocks could be selected for measurement and thermal noise averaging based on changes in noise and/or error rate conditions for the wireless signal paths. Noise and/or error rate conditions can be monitored and classified as improving, deteriorating, and/or steady state. Under improving or steady state noise/error rate trends, a reduced set of unused resource blocks can be selected for measurement of signal energy levels and thermal noise averaging determined therefrom, while a larger set of unused resource blocks can be selected under deteriorating noise/error rate conditions. The sample of unused resource blocks selected can also depend on known or scheduled events, time of day, day of the week, or combinations thereof. For example, traffic conditions may vary during time of day. Thus traffic conditions can be profiled daily and by geography (e.g., heavy traffic from noon to late afternoon, lighter traffic at other times). Events such as sporting events or conventions can also change traffic conditions.
Accordingly, the system and methods of the subject disclosure can measure signals levels and determine thermal noise averages based on a sample of unused resource blocks selected according to any number of techniques including without limitation distributing measured energy levels of unused resource blocks over a time period, accounting for traffic conditions at different times of the day, scheduled events that can change traffic conditions, and/or responding to trends in noise/error rate levels. In another embodiment, the measured energy levels can be weighted to emphasize or deemphasize certain measurements based on criteria such as location of wireless signal paths, relative importance of wireless signal paths, and/or how recently the measurement was collected. The average thermal noise level can be determine from the weighted sample of measured energy levels. In one embodiment, the average thermal noise level can be adjusted. Additionally, certain measured energy levels can be excluded from a thermal noise averaging calculation (e.g., excluding unexpectedly high measured energy levels in certain unused resource blocks, excluding measured energy levels that exceed a threshold, and so on).
In one or more embodiments, in step 916 the system and methods of the subject disclosure can be adapted to determine an adaptive inter-cell interference threshold for one or more wireless signal paths, one or more sectors, and/or one or more cell cites based on the average thermal noise level determined at step 912. In one embodiment, the adaptive inter-cell interference threshold can be based on the average thermal noise level determined at step 912 with no additional factors. In another embodiment, the adaptive inter-cell interference threshold can be determined from a sum of a threshold supplied by a service provider and the average noise level determined at step 912.
In one or more embodiments, in step 920 the system and methods of the subject disclosure can be adapted to scan signals in one or more wireless paths. The scanned signals can represent signals measured in one or more resource blocks of one or more wireless paths. If a resource block schedule is available to identify which resource blocks are in use as in the present case, then the scanned signals can represent signals measured in one or more “used” resource blocks. Alternatively, in another embodiment, whether or not a resource block schedule is available, the scanned signals can represent signals measured in one or more resource blocks that may include used and unused resource blocks. In one or more embodiments, the scanned signals can be compared to the adaptive inter-cell interference threshold at step 924 to detect interference signals that may adversely affect communications between UEs and base station(s). If the scanned signals exceed the adaptive inter-cell interference threshold in step 924, then the interference signal energies and frequencies are stored, in step 928.
In one or more embodiments, in step 932 the system and methods of the subject disclosure can be adapted to measure signal energy levels and noise energy levels of wireless signal paths. Again, the wireless signal paths can be selected for measurement of signal levels and noise levels based on one or more criteria, such as changes in noise or error rate trends, criticality of one or more wireless signal paths, loading of one or more wireless signal paths, availability of system resources, among other possible factors. The measured noise levels can include, for example, the noise factors previously described for EQ1, e.g., in-band co-channel interference (Nc), adjacent band noise in guard bands or the operator's other carriers (Nadj), Ncomp interference in the same overall frequency band from other operators, and Nout out-of-band noise. Thermal noise (N) in the present case can be based on the average thermal noise determined at step 912.
In one or more embodiments, in step 936 the system and methods of the subject disclosure can be adapted to determine signal-to-interference plus noise ratios (SINR) for the wireless signal paths based on measured signal levels, noise levels, and interference levels. The SINR can be determined by processing, in a SINR model, the measured signal and noise energy levels from step 932 and the stored above-threshold interference signal energy levels from step 928. The SINR model can be the equation for SINR calculation described above (EQ1). In one embodiment, SINR values can be determined for all wireless signal paths or for selected wireless signal paths. The wireless signal paths can be selected based on one or more criteria, such as changes in noise or error rate trends, criticality of one or more wireless signal paths, loading of one or more wireless signal paths, and/or availability of system resources. In one embodiment, SINR values can be generated and reported on a periodic basis. In one embodiment, SINR values can be generated and reported responsive to a system encountering communication issues between UEs and one or more cell sites.
In one or more embodiments, in step 940 the system and methods of the subject disclosure can be adapted to compare SINR values to one or more SINR thresholds to determine if any of the wireless signal paths is exhibiting an SINR value that is below the SINR threshold. If the a wireless signal path is operating with a below-threshold SINR, then, in step 944 the system and methods of the subject disclosure can be adapted to initiate a corrective action to improve the SINR for the wireless signal paths, as described above in relation to
In one or more embodiments, in step 958 the system and methods of the subject disclosure can be adapted to adjust the estimated thermal noise energy according to a thermal noise threshold adjustment to create an adjusted estimated thermal noise energy. In one embodiment, the thermal noise threshold adjustment can be provided by a service provider. In one embodiment, the thermal noise threshold adjustment can be specific can different between wireless signal paths, sectors or cell sites. In one embodiment, all wireless signal paths of the system can be associated with the same thermal noise threshold adjustment. In one embodiment, the thermal noise threshold adjustment can be determined by the system or device by accessing a configuration that is specific to a wireless signal path. In one embodiment, a default thermal noise threshold adjustment can be used. The default thermal noise threshold adjustment can be modified to account for one or more criteria, such as the location of the wireless signal path or current noise/error trend information.
In one or more embodiments, in step 956 the system and methods of the subject disclosure can be adapted to determine an average thermal noise level for the wireless signal paths, sectors, or cell sites based on averages of the adjusted estimated thermal noise. In one embodiment, the average thermal noise level for the wireless signal paths, sectors or cell sites can be based on averaging of the unadjusted estimated thermal noise of step 954. Steps 916-944 can be performed as described above in method 900.
It is further noted that the methods and systems of the subject disclosure can be used in whole or in part by a cellular base station (e.g., macro cell site, micro cell site, pico cell site, a femto cell site), a wireless access point (e.g., a WiFi device), a mobile communication device (e.g., a cellular phone, a laptop, a tablet, etc.), a commercial or utility communication device such as a machine-to-machine communications device (e.g., a vending machine with a communication device integrated therein, an automobile with an integrated communication device), a meter for measuring power consumption having an integrated communication device, and so on. Additionally, such devices can be adapted according to the embodiments of the subject disclosure to communicate with each other and share parametric data with each other to perform in whole or in part any of embodiments of the subject disclosure.
It is further noted that the methods and systems of the subject disclosure can be adapted to receive, process, and/or deliver information between devices wirelessly or by a tethered interface. For example, SINR information can be provided by the cell sites to a system by way of a tethered interface such as an optical communication link conforming to a standard such as a common public radio interface (CPRI) referred to herein as a CPRI link. In another embodiment, a CPRI link can be used to receive digital signals from an antenna system of the base station for processing according to the embodiments of the subject disclosure. The processed digital signals can in turn be delivered to other devices of the subject disclosure over a CPRI link. Similar adaptations can be used by any of the embodiments of the subject disclosure.
Although reference has been made to resource blocks in the methods and systems of the subject disclosure, the methods and systems of the subject disclosure can be adapted for use with any spectral segment of any size in the frequency domain, and any frequency of occurrence of the spectral segment in the time domain. Additionally, the methods and systems of the subject disclosure can be adapted for use with adjacent spectral segments in the frequency domain, spectral segments separated from each other in the frequency domain, and/or spectral segments of different wireless signal paths, sectors, or cell sites. It is further noted that the methods and systems of the subject disclosure can be performed by a cell site operating independently of the performance of other cell sites, by a cell site operating in cooperation with other adjacent cell sites, and/or by a central system controlling operations of multiple cell sites.
LTE downlink signals are modulated using orthogonal-frequency domain multiplexing, or OFDM. The LTE uplink, on the other hand, uses single-carrier frequency domain multiple access, or SC-FDMA. SC-FDMA was selected for the LTE uplink because it has a much lower peak-to-average than OFDM, and therefore, it helps reduce the power consumption of mobile phones. However, the use of SC-FDMA also introduces problems. SC-FDMA is very susceptible to interference. Even a small amount of interference can degrade the performance of an entire LTE cell.
Another form of interference that the embodiments of the subject disclosure can be adapted to mitigate is Passive Intermodulation Interference (PIM). PIM, is quickly becoming a common form of interference affecting 4G LTE networks. PIM occurs when RF energy from two or more transmitters is non-linearly mixed in a passive circuit. PIM becomes interference to a cellular base-station when one of the intermodulation products is received by a base-station in one or more of its receive channels. PIM can occur, for example, in a 700 MHz LTE network. For example, a service operator can operate 10 MHz LTE networks in band 17 and band 29. When base-stations operating in these bands are co-located, there is a high risk of degrading the performance of the band 19 receiver due to PIM. The non-linear mixing of the band 17 and band 29 downlinks produce a 3rd order intermodulation (IM) energy that occurs in the band 17 uplink channel. This situation is illustrated in
The most common sources of PIM are poor RF connections, damaged cables, or poor antennas. However, PIM can also be caused by metallic objects in front of the antennas. This situation is often observed when the base-station is installed in a roof-top, and the antennas are near rusted ducks, vents, and other metallic structures capable of mixing and reflecting the RF energy.
The conventional approach to removing PIM interference requires knowledge of the transmit signals. These algorithms work by injecting a digitized version of the transmitting signals into a mathematical model. The mathematical model is capable of generating an intermodulation signal similar to what is generated by an actual PIM source. The intermodulation signal produced by the mathematical model is then correlated against the receive signal. If a match is found, the algorithm attempts to remove as much intermodulation energy as possible. However, the conventional PIM cancellation approach cannot be used when there is no knowledge of the transmitting signals. Therefore, a new approach to PIM mitigation that operates without knowledge of the transmitting signals would be desirable.
The subject disclosure describes non-limiting embodiments for reducing the impact of PIM on an uplink receiver, without knowledge of the downlink signals producing the PIM interference. It will be appreciated that the embodiments of the subject disclosure previously described can be adapted to mitigate PIM as will be described below. Accordingly, such adaptations are contemplated by the subject disclosure.
The down-converted signal can then be supplied to an analog and digital conversion module 966 that utilizes analog-to-digital conversion to digitize the down-converted signal and thereafter utilizes digital-to-analog conversion to convert a processed version of the down-converted signal back to an analog signal. The digitized down-converted signal can be supplied to an FPGA module 967 that extracts I and Q signals from the digitized down-converted signal, processes these signals via a signal conditioning module 962 (labeled “C”), and supplies the processed I and Q signals to a combination of the analog and digital conversion module 966, the frequency shifting module 965, the LNA module 964, and the dual duplexer module 963, which together generate an RF signal that has been processed to remove interference (including PIM interference) impacting SC-FDMA systems. The processed RF signal is then supplied to a base station processor for further processing.
It will be appreciated that system 960 can be an integral component of the base station processor (e.g., eNode B). In other embodiments, the processing resources of the base station processor can be configured to perform the functions of system 965.
As noted above, the module 962 can be configured to perform signal conditioning to reduce interference (including PIM interference) impacting SC-FDMA systems. Module 962 can be configured to distinguish SC-FDMA signals from interfering signals such as PIM. SC-FDMA signals have many unique characteristics in both the time and frequency domains. Certain frequency and time domain characteristics are dictated by the LTE standard and can be used to identify a unique signal profile for legitimate LTE signals. Interfering signals, on the other hand, typically have a very different set of time and frequency characteristics or signal profile.
As an example, consider the case of PIM interference generated in a roof-top by two LTE base-stations. The LTE downlink signals from the base-stations mix to produce an intermodulation product that interferes with the uplink channel used by one of the base-stations to receive signals from mobile or stationary communication devices. In this illustration, the interfering PIM signal is generated by a non-linear mixing of two LTE downlink signals, modulated using 01-DM. The profile of the PIM signal is unique, and quite different from the profile of a desired SC-FDMA uplink channel signal unaffected by PIM interference. One of the signal profile differences is the peak-to-average power ratio (PAPR). The PAPR of LTE uplink signals range from 7 to 8.5 dB. The PAPR of a 3rd intermodulation product created by the mixing of two LTE downlink signals is in the range of 18 to 30 dB.
When PIM interference is present, the signal received by the base-station is the sum of the SC-FDMA signals transmitted by the UEs (desired signal), which has a PAPR under 10 dB, and the PIM signal (undesired signal), which has a PAPR of 18 to 30 dB. Since PIM energy has a much higher PAPR, most of the energy in the peaks of the received signal come from PIM and not from the UEs. I certain embodiments, clipping techniques can be used to reduce the amount of PIM energy in the receive signal.
In an embodiment, for example, the impact of PIM interference can be reduced by reducing the peak-to-average ratio of a received signal affected by PIM to expected levels. This can be accomplished with a circular clipper that limits or clips the peaks of an SC-FDMA uplink channel signal affected by PIM. Since the PIM signal has a much higher PAPR than the PAPR of an unaffected uplink channel signal, most of the energy in the peaks of the received signal come from PIM and not from the UEs. Therefore, clipping techniques can be used to reduce the amount of PIM energy in the receive signal. For example, if the PAPR of the received signal is 25 dBs, a clipper can be configured to clip the peak of the signals that exceed a PAPR of 10 dB. The result is a signal with a PAPR of around 10 dB, and with an improved signal-to-interference ratio.
The metrics computed by the time and frequency profile calculators 972, 973 can be compared by a comparator module 974 against a set of time domain and/or frequency domain thresholds. The time-domain and/or frequency-domain thresholds can be pre-determined based on measurements of desired LTE signals without interference. If the time-domain threshold and/or frequency-domain threshold is exceeded, signal conditioning techniques can be applied to the received signal based on parameters generated by a conditioning parameters module 975.
The signal conditioning technique used by a signal conditioner 976 depends on which time domain or frequency domain metric exceeds its corresponding threshold, and the parameters supplied by the conditioning parameters module 975. For example, where PIM is interfering with a SC-FDMA signal, the measured peak-to-average ratio will exceed the threshold for SC-FDMA. In this case, the signal conditioner 976 can be configured with a clipper that reduces the peaks of the signal based on time domain and/or frequency domain parameters supplied to the signal conditioner 976 to enable the clipper function to restore the peak-to-average ratio to normal levels for SC-FDMA signals, thereby reducing the power of the PIM interferer. The collective processing delay of the time-domain and frequency-domain profile calculators 972, 973, threshold comparisons by the comparator module 974, and generation of conditioning parameters by the conditioning parameters module 975 is approximately the same delay added to the signal by the delay buffer 971.
In certain embodiments, the foregoing embodiments can be described by the flow diagram of
In certain embodiments, the frequency domain and/or time domain profile thresholds can be pre-determine based on known signal characteristics of desired uplink signals without PIM interference. In other embodiments, the frequency domain and/or time domain profile thresholds can be determined dynamically based on historical measurements taken from the received signal comprising the sum of the desired signal(s) and interfering signal(s) (e.g., utilizing running averages, and/or other statistical techniques). Signal conditioning can be applied when time domain or frequency domain profile metrics that exceed a corresponding time domain or frequency domain threshold.
Signal conditioning techniques can include, but are not limited to:
limiting or clipping of the signal amplitude, magnitude, or power; and/or
frequency domain filtering (low pass, band pass, or notch)
Time-domain profile metrics can include, but are not limited to:
Frequency-domain profile metrics can include, but are not limited to:
occupied bandwidth;
shape of the spectrum density; and/or
resource block utilization
It will be appreciated that the foregoing embodiments can be applied to any type of RF signal affected by interference, as long as the profile of the desired signals and the interference are significantly different. Accordingly, the embodiments of the subject disclosure for reducing interference is not limited to LTE signals and/or PIM interference. Accordingly, a system can be adapted to filter interference affecting any type RF signal that can be profiled in the time domain and/or frequency domain for its desired characteristics relative to interference. It is further noted that module 962 is not limited to a clipping technique. Frequency and/or time domain filtering techniques used in place of, and/or combined with, clipping or signal limiting methods can also be applied to the subject disclosure. For example, spatial filtering and/or polarization selection techniques or combinations thereof can be used in place of clipping or signal limiting methods.
Additionally, RF signals being filtered for interference can be obtained over a CPRI interface, an analog cable coupled to the antenna receiving the RF signals, or a combination thereof. In yet other embodiments, the RF signals being filtered for interference can be received from MIMO antennas. In other embodiments, the PIM interference algorithm described above can be adapted to make use of a known transmitter of one or more base stations being a source of PIM interference. For example, by knowing that certain transmitters of one or more base stations is a source for PIM interference, the PIM interference algorithm can be adapted to more precisely pinpoint PIM interference at expected frequency ranges, power levels, and so on, which can speed up the algorithm and thereby reduce chances of a false positive or a false negative. It will be further appreciated that any of the embodiments of the subject disclosure can be combined in whole or in part and/or adapted in whole or in part to the foregoing embodiments associated with the descriptions of
An illustrative embodiment of a communication device 1000 is shown in
To enable these features, communication device 1000 can comprise a wireline and/or wireless transceiver 1002 (herein transceiver 1002), a user interface (UI) 1004, a power supply 1014, a location receiver 1016, a motion sensor 1018, an orientation sensor 1020, and a controller 1006 for managing operations thereof. The transceiver 1002 can support short-range or long-range wireless access technologies such as Bluetooth, ZigBee, WiFi, DECT, or cellular communication technologies, just to mention a few. Cellular technologies can include, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generation wireless communication technologies as they arise. The transceiver 1002 can also be adapted to support circuit-switched wireline access technologies (such as PSTN), packet-switched wireline access technologies (such as TCP/IP, VoIP, etc.), and combinations thereof.
The UI 1004 can include a depressible or touch-sensitive keypad 1008 with a navigation mechanism such as a roller ball, a joystick, a mouse, or a navigation disk for manipulating operations of the communication device 1000. The keypad 1008 can be an integral part of a housing assembly of the communication device 1000 or an independent device operably coupled thereto by a tethered wireline interface (such as a USB cable) or a wireless interface supporting for example Bluetooth. The keypad 1008 can represent a numeric keypad commonly used by phones, and/or a QWERTY keypad with alphanumeric keys. The UI 1004 can further include a display 1010 such as monochrome or color LCD (Liquid Crystal Display), OLED (Organic Light Emitting Diode) or other suitable display technology for conveying images to an end user of the communication device 1000. In an embodiment where the display 1010 is touch-sensitive, a portion or all of the keypad 1008 can be presented by way of the display 1010 with navigation features.
The display 1010 can use touch screen technology to also serve as a user interface for detecting user input. As a touch screen display, the communication device 1000 can be adapted to present a user interface with graphical user interface (GUI) elements that can be selected by a user with a touch of a finger. The touch screen display 1010 can be equipped with capacitive, resistive or other forms of sensing technology to detect how much surface area of a user's finger has been placed on a portion of the touch screen display. This sensing information can be used to control the manipulation of the GUI elements or other functions of the user interface. The display 1010 can be an integral part of the housing assembly of the communication device 1000 or an independent device communicatively coupled thereto by a tethered wireline interface (such as a cable) or a wireless interface.
The UI 1004 can also include an audio system 1012 that utilizes audio technology for conveying low volume audio (such as audio heard in proximity of a human ear) and high volume audio (such as speakerphone for hands free operation). The audio system 1012 can further include a microphone for receiving audible signals of an end user. The audio system 1012 can also be used for voice recognition applications. The UI 1004 can further include an image sensor 1013 such as a charged coupled device (CCD) camera for capturing still or moving images.
The power supply 1014 can utilize common power management technologies such as replaceable and rechargeable batteries, supply regulation technologies, and/or charging system technologies for supplying energy to the components of the communication device 1000 to facilitate long-range or short-range portable applications. Alternatively, or in combination, the charging system can utilize external power sources such as DC power supplied over a physical interface such as a USB port or other suitable tethering technologies.
The location receiver 1016 can utilize location technology such as a global positioning system (GPS) receiver capable of assisted GPS for identifying a location of the communication device 1000 based on signals generated by a constellation of GPS satellites, which can be used for facilitating location services such as navigation. The motion sensor 1018 can utilize motion sensing technology such as an accelerometer, a gyroscope, or other suitable motion sensing technology to detect motion of the communication device 1000 in three-dimensional space. The orientation sensor 1020 can utilize orientation sensing technology such as a magnetometer to detect the orientation of the communication device 1000 (north, south, west, and east, as well as combined orientations in degrees, minutes, or other suitable orientation metrics).
The communication device 1000 can use the transceiver 1002 to also determine a proximity to a cellular, WiFi, Bluetooth, or other wireless access points by sensing techniques such as utilizing a received signal strength indicator (RSSI) and/or signal time of arrival (TOA) or time of flight (TOF) measurements. The controller 1006 can utilize computing technologies such as a microprocessor, a digital signal processor (DSP), programmable gate arrays, application specific integrated circuits, and/or a video processor with associated storage memory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologies for executing computer instructions, controlling, and processing data supplied by the aforementioned components of the communication device 400.
Other components not shown in
The communication device 1000 as described herein can operate with more or less of the circuit components shown in
It should be understood that devices described in the exemplary embodiments can be in communication with each other via various wireless and/or wired methodologies. The methodologies can be links that are described as coupled, connected and so forth, which can include unidirectional and/or bidirectional communication over wireless paths and/or wired paths that utilize one or more of various protocols or methodologies, where the coupling and/or connection can be direct (e.g., no intervening processing device) and/or indirect (e.g., an intermediary processing device such as a router).
The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet PC, a smart phone, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. It will be understood that a communication device of the subject disclosure includes broadly any electronic device that provides voice, video or data communication. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.
The computer system 1100 may include a processor (or controller) 1102 (e.g., a central processing unit (CPU), a graphics processing unit (GPU, or both), a main memory 1104 and a static memory 1106, which communicate with each other via a bus 1108. The computer system 1100 may further include a display unit 1110 (e.g., a liquid crystal display (LCD), a flat panel, or a solid state display. The computer system 1100 may include an input device 1112 (e.g., a keyboard), a cursor control device 1114 (e.g., a mouse), a disk drive unit 1116, a signal generation device 1118 (e.g., a speaker or remote control) and a network interface device 1120. In distributed environments, the embodiments described in the subject disclosure can be adapted to utilize multiple display units 1110 controlled by two or more computer systems 1100. In this configuration, presentations described by the subject disclosure may in part be shown in a first of the display units 1110, while the remaining portion is presented in a second of the display units 1110.
The disk drive unit 1116 may include a tangible computer-readable storage medium 1122 on which is stored one or more sets of instructions (e.g., software 1124) embodying any one or more of the methods or functions described herein, including those methods illustrated above. The instructions 1124 may also reside, completely or at least partially, within the main memory 1104, the static memory 1106, and/or within the processor 1102 during execution thereof by the computer system 1100. The main memory 1104 and the processor 1102 also may constitute tangible computer-readable storage media.
Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays and other hardware devices that can likewise be constructed to implement the methods described herein. Application specific integrated circuits and programmable logic array can use downloadable instructions for executing state machines and/or circuit configurations to implement embodiments of the subject disclosure. Applications that may include the apparatus and systems of various embodiments broadly include a variety of electronic and computer systems. Some embodiments implement functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of an application-specific integrated circuit. Thus, the example system is applicable to software, firmware, and hardware implementations.
In accordance with various embodiments of the subject disclosure, the operations or methods described herein are intended for operation as software programs or instructions running on or executed by a computer processor or other computing device, and which may include other forms of instructions manifested as a state machine implemented with logic components in an application specific integrated circuit or field programmable gate array. Furthermore, software implementations (e.g., software programs, instructions, etc.) including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein. It is further noted that a computing device such as a processor, a controller, a state machine or other suitable device for executing instructions to perform operations or methods may perform such operations directly or indirectly by way of one or more intermediate devices directed by the computing device.
While the tangible computer-readable storage medium 1122 is shown in an example embodiment to be a single medium, the term “tangible computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “tangible computer-readable storage medium” shall also be taken to include any non-transitory medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methods of the subject disclosure.
The term “tangible computer-readable storage medium” shall accordingly be taken to include, but not be limited to: solid-state memories such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories, a magneto-optical or optical medium such as a disk or tape, or other tangible media which can be used to store information. Accordingly, the disclosure is considered to include any one or more of a tangible computer-readable storage medium, as listed herein and including art-recognized equivalents and successor media, in which the software implementations herein are stored.
Although the present specification describes components and functions implemented in the embodiments with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. Each of the standards for Internet and other packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) represent examples of the state of the art. Such standards are from time-to-time superseded by faster or more efficient equivalents having essentially the same functions. Wireless standards for device detection (e.g., RFID), short-range communications (e.g., Bluetooth, WiFi, Zigbee), and long-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used by computer system 1100.
The illustrations of embodiments described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The exemplary embodiments can include combinations of features and/or steps from multiple embodiments. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, can be used in the subject disclosure.
The Abstract of the Disclosure is provided with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.
The present application claims the benefit of priority to U.S. Provisional Application No. 62/344,280 filed on Jun. 1, 2016, which is hereby incorporated herein by reference in its entirety. The present application further claims the benefit of priority to U.S. Provisional Application No. 62/481,789 filed on Apr. 5, 2017, which is hereby incorporated herein by reference in its entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3732500 | Dishal | May 1973 | A |
| 3781705 | Dishal | Dec 1973 | A |
| 3783397 | Dishal | Jan 1974 | A |
| 3887222 | Hammond | Jun 1975 | A |
| 3911366 | Baghdady | Oct 1975 | A |
| 4027264 | Gutleber | May 1977 | A |
| 4328591 | Baghdady | May 1982 | A |
| 4513249 | Baghdady | Apr 1985 | A |
| 4648124 | Mantovani et al. | Mar 1987 | A |
| 4712235 | Jones | Dec 1987 | A |
| 4761829 | Lynk et al. | Aug 1988 | A |
| 4859958 | Myers | Aug 1989 | A |
| 4992747 | Myers | Feb 1991 | A |
| 5038115 | Myers | Aug 1991 | A |
| 5038145 | DeCesare et al. | Aug 1991 | A |
| 5048015 | Zilberfarb | Sep 1991 | A |
| 5168508 | Iwasaki | Dec 1992 | A |
| 5185762 | Schilling | Feb 1993 | A |
| 5226057 | Boren | Jul 1993 | A |
| 5263048 | Wade | Nov 1993 | A |
| 5282023 | Scarpa | Jan 1994 | A |
| 5301197 | Yamada et al. | Apr 1994 | A |
| 5303413 | Braegas | Apr 1994 | A |
| 5307517 | Rich | Apr 1994 | A |
| 5325204 | Scarpa | Jun 1994 | A |
| 5339184 | Tang | Aug 1994 | A |
| 5343496 | Honig | Aug 1994 | A |
| 5381150 | Hawkins et al. | Jan 1995 | A |
| 5475868 | Duque-Anton et al. | Dec 1995 | A |
| 5497505 | Koohgoli et al. | Mar 1996 | A |
| 5500872 | Kinney | Mar 1996 | A |
| 5541959 | Myers | Jul 1996 | A |
| 5570350 | Myer | Oct 1996 | A |
| 5596600 | Dimos | Jan 1997 | A |
| 5640146 | Campana | Jun 1997 | A |
| 5640385 | Long | Jun 1997 | A |
| RE35650 | Partyka | Nov 1997 | E |
| 5703874 | Schilling | Dec 1997 | A |
| 5721733 | Wang et al. | Feb 1998 | A |
| 5757246 | Johnson et al. | May 1998 | A |
| 5758275 | Cox | May 1998 | A |
| 5822373 | Addy | Oct 1998 | A |
| 5838742 | Abu-Dayya | Nov 1998 | A |
| 5852630 | Langberg | Dec 1998 | A |
| 5857143 | Kataoka | Jan 1999 | A |
| 5926761 | Reed et al. | Jul 1999 | A |
| 5947505 | Martin | Sep 1999 | A |
| 5949368 | DeCesare | Sep 1999 | A |
| 5960329 | Ganesh et al. | Sep 1999 | A |
| 5966657 | Sporre | Oct 1999 | A |
| 5970105 | Dacus | Oct 1999 | A |
| 5974101 | Nago | Oct 1999 | A |
| 5978362 | Lee | Nov 1999 | A |
| 5991273 | Abu-Dayya et al. | Nov 1999 | A |
| 5999574 | Sun et al. | Dec 1999 | A |
| 6005899 | Khayrallah | Dec 1999 | A |
| 6009129 | Kenney et al. | Dec 1999 | A |
| 6020783 | Coppola | Feb 2000 | A |
| 6035213 | Tokuda | Mar 2000 | A |
| 6038250 | Shou et al. | Mar 2000 | A |
| 6047175 | Trompower | Apr 2000 | A |
| 6052158 | Nielsen | Apr 2000 | A |
| 6104934 | Patton | Aug 2000 | A |
| 6115409 | Upadhyay | Sep 2000 | A |
| 6115580 | Chuprun et al. | Sep 2000 | A |
| 6118805 | Bergstrom et al. | Sep 2000 | A |
| 6122309 | Bergstrom et al. | Sep 2000 | A |
| 6125139 | Hendrickson et al. | Sep 2000 | A |
| 6127962 | Martinson | Oct 2000 | A |
| 6130907 | Chen | Oct 2000 | A |
| 6133942 | Lee | Oct 2000 | A |
| 6167240 | Carlsson et al. | Dec 2000 | A |
| 6167244 | Tomoe | Dec 2000 | A |
| 6205334 | Dent | Mar 2001 | B1 |
| 6208629 | Jaszewski et al. | Mar 2001 | B1 |
| 6215812 | Young | Apr 2001 | B1 |
| 6289004 | Mesecher et al. | Sep 2001 | B1 |
| 6313620 | Richardson | Nov 2001 | B1 |
| 6327245 | Satyanarayana et al. | Dec 2001 | B1 |
| 6327312 | Jovanovich | Dec 2001 | B1 |
| 6360077 | Mizoguchi | Mar 2002 | B2 |
| 6377606 | Toskala et al. | Apr 2002 | B1 |
| 6393284 | Dent | May 2002 | B1 |
| 6405043 | Jensen et al. | Jun 2002 | B1 |
| 6421077 | Reed et al. | Jul 2002 | B1 |
| 6426983 | Rakib et al. | Jul 2002 | B1 |
| 6430164 | Jones | Aug 2002 | B1 |
| 6490460 | Soliman | Dec 2002 | B1 |
| 6577670 | Roberts | Jun 2003 | B1 |
| 6580899 | Dalgleish et al. | Jun 2003 | B1 |
| 6621454 | Reudink et al. | Sep 2003 | B1 |
| 6631264 | Tat et al. | Oct 2003 | B1 |
| 6631266 | Lee et al. | Oct 2003 | B1 |
| 6671338 | Gamal et al. | Dec 2003 | B1 |
| 6678520 | Wang et al. | Jan 2004 | B1 |
| 6704378 | Jagger | Mar 2004 | B2 |
| 6718166 | Cordone | Apr 2004 | B2 |
| 6791995 | Azenkot et al. | Sep 2004 | B1 |
| 6807405 | Jagger | Oct 2004 | B1 |
| 6843819 | Suzuki et al. | Jan 2005 | B2 |
| 6850764 | Patel | Feb 2005 | B1 |
| 6920424 | Padmanabhan | Jul 2005 | B2 |
| 6959170 | Vannatta | Oct 2005 | B2 |
| 6975673 | Rouquette et al. | Dec 2005 | B1 |
| 6976044 | Kilani | Dec 2005 | B1 |
| 7003310 | Youssefmir et al. | Feb 2006 | B1 |
| 7031266 | Patel et al. | Apr 2006 | B1 |
| 7054396 | Shan | May 2006 | B2 |
| 7089008 | Bäck et al. | Aug 2006 | B1 |
| 7103316 | Hall | Sep 2006 | B1 |
| 7317698 | Jagger | Jan 2008 | B2 |
| 7359691 | Adachi et al. | Apr 2008 | B2 |
| 7408907 | Diener et al. | Aug 2008 | B2 |
| 7424002 | Barratt et al. | Sep 2008 | B2 |
| 7424268 | Diener et al. | Sep 2008 | B2 |
| 7457382 | Jones | Nov 2008 | B1 |
| 7477915 | Leinonen et al. | Jan 2009 | B2 |
| 7525942 | Cordone | Apr 2009 | B2 |
| 7929953 | Jiang | Apr 2011 | B2 |
| 8054088 | Delforce et al. | Nov 2011 | B2 |
| 8055191 | Unger | Nov 2011 | B2 |
| 8219105 | Kronestedt et al. | Jul 2012 | B2 |
| 8229368 | Immendorf et al. | Jul 2012 | B1 |
| 8238954 | Liu et al. | Aug 2012 | B2 |
| 8412256 | Lee et al. | Apr 2013 | B2 |
| 8422469 | Guvenc | Apr 2013 | B2 |
| 8478342 | Vedantham et al. | Jul 2013 | B2 |
| 8503938 | Laroia et al. | Aug 2013 | B2 |
| 8583170 | Sundström et al. | Nov 2013 | B2 |
| 8605686 | Lundby et al. | Dec 2013 | B2 |
| 8694017 | Bhushan | Apr 2014 | B2 |
| 8718024 | Jagger et al. | May 2014 | B2 |
| 8781044 | Cordone et al. | Jul 2014 | B2 |
| 8811213 | Rai et al. | Aug 2014 | B1 |
| 8811552 | Bayesteh et al. | Aug 2014 | B2 |
| 8874125 | Khojastepour et al. | Oct 2014 | B2 |
| 8942117 | Tamaki et al. | Jan 2015 | B2 |
| 8989235 | Myers et al. | Mar 2015 | B2 |
| 9008680 | Abdelmonem | Apr 2015 | B2 |
| 9042497 | Wong et al. | May 2015 | B2 |
| 9071343 | Abdelmonem | Jun 2015 | B2 |
| 9209857 | Galeev et al. | Dec 2015 | B2 |
| 9226303 | Kronestedt | Dec 2015 | B2 |
| 9231650 | Abdelmonem | Jan 2016 | B2 |
| 9369909 | Wong et al. | Jun 2016 | B2 |
| 9397923 | Otonari et al. | Jul 2016 | B2 |
| 9413677 | Vargantwar et al. | Aug 2016 | B1 |
| 9438285 | Wyville et al. | Sep 2016 | B2 |
| 9521603 | Yamazaki et al. | Dec 2016 | B2 |
| 9642155 | Sandrasegaran et al. | May 2017 | B2 |
| 9647720 | Abdelmonem et al. | May 2017 | B2 |
| 9654170 | Abdelmonem | May 2017 | B2 |
| 9668223 | Abdelmonem et al. | May 2017 | B2 |
| 9729301 | Galeev et al. | Aug 2017 | B2 |
| 9742547 | Wang et al. | Aug 2017 | B2 |
| 9768812 | Tsui et al. | Sep 2017 | B1 |
| 9853747 | Shariat et al. | Dec 2017 | B2 |
| 10079667 | Galeev et al. | Sep 2018 | B2 |
| 10244483 | Abdelmonem et al. | Mar 2019 | B2 |
| 20010046867 | Mizoguchi | Nov 2001 | A1 |
| 20020057751 | Jagger | May 2002 | A1 |
| 20020110206 | Becker et al. | Aug 2002 | A1 |
| 20020155812 | Takada | Oct 2002 | A1 |
| 20030081277 | Corbeil et al. | May 2003 | A1 |
| 20030123530 | Maeda | Jul 2003 | A1 |
| 20030142759 | Anderson et al. | Jul 2003 | A1 |
| 20030193366 | Barksdale | Oct 2003 | A1 |
| 20030216122 | Cordone | Nov 2003 | A1 |
| 20040042561 | Ho et al. | Mar 2004 | A1 |
| 20040048574 | Walker et al. | Mar 2004 | A1 |
| 20040088637 | Wada | May 2004 | A1 |
| 20040160927 | Yang et al. | Aug 2004 | A1 |
| 20040162093 | Bevan et al. | Aug 2004 | A1 |
| 20040198451 | Varghese | Oct 2004 | A1 |
| 20040202136 | Attar et al. | Oct 2004 | A1 |
| 20040223484 | Xia et al. | Nov 2004 | A1 |
| 20050078734 | Baker | Apr 2005 | A1 |
| 20050117676 | Liu | Jun 2005 | A1 |
| 20050201498 | Nakai | Sep 2005 | A1 |
| 20050271009 | Shirakabe et al. | Dec 2005 | A1 |
| 20060025127 | Cromer et al. | Feb 2006 | A1 |
| 20060068849 | Bernhard et al. | Mar 2006 | A1 |
| 20060094372 | Ahn et al. | May 2006 | A1 |
| 20060105769 | Flondro et al. | May 2006 | A1 |
| 20060153283 | Scharf et al. | Jul 2006 | A1 |
| 20060246938 | Hulkkonen et al. | Nov 2006 | A1 |
| 20070014254 | Chung et al. | Jan 2007 | A1 |
| 20070025455 | Greenwood et al. | Feb 2007 | A1 |
| 20070047494 | Cordone | Mar 2007 | A1 |
| 20070091896 | Liu | Apr 2007 | A1 |
| 20070105520 | Van Houtum | May 2007 | A1 |
| 20070115878 | Ashish et al. | May 2007 | A1 |
| 20070129071 | Shapira et al. | Jun 2007 | A1 |
| 20070173252 | Jiang | Jul 2007 | A1 |
| 20070183483 | Narayan | Aug 2007 | A1 |
| 20070189225 | Qian et al. | Aug 2007 | A1 |
| 20070274279 | Wood | Nov 2007 | A1 |
| 20080043612 | Geile | Feb 2008 | A1 |
| 20080043657 | Ishii et al. | Feb 2008 | A1 |
| 20080081655 | Shin et al. | Apr 2008 | A1 |
| 20080089296 | Kazmi et al. | Apr 2008 | A1 |
| 20080160916 | Jagger et al. | Jul 2008 | A1 |
| 20080166976 | Rao et al. | Jul 2008 | A1 |
| 20080176519 | Kwak et al. | Jul 2008 | A1 |
| 20080285526 | Gorokhov et al. | Nov 2008 | A1 |
| 20090086841 | Guo et al. | Apr 2009 | A1 |
| 20090131067 | Aaron et al. | May 2009 | A1 |
| 20090161614 | Grandblaise | Jun 2009 | A1 |
| 20090233544 | Oyman et al. | Sep 2009 | A1 |
| 20090233568 | Zhang | Sep 2009 | A1 |
| 20090245331 | Palanki et al. | Oct 2009 | A1 |
| 20090245409 | Kandukuri Narayan et al. | Oct 2009 | A1 |
| 20090247107 | Roy et al. | Oct 2009 | A1 |
| 20090310561 | Grob et al. | Dec 2009 | A1 |
| 20090325509 | Mattisson et al. | Dec 2009 | A1 |
| 20100002575 | Eichinger et al. | Jan 2010 | A1 |
| 20100029289 | Love et al. | Feb 2010 | A1 |
| 20100046374 | Ludwig et al. | Feb 2010 | A1 |
| 20100054373 | Reial et al. | Mar 2010 | A1 |
| 20100061244 | Meier et al. | Mar 2010 | A1 |
| 20100098051 | Uemura | Apr 2010 | A1 |
| 20100099415 | Li et al. | Apr 2010 | A1 |
| 20100099449 | Borran et al. | Apr 2010 | A1 |
| 20100118921 | Abdelmonem et al. | May 2010 | A1 |
| 20100157934 | Tanno et al. | Jun 2010 | A1 |
| 20100159858 | Dent et al. | Jun 2010 | A1 |
| 20100167778 | Raghothaman et al. | Jul 2010 | A1 |
| 20100202400 | Richardson | Aug 2010 | A1 |
| 20100220670 | Teo et al. | Sep 2010 | A1 |
| 20100227613 | Kim et al. | Sep 2010 | A1 |
| 20100246503 | Fox et al. | Sep 2010 | A1 |
| 20100246512 | Kawamura et al. | Sep 2010 | A1 |
| 20100248736 | Hulkkonen et al. | Sep 2010 | A1 |
| 20100254292 | Kim et al. | Oct 2010 | A1 |
| 20100255868 | Lee et al. | Oct 2010 | A1 |
| 20100267408 | Lee et al. | Oct 2010 | A1 |
| 20100279724 | Li et al. | Nov 2010 | A1 |
| 20100303183 | Desai | Dec 2010 | A1 |
| 20100304681 | Ghassemzadeh et al. | Dec 2010 | A1 |
| 20100309864 | Tamaki et al. | Dec 2010 | A1 |
| 20100310026 | Sikri et al. | Dec 2010 | A1 |
| 20100315970 | Ramamurthi et al. | Dec 2010 | A1 |
| 20100330919 | Gurney et al. | Dec 2010 | A1 |
| 20110014938 | Shekalim | Jan 2011 | A1 |
| 20110044188 | Luo et al. | Feb 2011 | A1 |
| 20110045856 | Feng et al. | Feb 2011 | A1 |
| 20110075754 | Smith et al. | Mar 2011 | A1 |
| 20110096703 | Nentwig et al. | Apr 2011 | A1 |
| 20110117967 | Vedantham et al. | May 2011 | A1 |
| 20110130098 | Madan et al. | Jun 2011 | A1 |
| 20110136497 | Youtz et al. | Jun 2011 | A1 |
| 20110149764 | Wietfeldt et al. | Jun 2011 | A1 |
| 20110164659 | Kawamura et al. | Jul 2011 | A1 |
| 20110170440 | Gaal et al. | Jul 2011 | A1 |
| 20110183679 | Moon et al. | Jul 2011 | A1 |
| 20110188544 | Ponnuswamy et al. | Aug 2011 | A1 |
| 20110200126 | Bontu et al. | Aug 2011 | A1 |
| 20110222591 | Furudate et al. | Sep 2011 | A1 |
| 20110244905 | Burstroem et al. | Oct 2011 | A1 |
| 20110258678 | Cowling et al. | Oct 2011 | A1 |
| 20110275399 | Englund et al. | Nov 2011 | A1 |
| 20110305306 | Hu et al. | Dec 2011 | A1 |
| 20110310747 | Seo et al. | Dec 2011 | A1 |
| 20120021753 | Damnjanovic et al. | Jan 2012 | A1 |
| 20120028663 | Nejatian et al. | Feb 2012 | A1 |
| 20120032854 | Bull et al. | Feb 2012 | A1 |
| 20120051315 | Wang et al. | Mar 2012 | A1 |
| 20120069806 | Norlén et al. | Mar 2012 | A1 |
| 20120082050 | Lysejko et al. | Apr 2012 | A1 |
| 20120082055 | Seyama | Apr 2012 | A1 |
| 20120088535 | Wang et al. | Apr 2012 | A1 |
| 20120134280 | Rotvold et al. | May 2012 | A1 |
| 20120135763 | Johnsson et al. | May 2012 | A1 |
| 20120149413 | Pedersen et al. | Jun 2012 | A1 |
| 20120163223 | Lo et al. | Jun 2012 | A1 |
| 20120164950 | Nentwig et al. | Jun 2012 | A1 |
| 20120182857 | Bertrand et al. | Jul 2012 | A1 |
| 20120182930 | Sawai et al. | Jul 2012 | A1 |
| 20120195216 | Wu et al. | Aug 2012 | A1 |
| 20120207038 | Choi et al. | Aug 2012 | A1 |
| 20120213116 | Koo et al. | Aug 2012 | A1 |
| 20120236731 | Beaudin | Sep 2012 | A1 |
| 20120276937 | Astely et al. | Nov 2012 | A1 |
| 20120281731 | Jagger et al. | Nov 2012 | A1 |
| 20120282889 | Tanaka et al. | Nov 2012 | A1 |
| 20120282943 | Hsiao et al. | Nov 2012 | A1 |
| 20120307770 | Kubota | Dec 2012 | A1 |
| 20120315935 | Wang Helmersson et al. | Dec 2012 | A1 |
| 20120322453 | Weng et al. | Dec 2012 | A1 |
| 20130012134 | Jin et al. | Jan 2013 | A1 |
| 20130029658 | Jagger et al. | Jan 2013 | A1 |
| 20130051317 | Ji et al. | Feb 2013 | A1 |
| 20130058300 | Perets et al. | Mar 2013 | A1 |
| 20130071112 | Melester et al. | Mar 2013 | A1 |
| 20130077670 | Wang et al. | Mar 2013 | A1 |
| 20130090125 | Clifton et al. | Apr 2013 | A1 |
| 20130107737 | Lee et al. | May 2013 | A1 |
| 20130115988 | Sun et al. | May 2013 | A1 |
| 20130115999 | Sirotkin et al. | May 2013 | A1 |
| 20130142136 | Pi et al. | Jun 2013 | A1 |
| 20130143592 | Brisebois et al. | Jun 2013 | A1 |
| 20130150106 | Bucknell et al. | Jun 2013 | A1 |
| 20130163570 | Zhang et al. | Jun 2013 | A1 |
| 20130165169 | Lee et al. | Jun 2013 | A1 |
| 20130170574 | Fleming et al. | Jul 2013 | A1 |
| 20130170829 | Khatana | Jul 2013 | A1 |
| 20130171955 | Makhlouf | Jul 2013 | A1 |
| 20130194982 | Fwu et al. | Aug 2013 | A1 |
| 20130242791 | Lim et al. | Sep 2013 | A1 |
| 20130242939 | Wagner | Sep 2013 | A1 |
| 20130250885 | Davydov et al. | Sep 2013 | A1 |
| 20130260807 | Suresh et al. | Oct 2013 | A1 |
| 20130287077 | Fernando et al. | Oct 2013 | A1 |
| 20130316710 | Maaref et al. | Nov 2013 | A1 |
| 20140004866 | Dalsgaard et al. | Jan 2014 | A1 |
| 20140029524 | Dimou et al. | Jan 2014 | A1 |
| 20140071933 | Lee et al. | Mar 2014 | A1 |
| 20140086191 | Berntsen et al. | Mar 2014 | A1 |
| 20140098695 | Jeong et al. | Apr 2014 | A1 |
| 20140105136 | Tellado et al. | Apr 2014 | A1 |
| 20140105262 | Alloin et al. | Apr 2014 | A1 |
| 20140106695 | Luz et al. | Apr 2014 | A1 |
| 20140106697 | Wang et al. | Apr 2014 | A1 |
| 20140153433 | Zhou | Jun 2014 | A1 |
| 20140160955 | Lum et al. | Jun 2014 | A1 |
| 20140169279 | Song et al. | Jun 2014 | A1 |
| 20140187255 | Dimou et al. | Jul 2014 | A1 |
| 20140206339 | Lindoff et al. | Jul 2014 | A1 |
| 20140212129 | Huang et al. | Jul 2014 | A1 |
| 20140241293 | Luo | Aug 2014 | A1 |
| 20140256322 | Zhou et al. | Sep 2014 | A1 |
| 20140269318 | Hasarchi et al. | Sep 2014 | A1 |
| 20140269374 | Abdelmonem et al. | Sep 2014 | A1 |
| 20140274094 | Abdelmonem et al. | Sep 2014 | A1 |
| 20140274100 | Galeev et al. | Sep 2014 | A1 |
| 20140315593 | Vrzic et al. | Oct 2014 | A1 |
| 20140378077 | Din | Dec 2014 | A1 |
| 20150071239 | Zhang et al. | Mar 2015 | A1 |
| 20150092621 | Jalloul | Apr 2015 | A1 |
| 20150099527 | Zhuang | Apr 2015 | A1 |
| 20150105903 | Denny et al. | Apr 2015 | A1 |
| 20150236814 | Liu et al. | Aug 2015 | A1 |
| 20150236882 | Bertrand et al. | Aug 2015 | A1 |
| 20150244413 | Baudin et al. | Aug 2015 | A1 |
| 20150249965 | Dussmann et al. | Sep 2015 | A1 |
| 20150257165 | Gale et al. | Sep 2015 | A1 |
| 20150263844 | Kinnunen | Sep 2015 | A1 |
| 20150282206 | Kalhan et al. | Oct 2015 | A1 |
| 20150295695 | Davydov et al. | Oct 2015 | A1 |
| 20150296523 | Joshi | Oct 2015 | A1 |
| 20150303950 | Shattil | Oct 2015 | A1 |
| 20150304885 | Jalali | Oct 2015 | A1 |
| 20150318944 | Wigren et al. | Nov 2015 | A1 |
| 20150318945 | Abdelmonem et al. | Nov 2015 | A1 |
| 20150318946 | Abdelmonem et al. | Nov 2015 | A1 |
| 20150318964 | Abdelmonem et al. | Nov 2015 | A1 |
| 20150319763 | Abdelmonem et al. | Nov 2015 | A1 |
| 20150319768 | Abdelmonem et al. | Nov 2015 | A1 |
| 20150349819 | Meng | Dec 2015 | A1 |
| 20150350931 | Dillinger et al. | Dec 2015 | A1 |
| 20150350940 | Wilson et al. | Dec 2015 | A1 |
| 20150351106 | Wijetunge et al. | Dec 2015 | A1 |
| 20160006468 | Gale et al. | Jan 2016 | A1 |
| 20160029236 | Sudo | Jan 2016 | A1 |
| 20160050031 | Hwang et al. | Feb 2016 | A1 |
| 20160094318 | Shattil | Mar 2016 | A1 |
| 20160135061 | Abdelmonem | May 2016 | A1 |
| 20160142229 | Bevan et al. | May 2016 | A1 |
| 20160192362 | Galeev et al. | Jun 2016 | A1 |
| 20160198353 | Abdelmonem | Jul 2016 | A1 |
| 20160248487 | Sun | Aug 2016 | A1 |
| 20160249364 | Siomina | Aug 2016 | A1 |
| 20160249367 | Abdelmonem | Aug 2016 | A1 |
| 20160254840 | Abdelmonem et al. | Sep 2016 | A1 |
| 20160254886 | Bontu et al. | Sep 2016 | A1 |
| 20160295574 | Papasakellariou | Oct 2016 | A1 |
| 20160302125 | Tejedor et al. | Oct 2016 | A1 |
| 20160302209 | Behravan et al. | Oct 2016 | A1 |
| 20160316396 | Yu et al. | Oct 2016 | A1 |
| 20160323086 | Abdelmonem et al. | Nov 2016 | A1 |
| 20160353455 | Jagger | Dec 2016 | A1 |
| 20160360370 | Edge et al. | Dec 2016 | A1 |
| 20160360490 | Henia | Dec 2016 | A1 |
| 20160366605 | Tsui et al. | Dec 2016 | A1 |
| 20160381680 | Yasukawa et al. | Dec 2016 | A1 |
| 20170026158 | Burström et al. | Jan 2017 | A1 |
| 20170026866 | Sandberg et al. | Jan 2017 | A1 |
| 20170034693 | Ono et al. | Feb 2017 | A1 |
| 20170063470 | Smith et al. | Mar 2017 | A1 |
| 20170104522 | Zinevich | Apr 2017 | A1 |
| 20170118758 | Li | Apr 2017 | A1 |
| 20170188363 | Ellinikos et al. | Jun 2017 | A1 |
| 20170188374 | Galeev | Jun 2017 | A1 |
| 20170196013 | Shin et al. | Jul 2017 | A1 |
| 20170207816 | Abdelmonem et al. | Jul 2017 | A1 |
| 20170215096 | Moon et al. | Jul 2017 | A1 |
| 20170215197 | Sagong et al. | Jul 2017 | A1 |
| 20170230128 | Abdelmonem et al. | Aug 2017 | A1 |
| 20170230983 | Abdelmonem et al. | Aug 2017 | A1 |
| 20170280464 | Jagger | Sep 2017 | A1 |
| 20170289011 | Johnsson et al. | Oct 2017 | A1 |
| 20170294994 | Shor et al. | Oct 2017 | A1 |
| 20170302332 | Abdelmonem et al. | Oct 2017 | A1 |
| 20170310450 | Galeev | Oct 2017 | A1 |
| 20170317807 | Abdelmonem et al. | Nov 2017 | A1 |
| 20170324469 | Jalali | Nov 2017 | A1 |
| 20170374625 | Abdelmodem et al. | Dec 2017 | A1 |
| 20170374626 | Abdelmonem et al. | Dec 2017 | A1 |
| 20180020462 | Xiong et al. | Jan 2018 | A1 |
| 20180063799 | Sadek et al. | Mar 2018 | A1 |
| 20180069682 | Abdelmonem et al. | Mar 2018 | A1 |
| 20180076910 | Zhang et al. | Mar 2018 | A1 |
| 20180124813 | Li et al. | May 2018 | A1 |
| 20180139004 | Abdelmonem et al. | May 2018 | A1 |
| 20180167189 | Abdelmonem et al. | Jun 2018 | A9 |
| 20180206240 | Abdelmonem | Jul 2018 | A1 |
| 20180220377 | Abdelmonem et al. | Aug 2018 | A1 |
| 20180254878 | Abdelmonem et al. | Sep 2018 | A1 |
| 20180287660 | Arambepola et al. | Oct 2018 | A1 |
| 20180287696 | Barbieri et al. | Oct 2018 | A1 |
| 20180294827 | Abdelmonem | Oct 2018 | A1 |
| 20180294903 | Goodman et al. | Oct 2018 | A1 |
| 20180294908 | Abdelmonem | Oct 2018 | A1 |
| 20180294932 | Abdelmonem | Oct 2018 | A1 |
| 20180295020 | Mo et al. | Oct 2018 | A1 |
| 20180295553 | Abdelmonem | Oct 2018 | A1 |
| 20180295588 | Abdelmonem | Oct 2018 | A1 |
| 20180295617 | Abdelmonem | Oct 2018 | A1 |
| 20180295632 | Goodman et al. | Oct 2018 | A1 |
| 20180295633 | Abdelmonem | Oct 2018 | A1 |
| 20180324710 | Abdelmonem et al. | Nov 2018 | A1 |
| 20180324812 | Abdelmonem et al. | Nov 2018 | A1 |
| 20180332593 | Galeev et al. | Nov 2018 | A1 |
| 20180332594 | Abdelmonem et al. | Nov 2018 | A1 |
| 20180376431 | Abdelmonem et al. | Dec 2018 | A1 |
| 20190052294 | Abdelmonem | Feb 2019 | A1 |
| 20190052381 | Abdelmonem | Feb 2019 | A1 |
| 20190053247 | Abdelmonem et al. | Feb 2019 | A1 |
| 20190082394 | Abdelmonem et al. | Mar 2019 | A1 |
| 20190116603 | Abdelmonem et al. | Apr 2019 | A1 |
| 20190141641 | Abdelmonem et al. | May 2019 | A1 |
| 20190150099 | Abdelmonem | May 2019 | A1 |
| 20190158258 | Abdelmonem | May 2019 | A1 |
| 20190182133 | Gupta et al. | Jun 2019 | A1 |
| 20190207735 | Abdelmonem | Jul 2019 | A1 |
| 20190222243 | Abdelmonem | Jul 2019 | A1 |
| 20190222329 | Abdelmonem | Jul 2019 | A1 |
| 20190239234 | Abdelmonem et al. | Aug 2019 | A1 |
| 20190253878 | Yu et al. | Aug 2019 | A1 |
| 20190254041 | Abdelmonem et al. | Aug 2019 | A1 |
| 20190349102 | Hasarchi et al. | Nov 2019 | A1 |
| 20190372613 | Abdelmonem | Dec 2019 | A1 |
| 20200059258 | Goodman et al. | Feb 2020 | A1 |
| Number | Date | Country |
|---|---|---|
| 2260653 | Jan 2000 | CA |
| 2288633 | Apr 2000 | CA |
| 1173101 | Feb 1998 | CN |
| 0704986 | Apr 1996 | EP |
| 0812069 | Dec 1997 | EP |
| 2288061 | Feb 2011 | EP |
| 2800412 | Nov 2014 | EP |
| 2955862 | Dec 2015 | EP |
| 2304000 | Mar 1997 | GB |
| 06-061876 | Mar 1994 | JP |
| 09-326713 | Dec 1997 | JP |
| 2018074296 | May 2018 | JP |
| 199810514 | Mar 1998 | WO |
| 200046929 | Aug 2000 | WO |
| 2007063514 | Jun 2007 | WO |
| 2008033369 | Mar 2008 | WO |
| 2009005420 | Jan 2009 | WO |
| 2009019074 | Feb 2009 | WO |
| 20120116755 | Sep 2012 | WO |
| 2012158046 | Nov 2012 | WO |
| 2012172476 | Dec 2012 | WO |
| 2015002584 | Jan 2015 | WO |
| Entry |
|---|
| International Search Report and Written Opinion for PCT/US2017/034237 dated Aug. 7, 2017. |
| Article 34 Amendment for PCT/US17/34237 filed, Mar. 28, 2018. |
| International Search Report and Written Opinion for PCT/US2019/030835 dated Aug. 22, 2019. |
| “Excel Average Formula/Function without including Zeros”, Ozgrid.com, Aug. 8, 2011, 3 pages. |
| “International Preliminary Report on Patentability”, PCT/US2016/026212, dated Nov. 16, 2017, 9. |
| “International Search Report & Written Opinion”, PCT/US01/11351, dated Apr. 2002. |
| “PCT/US16/26212 International Search Report dated Jun. 7, 2016.”, Jun. 7, 2016, 12 pages. |
| “U.S. Appl. No. 13/956,690, filed Aug. 1, 2013, pp. 4-10”. |
| Berlemann, et al., “Cognitive Radio and Management of Spectrum and Radio Resources in Reconfigurable Networks”, Wireless World Research Forum, Working Group 6 White Paper, 2005. |
| Kim, Kihong , “Interference Mitigation in Wireless Communications”, Aug. 23, 2005, 133 pages. |
| Milstein, , “Interference Rejection Techniques in Spread Spectrum Communications”, Proceedings of the IEEE, vol. 76, No. 6, Jun. 1988. |
| Patent Cooperation Treaty, , “International Search Report and Written Opinion dated Jun. 1, 2010, International Application No. PCT/US2009/064191”. |
| Patent Cooperation Treaty, , “International Search Report and Written Opinion dated Jun. 1, 2010, from International Application No. PCT/US2009/064191”. |
| Poor, et al., “Narrowband Interference Suppression in Spread Spectrum COMA”, IEEE Personal Communications Magazine, Third Quarter, 1994, pp. 14-27. |
| Salgado-Galicia, Hector et al., “A Narrowband Approach to Efficient PCS Spectrum Sharing Through Decentralized DCA Access Policies”, IEEE Personal Communications, Feb. 1997, 24-34. |
| Tacconi, Pablo et al., “Method and Apparatus for Performing Signal Conditioning to Mitigate Interference Detected in a Communication System”, U.S. Appl. No. 15/603,851, filed May 24, 2017, May 24, 2017. |
| Zyren, Jim et al., “Overview of the 3GPP Long Term Evolution Physical Layer”, Freescale Semiconductor, Jul. 2007, 27 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20170353929 A1 | Dec 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62344280 | Jun 2016 | US | |
| 62481789 | Apr 2017 | US |
