Patent Application
20170272305
The present invention relates generally to cellular networks. More particularly, the present invention relates to a systems and methods for cellular network anomaly detection using spatially aggregated data.
Systems and methods for detecting anomalies on cellular networks ranging from 1G to 5G are known. Indeed, a variety of tools exist in the marketplace, and a variety of known methods for identifying such anomalies are utilized throughout the industry.
However, known systems and methods rely on a linearly collected set of data that reflects a user's forward travel through both space and time. Furthermore, known systems and methods rely on the detection of known and predefined events and criteria that match linearly collected events. Such systems and methods can fail to detect some cellular network anomalies because detection cannot occur until an anomaly event occurs and exceeds a known and predefined threshold. Moreover, such systems and methods heavily rely on being in the right place at the right time to capture the anomaly event.
In view of the above, there is a continuing, ongoing need for improved systems and methods.
While this invention is susceptible of an embodiment in many different forms, there are shown in the drawings and will be described herein in detail specific embodiments thereof with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention. It is not intended to limit the invention to the specific illustrated embodiments.
Embodiments disclosed herein can include systems and methods for cellular network anomaly detection using spatially aggregated data. For example, in some embodiments, data can be captured at multiple times in non-continuous collections, aggregated into data sets, and used to extrapolate anomaly events prior to the event exceeding a predefined threshold. Furthermore, in some embodiments, systems and methods disclosed herein can monitor for and identify changes and fluctuations in overall performance prior to anomaly events degrading to the point of a critical alarm. That is, in some embodiments, burgeoning problem areas can be identified before they fail.
It is to be understood that systems and methods disclosed herein can be implemented with a transceiver device, a memory device, and a user interface device, each of which can be in communication with control circuitry, one or more programmable processors, and executable control software as would be understood by one of ordinary skill in the art. In some embodiments, the transceiver device can communicate with and receive signals from one or more cellular networks at multiple, non-continuous times, and in some embodiments, the memory device can store the signals received by the transceiver device, data related to the signals received by the transceiver device, and a plurality of predefined threshold values. In some embodiments, the executable control software can be stored on a transitory or non-transitory computer readable medium, including, but not limited to, local computer memory, RAM, optical storage media, magnetic storage media, flash memory, and the like, and some or all of the control circuitry, the programmable processors, and the control software can execute and control at least some of the methods described herein.
External interference in LTE communications is a major problem for network operators, and such external interference can be found in either the uplink or the downlink of data signals. However, it is typically very difficult for a network operator to identify a physical location of sources of downlink interference. To solve these and other problems, some embodiments disclosed herein can include systems and methods for detecting LTE downlink external interference, for example, by identifying areas of external interference using the same set of drive test data that is collected in connection with known systems and methods for, for example, benchmarking, optimization, design, and integration.
For example, some systems and methods disclosed herein can use a wide dynamic range of RF scanning data to calculate a wideband interfering signal received power (ISRP), which can be derived as a difference of a measured wideband received signal received power (RSRP) and a measured wideband carrier to interference and noise ratio (CINR) for the LTE downlink of data signals. Then, systems and methods disclosed herein can identify areas that have a high ISRP value as compared to a neighboring area and identify such areas as possible locations of high downlink external interference. In some embodiments, systems and methods disclosed herein can also ensure that areas identified as possible locations of high downlink external interference are not in close proximity to a transmitting cellular tower by identifying or referencing threshold values for the RSRP or actual tower location information, when available.
After the relevant data is identified as in 110, the method 100 can include, for each of the plurality of bins and for each of the plurality of frequency bands on the LTE downlink channel, determining whether a best server RSRP value is less than an RSRP poor coverage threshold value as in 120. If yes, then the method 100 can include classifying a respective one of the plurality of bins as a downlink coverage hole with inadequate coverage, calculating a coverage hole value (CHV) as the best server RSRP value minus an average buffer size RSRP value, assigning a rank for a respective one of a plurality of geographic areas on a map based on the CHV, and displaying coverage holes on the map as C1 . . . C10 as in 130, wherein the highest CHV can be assigned C1 to identify the worst coverage hole.
However, if the method 100 determines that the best RSRP value is not less than the RSRP poor coverage threshold value as in 120, then the method can include executing a pilot pollution algorithm and an ISRP algorithm for a respective one of the plurality of bins as in 140. Then, based on the results of executing the algorithms as in 140, the method 100 can include determining whether the respective one of the plurality of bins includes both pilot pollution and interference as in 150.
If the method 100 determines that the respective one of the plurality of bins includes both pilot pollution and ISRP as in 150, then the method 100 can include determining whether the best server RSRP value is greater than a good coverage threshold value and whether the best server CINR value is less than a good quality threshold value as in 160. If yes, then the method 100 can include classifying the respective one of the plurality of bins as having a pilot pollution issue and ranking the respective one of the plurality of bins as in 170. However, if the method 100 determines that the best server RSRP value is not greater than the good coverage threshold value or that the best server CINR value is less than the good quality threshold value as in 160, then the method 100 can include classifying the respective one of the plurality of bins as having an interference issue and ranking the respective one of the plurality of bins as in 180.
If the method 100 determines that the respective one of the plurality of bins does not include both pilot pollution and ISRP as in 150, then the method 100 can include determining whether the respective one of the plurality of bins includes either pilot pollution or ISRP as in 190. If not, then the method 100 can include determining that no issues are found with respect to the respective one of the plurality of bins as in 192. However, if the method 100 determines that the respective one of the plurality of bins includes either pilot pollution or ISRP as in 190, then the method 100 can include ranking each of the plurality of bins for existing problem types based on the plurality of geographic areas on a map and issuing ranking criteria for each of the problem types as in 194.
As explained above, the method 100 can include executing a pilot pollution algorithm for a respective one of the plurality of bins as in 140, and
However, if the method 200 determines that the number of measured PCIs in the possible server set is greater than the maximum server threshold value and the best server CINR value is less than the good coverage threshold value as in 220, then the method 200 can include determining whether the PCI RSRP value for any possible server is greater than or equal to the best server RSRP value minus a dominance power threshold value as in 240. If not, then the method 200 can include classifying the pilot pollution as having too many servers as in 250. However, if the method 200 determines that the PCI RSRP value for any possible server is not greater than or equal to the best server RSRP value minus the dominant power threshold value as in 240, then the method 200 can include classifying the pilot pollution as having a non-dominant server as in 260.
In accordance with the method 200, systems and methods disclosed herein can count the number of non-dominant servers and assign a ranking based thereon, wherein a higher ranking can correspond to bigger issues with more servers in the dominant range. Similarly, systems and methods disclosed herein can count the number of bins with too many servers in a bin and assign a ranking based thereon. In some embodiments, bins with a non-dominant server can be assigned a higher ranking then bins with too many servers, and in some embodiments, bins within a predetermined radius of a first bin assigned a ranking, for example, 0.1 miles, can be assigned the same ranking.
As explained above, the method 100 can include executing an ISRP algorithm for a respective one of the plurality of bins as in 140, and
Then, the method 300 can include determining whether the difference between the best server ISRP value and the buffer ISRP value is greater than an interference threshold value as in 320. If not, then the method 300 can include classifying external interference as no interference as in 330.
However, if the method 300 determines that the difference between the best server ISRP value and the buffer ISRP value is not greater than the interference threshold value as in 320, then the method 300 can include determining whether network topology information is available as in 340. If not, then the method 300 can include classifying external interference for the respective one of the plurality of bins as in 350 in accordance with the following rules. In some embodiments, the respective one of the plurality of bins can be marked as having external interference, and the distance from the closest emitter and the difference between the best server ISRP value and the buffer ISRP value can determine the interference ranking. In some embodiments, if no distance information is available, then the interference ranking can be based on only the difference between the best server ISRP value and the buffer ISRP value, and in some embodiments, bins within a predetermined radius of a first bin assigned a ranking, for example, 0.1 miles, can be assigned the same ranking.
If the method 300 determines that network topology information is available as in 340, then the method 300 can include determining whether the distance to the closest cell site is less than an emitter radius threshold value as in 360. If not, then the method 300 can include classifying external interference for the respective one of the plurality of bins as in 350 and as explained above. However, if the method 300 determines that the distance to the closest cell site is not less than the emitter radius threshold value as in 360, the method 300 can include classifying external interference as no interference as in 330.
As explained above, some embodiments disclosed herein can include systems and methods for detecting LTE downlink external interference. However, uplink interference in LTE communication can also occur and be caused by a number of different sources of interference. It can be difficult to locate such sources, both in terms of frequency and physical location because the sources of interference can be continuous or intermittent and can be wideband or narrow band. Nevertheless, for TD-LTE technology in which both uplink and downlink communications are transmitted in the same frequency range, it is essential that sources of interference on the uplink communication be identified with accuracy. Accordingly, in addition to or as an alternative to detecting downlink external interference, some systems and methods disclosed herein can include detecting TD-LTE uplink external interference, for example, by identifying the frequency and a possible physical location of a source of interference on the uplink channel.
For example, some systems and methods disclosed herein can use LTE power analysis measurements for TD-LTE technology to identify interference in the uplink communications. In some embodiments, systems and methods disclosed herein can analyze uplink transmission time based slots across various transmission techniques and identify as possible sources of interference anomalies in the data transmissions that are higher than an expected power level across the time based slots. In some embodiments, systems and methods can also identify wideband or narrow band sources of interference that are continuous in time or intermittent.
As explained above, uplink interference in LTE communication can be caused by a number of different sources of interference. However, it can be difficult to locate such sources, both in terms of frequency and physical location because the sources of interference can be continuous or intermittent and can be wideband or narrow band. Nevertheless, for FD-LTE technology in which both uplink and downlink communications are transmitted in the same frequency range, it is also essential that sources of interference on the uplink communication be identified with accuracy. Accordingly, in addition to or as an alternative to detecting TD-LTE uplink external interference, some systems and methods disclosed herein can include detecting FD-LTE uplink external interference, for example, by identifying the frequency and a possible physical location of a source of interference on the uplink channel.
For example, some systems and methods disclosed herein can use a spectrum analysis to detect an average minimum power value for each of a plurality of bins and for each of a plurality of frequency bands on the FD-LTE uplink channel and, based thereon, identify a maximum power value of average minimum power values across the plurality of bins and the plurality of frequency bins and determine whether the identified value is higher than a predefined interference threshold value for expected user device transmissions. When the identified value is higher than the predefined interference threshold value, systems and methods disclosed herein can calculate a coverage area or range for the identified value in terms of bins and frequencies to identify a geographic area affected by a source of interference and a bandwidth of the source of interference. Systems and methods disclosed herein can then repeat the above-identified steps to identify other possible sources of interference. In some embodiments, systems and methods disclosed herein can display on a user interface the top N sources of interference and can receive user input to rank each of the displayed sources or to filter the displayed sources according to a time range to distinguish between the origination and the resolution of sources of interference, when applicable.
In accordance with the above, in some embodiments, power values for each of a plurality of frequencies can be divided into different ones of a plurality of bins based on geography so that values from the same geographic region are grouped in the same bin. Then, a minimum power value of each of the plurality of bins for each of the plurality of frequencies can be identified, and the one of the plurality of bins and the one of the plurality of frequencies with the maximum power value of all of the identified minimum power values can be identified as a possible source of interference. Table 1 illustrates an exemplary matrix in accordance with disclosed embodiments and shows the identified minimum power value of the highest point in the matrix as −95 in Bin 2 and the 709.06 frequency range. Accordingly, the possible source of interference can be located in a geographic region corresponding to Bin 2 and operate in the 709.06 frequency range.
In accordance with the above, systems and methods can determine whether the identified value of the highest point in the matrix is higher than a predefined interference threshold, for example, −100. When the identified value is higher than the predefined interference threshold, systems and methods disclosed herein can evaluate the coverage area of the identified possible source of interference in terms of bandwidth and distance to identify points until which the minimum values as identified above for each data point are higher than the predefined interference threshold. For example, as seen in Table 1, the points surrounding the identified minimum power value of the highest point in the matrix that are higher than the predefined interference threshold can be in Bin 2 and the 709.045 frequency range (−97), in Bin 2 and the 709.075 frequency range (−98), in Bin 3 and the 709.045 frequency range (−99), in Bin 3 and the 709.06 frequency range (−96), and in Bin 3 and the 709.075 frequency range (−99). Accordingly, the coverage area of the possible source of interference can include the geographic regions corresponding to Bin 2 and Bin 3 and operate in the 709.045, 709.06, and 709.075 frequency ranges.
The above-identified steps can be repeated until the top N number of possible sources of interference are identified. For example, as seen in Table 1, a second identified minimum power value in the matrix of −98 can be identified in Bin 3 in the 709.015 frequency range, and a point surrounding the second identified minimum power value that is higher than the predefined interference threshold can be in Bin 4 and the 709.015 frequency range (−99). Accordingly, the second possible source of interference can be located in a geographic region corresponding to Bin 3 and operate in the 709.015 frequency range, and the coverage area of the second possible source of interference can cover the geographic area corresponding to Bin 3 and Bin 4 and operate in the 709.015 frequency range. Then, the top N identified possible sources of interference can be ranked according to their identified minimum values, and the ranked minimum values and their corresponding coverage size and bandwidth can be displayed on a user interface. For example, in some embodiments, the bandwidth corresponding to each identified minimum power value can be calculated on either side of a center frequency by counting frequency bins on each side of the center frequency and multiplying by a respective resolution bandwidth value, and the size of the geographic area affected by the possible source of interference can be calculated by summing the identified bins.
Although a few embodiments have been described in detail above, other modifications are possible. For example, the logic flows described above do not require the particular order described or sequential order to achieve desirable results. Other steps may be provided, steps may be eliminated from the described flows, and other components may be added to or removed from the described systems. Other embodiments may be within the scope of the invention.
From the foregoing, it will be observed that numerous variations and modifications may be effected without departing from the spirit and scope of the invention. It is to be understood that no limitation with respect to the specific system or method illustrated is intended or should be inferred. It is, of course, intended to cover by the appended claims all such modifications as fall within the spirit and scope of the claims.
This application claims priority to U.S. Provisional Patent Application No. 62/309,262 filed Mar. 16, 2016 and titled “System and Method for Cellular Network Anomaly Detection Using Spatially Aggregated Data.” U.S. Application No. 62/309,262 is hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62309262 | Mar 2016 | US |
