Communications module, device, and method for implementing a system acquisition function

Information

  • Patent Grant
  • 7620097
  • Patent Number
    7,620,097
  • Date Filed
    Tuesday, December 23, 2008
    15 years ago
  • Date Issued
    Tuesday, November 17, 2009
    14 years ago
Abstract
A communications module, device and corresponding method for facilitating PN code searching. The module and device have a PN sequence generator configurable to generate a plurality of PN sequences. The module and device also include computational units configurable to correlate received signal samples of a plurality of received signal samples with a corresponding PN sequence of the plurality of PN sequences, and further configurable to provide other hardware resources. A number of computational units from the plurality of computational units are selectively configured to correlate the received signal samples with the PN sequences—the number depending upon availability of the plurality of computational units from providing the other hardware resources. According to a preferred embodiment, a plurality of configurable computational units are selectively configurable to implement the PN sequence generator.
Description
BACKGROUND OF THE INVENTION

The present invention generally relates to communications functions. More specifically, the present invention relates to a communications module, device, and method for implementing a system acquisition function.


In CDMA communication systems, each base station differentiates amongst one another by using an unique PN code. A communication device, such as a mobile phone, is equipped with a system acquisition function, typically embodied in a searcher, to search for and locate the PN codes of the base stations within the vicinity of the mobile phone. Upon power-on, one of the initial tasks of the mobile phone is to find the strongest pilot signal from the nearby base stations as soon as possible. The task of finding the strongest pilot signal is commonly known as system or pilot acquisition and is usually performed by a searcher within the mobile phone.


Under one conventional approach, the system acquisition function within the mobile phone is implemented in the form of the searcher using a serial search technique that only utilizes a set of complex correlators to search for the correlation peak from one PN code offset to another. This approach consumes less power and requires less hardware; however, the search for the correlation peak may take longer.


Under another conventional approach, the searcher within the mobile phone is implemented using a traditional parallel search technique that utilizes several sets of fixed, dedicated correlators to compute the correlation peak in a concurrent manner. This other approach may shorten the search time but it does so at cost of incurring more hardware and power consumption. Furthermore, since the acquisition mode is typically less active than other modes, the exclusive use of fixed, dedicated correlators often results in a waste of hardware resources within the mobile phone.


More specifically, system or pilot acquisition in a CDMA communication system is typically performed as follows. Each base station continually broadcasts its own unique PN code in a periodic manner. One PN code from one base station differs from another PN code from another base station by an offset. Before a PN code can be identified by the mobile phone, the mobile phone first searches for signals at a particular frequency. As a result, only signals from base stations transmitting at that particular frequency are received by the mobile phone.


Next, the PN code of the base station which transmits the strongest pilot signal is identified and synchronized. The mobile phone receives signals from different base stations and these received signals are added up. Typically, the received signals are stored by the mobile phone before the correlation process begins. The mobile phone has a local PN sequence generator which is capable of generating sequences of PN codes. Initially, before the PN code of the base station which transmits the strongest pilot signal is identified, the PN sequence generator generates an initial PN code. This initial PN code is correlated with the received signals by a correlator residing in the mobile phone. Correlation is done to determine the power level of the received signals. The correlation results are examined to determine if the received signals representing the PN code of the transmitting base station fall within an acceptable time delay from the initial PN code to qualify as the strongest pilot signal. If the correlation results are below a predetermined threshold, i.e., the initial PN code generated by the local PN sequence generator does not qualify as the strongest pilot signal, then the local PN sequence generator shifts by one chip to generate another PN code and this other PN code is correlated with the received signals. The generation of PN codes and the correlation of these codes with the received signals continue until the strongest pilot signal is identified.


When the strongest pilot signal is identified, the PN code generated by the PN sequence generator and used to identify the strongest pilot signal is synchronized with the PN code of the base station which transmits the strongest pilot signal. Once the synchronization of the PN code is achieved, the mobile phone is able to communicate with the base station.


Furthermore, after pilot acquisition is completed, the mobile phone continues searching for nearby strong pilot signals and maintains a list to keep track of such signals. This process is commonly called set maintenance. That is, in addition to the strongest pilot signal, the mobile phone also searches for and keeps track of a number of additional pilot signals (and their associated PN codes) with different levels of signal strength. For example, the mobile phone may maintain an active set which keeps track of additional multipaths associated with the pilot signal of the base station that the mobile phone is currently communicating with, a candidate set with pilot signals whose strengths exceed certain threshold, and a neighbor set that includes pilot signals from cells that are in the vicinity of the cells that the mobile phone is communicating with. Maintaining a number of additional pilot signals (and their associated PN codes) facilitates the handoff process. A handoff typically occurs when a mobile phone is roaming from one area to another. This happens when a pilot signal transmitted from another base station is stronger than the one that the mobile phone is currently communicating with. The candidate set may be used to more efficiently identify the new base station transmitting the strongest pilot signal. This is because the strongest pilot signal is more likely to be one of the signals included in the candidate set. Hence, the associated PN code can be retrieved more quickly and communication with the new base station likewise can be established in a shorter period of time.


As can be seen above, the received signals need to be stored by the mobile phone so they can be subsequently used for correlation purposes. Furthermore, generation of the PN codes by the PN sequence generator is done in a sequential manner by shifting the current PN code.


Hence, it would be desirable to provide a method and system to implement a searcher for use with a mobile phone to more efficiently identify the PN code of the base station which transmits the strongest pilot signal.


SUMMARY OF THE INVENTION

A method and system for implementing a system acquisition function for use with a communication device is provided. According to one exemplary embodiment of the system, the system acquisition function is embodied in a searcher. The searcher is embedded in the communication device, such as, a mobile phone. The searcher includes one or more computational units which are used to perform a PN sequence generation function to generate PN sequences. Each PN sequence is comprised of a number of PN chips. The searcher further includes a number of computational units which are used to correlate received signal samples with the PN chips generated by the PN sequence generation function. As each signal sample is received by the communication device, the received signal sample is correlated (complex multiplied) with a PN sequence in a parallel manner using the computational units. The sample correlation results are then respectively accumulated within each computational unit that conducts the corresponding sample correlation. As the next signal sample is received, this newly received signal sample is similarly correlated with the next PN sequence in a parallel manner. Likewise, the sample correlation results are also accumulated. The foregoing process is repeated until all the signal samples needed to complete a signal correlation are received and correlated with the PN sequences. The number of PN chips within a PN sequence used to correlate with each received signal sample is equivalent to a correlation length chosen such that the correlation results between each received signal sample and the locally generated PN sequence are sufficiently reliable to determine whether the strongest pilot is found.


According to another aspect of the system, the computational units are implemented using adaptive hardware resources. The number of computational units which are used to implement the PN sequence generation function and the correlation function are adjustable depending on, for example, the amount of available adaptive hardware resources.


Reference to the remaining portions of the specification, including the drawings and claims, will realize other features and advantages of the present invention. Further features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention, are described in detail below with respect to accompanying drawings, like reference numbers indicate identical or functionally similar elements.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a simplified diagram illustrating an exemplary embodiment of an M-node having four (4) computational units in accordance with the present invention;



FIG. 2 is a simplified diagram illustrating an exemplary method for performing correlations in accordance with the present invention;



FIG. 3 is a simplified diagram illustrating the exemplary method as shown in FIG. 2 for performing an additional round of correlations in accordance with the present invention;



FIG. 4 is a simplified diagram illustrating a second exemplary method for performing correlations in accordance with the present invention;



FIG. 5 is a simplified diagram illustrating a third exemplary method for performing correlations in accordance with the present invention;



FIG. 6 is a block diagram illustrating an exemplary system embodiment in accordance with the present invention;



FIG. 7 is a flow diagram illustrating a first exemplary method embodiment in accordance with the present invention; and



FIG. 8 is a flow diagram illustrating a second exemplary method embodiment in accordance with the present invention.





DETAILED DESCRIPTION OF THE INVENTION

The present invention in the form of one or more exemplary embodiments will now be described. FIG. 1 is a simplified diagram illustrating an exemplary embodiment of the present invention. Referring to FIG. 1, there is shown a searcher 10 having a number of computational units 12a-m. The searcher 10 can be located in any type of communication device, such as a mobile phone. As will be further demonstrated below, each computational unit 12a-m correlates the received signal samples with a corresponding PN chip. In an exemplary embodiment, these computational units 12a-m are implemented using reconfigurable hardware resources within an adaptive computing architecture. Details relating to the adaptive computing architecture and how reconfigurable hardware resources are used to implement functions on an on-demand basis are disclosed in U.S. Pat. No. 6,836,839, issued Dec. 28, 2004, the disclosure of which is hereby incorporated by reference in their entirety as if set forth in full herein for all purposes. It should be understood that while the present invention is described as being in the searcher 10, it will be appreciated by a person of ordinary skill in the art that the present invention can be implemented in other manners within a communication device. For example, some or all of the functionality of the present invention as described herein may be implemented outside of the searcher 10 in other parts of the communication device.


In an exemplary embodiment, the computational units 12a-m are arranged in a sequential order and configured to calculate the correlations between the received signal samples and a number of PN sequences. The start of any two adjacent PN sequences is offset by one chip. More specifically, the computational units 12a-m correlate each received signal sample with their corresponding components of a PN sequence in a parallel manner.


The PN sequences used by the computational units 12a-m are generated in a successive, offset order. The starting position of each successive PN sequence is only one chip off from the preceding PN sequence. The PN chips of each PN sequence can be provided to the computational units 12a-m in a number of ways. For example, the PN chips can be generated by either a PN sequence generator implemented in the form of another computational unit (not shown) or a RISC processor. As will be described further below, each PN chip is shifted into a corresponding computational unit 12a-m. Each computational unit 12a-m includes a local memory for storing its corresponding PN chip.



FIG. 2 illustrates an exemplary method for performing correlations in accordance with the present invention. Assume the time duration of a received signal sample is Td, that is, one signal sample is received every Td. Then, conversely, the frequency of the received signal sample is 1/Td=fd.


Referring to FIG. 2, there are m computational units 20a-m within the searcher 10. At time t0, signal sample R0 is received by a receiver (not shown) located within the communication device. Signal sample R0 is then correlated with the PN sequence, P0P1 . . . PM−1. The PN sequence, P0P1 . . . PM−1, is generated by a PN sequence generator (as shown in FIG. 6) located within the communication device. Since there are M PN chips within the PN sequence, M computational units 20a-m are used to do the correlations in parallel. Hence, each computational unit 20a-m correlates the signal sample R0 with one PN chip. For example, computational unit 20a correlates R0 with P0 to generate correlation result R0P0. The collective correlation results generated by the computational units 20a-m are as follows: R0P0, R0P1, . . . , R0PM−1. The correlations are performed and the correlation results are respectively accumulated into the computational units 20a-m before the next signal sample R1 is received at time t1. The signal sample R0 may then be discarded after the correlations are performed.


At time t1, signal sample R1 is received. Signal sample R1 is then correlated with a second PN sequence, P1P2 . . . PM. The PN sequence, P1P2 . . . PM, is only a shift of the PN sequence used at time to plus a newly generated PN chip PM. That is, the start of the new PN sequence is offset by one chip from the preceding PN sequence. Consequently, the new PN sequence can be supplied to or propagated through the computational units 20a-m as follows. Except for the last computational unit 20m, each computational unit 20a-l receives its corresponding PN chip for the next correlation from its neighbor. The last computational unit 20m receives its corresponding PN chip PM from the PN sequence generator. In other words, except for the first computational unit 20a, each remaining computational unit 20b-m passes its current PN chip to its neighbor in the same direction. As to the first computational unit 20a, its current PN chip is discarded; and as to the last computational unit 20m, as mentioned above, the PN sequence generator provides the next PN chip. For example, after the correlations are completed for the received signal sample R0 (which is some time before time t1), computational unit 20a discards its current PN chip P0 and receives its next PN chip (which will be P1) from computational unit 20b; computational unit 20m passes its current PN chip PM−1 to its neighboring computational unit 201 (not shown) and receives its next PN chip PM from the PN sequence generator; and the remaining computational units 20b-l pass their current PN chips respectively to their neighbors in one direction and receive their next PN chips respectively from their neighbors in the other direction.


Again, since there are M PN chips within a PN sequence, M computational units 20a-m are used to do the correlations in parallel. This time around, the collective correlation results generated by the computational units 20a-m are as follows: R1P1, R1P2, . . . , R1PM. The correlations are performed and the results are accumulated with the correlation results that were done at time t0 before the next signal sample R2 is received at time t2. Hence, for example, before time t2, computational unit 20a contains correlation results R0P0 and R1P1. The foregoing process is repeated until the last signal sample Rn−1 is received at time tn−1 and then correlated with the PN sequence, Pn−1Pn . . . PM+n−2 generating the following collective correlation results: Rn−1Pn−1, Rn−1Pn, . . . Rn−1PM+n−2.


At the end of the time period, tn−1+Td, the correlation results for the received signal samples, R0R1 . . . Rn−1, with n different PN sequences that are offset by one chip between the start of any two adjacent PN sequences, are then obtained. For example, R0P0+R1P1+ . . . +Rn−1Pn−1 represent the correlation results accumulated at computational unit 20a. Also, at the end of the time period, tn−1+Td, M different PN code offsets have been searched. If the number of PN chips, within a PN sequence, that need to be searched is M or fewer, then the entire search process is completed at the end of the time period tn−1+Td.


If the number of PN chips, within a PN sequence, that need to be searched is more than M, then a second round of search or correlations (or additional rounds if necessary) may be performed. The length (time-wise) of a round of correlations is the time period tn−1+Td. For example, FIG. 3 illustrates this second round of correlations. Before the second round of correlations begins, the accumulated correlation results in each of the computational unit 20a-m are transferred and stored in other memory locations and then cleared. Referring to FIG. 3, in the second round of correlations, the received signal sample Rn is correlated by the computational units 20a-m with the PN sequence, Pn+MPn+M+1 . . . Pn+2M−1 at time tn. The correlation results are then accumulated at each of the computational unit 20-a-m.


At time tn+1, the signal sample Rn+1 is correlated with the next PN sequence, Pn+M+1Pn+M+2 . . . Pn+2M. Similarly, the start of this next PN sequence is offset from the preceding PN sequence by one chip and a new PN chip is added at the end. This process will continue until the second round of correlations is completed. For the second round of real-time correlations, another M PN offsets (PM, PM+1, . . . , P2M+1) are searched. The correlation results are then stored and cleared from each computational unit 20a-m before the next round of correlations starts.


According to the exemplary method shown in FIG. 2, all the received signal samples Rx are not stored first and then later used for correlation purposes. Instead, as each signal sample Rx is received, the signal sample Rx is correlated with M PN chips and then accumulated. The collective correlation results for all the received signal samples Rx are then examined to identify the PN sequence which corresponds to the strongest pilot signal. Hence, the collective correlation results for the received signal samples Rx can be derived much faster. In addition, since all the received signal samples Rx need not be stored before the correlation function is performed, the memory overhead and hardware requirements and costs correspondingly become less.


As can be seen from FIG. 2, for each time period Td, M computational units 20a-m are used to correlate a received signal sample Rx with a PN sequence which has M PN chips. For each time period Td, each computational unit 20a-m performs one correlation. As a result, with M computational units 20a-m, M correlations are collectively performed. As will be further described below, the number of computational units 20a-m which are used to perform the correlations is scalable. That is, the number of computational units 20a-m may vary depending on the amount of hardware resources available and the clock rate that is used to drive each computational unit.


Referring back to FIG. 2, for each time period Td and a PN sequence with M PN chips, each computational unit performs one correlation thereby resulting in M correlations being performed. However, each computational unit is not necessarily restricted to performing one correlation during each time period Td.


Each computational unit may perform two or more correlations per time period Td. While M correlations are to be performed per time period Td, these M correlations may be collectively performed by a fewer number of computational units. For example, referring to FIG. 4, there are M/2 computational units. In this case, each of the M/2 computational units is driven to perform two (2) correlations within the time period Td; for instance, computational unit 30a performs two (2) correlations and generates correlation results R0P0 and R0P1. In order to perform two (2) correlations with the time period Td, each computational unit is driven at a higher clock rate to increase the speed of execution.


In another example, as shown in FIG. 5, there are M/4 computational units. In this case, each of the M/4 computational units is driven to perform four (4) correlations within the time period Td; for instance, computational unit 40a performs four (4) correlations and generates correlation results R0P0, R0P1, R0P2 and R0P3. In order to perform four (4) correlations with the time period Td, each computational unit is driven at an even higher clock rate to increase the speed of execution.



FIG. 6 is a block diagram illustrating an exemplary system 100 embodiment in accordance with the present invention. As illustrated, an exemplary system 100, for implementing a system acquisition function to facilitate PN code searching, comprises: a PN sequence generator 110 configured to generate a plurality of PN sequences; and a searcher 10 having a plurality of computational units 20a-20m forming a correlator 130 and configurable to correlate a received signal sample (from receiver 120) with a PN sequence generated by the PN sequence generator, the correlations being executed in a parallel manner. As discussed above, the plurality of PN sequences are generated in a sequential manner; the plurality of PN sequences includes a first PN sequence and a second PN sequence, the second PN sequence immediately following the first PN sequence; and the start of the second PN sequence is determined by shifting the first PN sequence. In addition, a number of computational units from the plurality of computational units are selectively configured to correlate the received signal sample with the PN sequence, with the number of computational units which are selectively configured to correlate the received signal with the PN sequence depending on availability of the plurality of computational units.



FIG. 7 is a flow diagram illustrating a first exemplary method embodiment for implementing a system acquisition function to facilitate the PN code searching in accordance with the present invention. The first exemplary method begins with generating a first PN sequence, the first PN sequence being made up of a plurality of PN chips, step 205, and receiving a first signal sample, step 210. The first signal sample is correlated with the first PN sequence upon receiving the first signal sample, step 215, and a correlation result from the correlation between the first signal sample and the first PN sequence is stored, step 220. A second PN sequence is generated by shifting the first PN sequence and adding an additional PN chip, step 225, and a second signal sample is received, step 230. The second signal sample is correlated with the second PN sequence, step 235, and the methodology accumulates a correlation result from the correlation between the second signal sample and the second PN sequence with the correlation result from the correlation between the first signal sample and the first PN sequence, step 240. The method then repeats the above generating, receiving, correlating and accumulating steps with each received signal and each newly generated PN sequence, step 245.



FIG. 8 is a flow diagram illustrating a second exemplary method embodiment for implementing a system acquisition function to facilitate PN code searching in accordance with the present invention. The second exemplary method begins with maintaining a plurality of configurable computational units, step 305, and receiving a plurality of signal samples, step 310. One or more of the plurality of configurable computational units are configured to implement a PN sequence generator to generate a plurality of PN sequences, step 315. One or more of the plurality of configurable computational units are configured to implement a correlator to correlate the plurality of signal samples with the plurality of PN sequences, step 320. Each one of the plurality of signal samples is correlated with a corresponding one of the plurality of PN sequences at the time when each one of the plurality of signal samples is received, step 325. As discussed above, the number of configurable computational units used to implement the correlator depends on availability of the plurality of configurable computational units. In addition, the method may also provide for generating the plurality of PN sequences in a sequential manner, wherein the plurality of PN sequences includes a first PN sequence and second PN sequence, the second PN sequence immediately following the first PN sequence, and wherein the start of the second PN sequence is determined by shifting the first PN sequence.


Based on the disclosure provided herein, a person of ordinary skill in the art should be able to determine the appropriate number of computational units to be used to implement the PN sequence generation function and the correlation function in accordance with the present invention. The number of computational units which can be used depends on a number of factors, such as the availability of the configurable hardware resources, the incoming signal rate or, conversely, the signal period, and the available clock rates, etc. For instance, if only a limited number of computational units can be used, then the clock rate may need to be driven higher in order to perform the requisite number of correlations. Conversely, if additional hardware resources are available, additional computational units driven at a lower clock rate may be implemented to perform the same number of correlations. For another instance, if the signal period is shortened, then additional computational units may be needed to perform the requisite number of correlations within the signal period.


The present invention as described above can also be used to provide more efficient set maintenance. Signals from the base station which previously transmitted the strongest pilot signal can be searched and correlated more quickly to confirm that this base station continues to be the one transmitting the strongest pilot signal. Likewise, signals from the base stations which correspond to the candidate set and the neighbor set respectively can also be searched and correlated more quickly to update the status of the neighbor set and the neighbor set. A candidate set may be searched more frequently than a neighbor set. As a result, the set maintenance update cycle is reduced.


Moreover, while the above disclosure provided above is described in connection with a searcher 10, it should be understood that the present invention is not restricted to use with a searcher and that the present invention is applicable to and can be used with any communication devices which are capable of performing a system acquisition function.


It is understood that the present invention as described above is applicable to a CDMA communication system but that a person of ordinary skill in the art should know of other ways and/or methods to apply the present invention to other types of communication systems.


Furthermore, it is to be understood that the present invention as described above can be implemented in the form of control logic using software, hardware or a combination of both. Based on the disclosure provided herein, a person of ordinary skill in the art will know of other ways and/or methods to implement the present invention.


It is further understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference for all purposes in their entirety.

Claims
  • 1. A communications module for identifying a particular PN code among one or more PN codes embedded in a plurality of received signal samples, the communications module comprising: a PN sequence generator configured to generate a plurality of PN sequences corresponding to a plurality of PN codes;a plurality of computational units configurable to correlate the plurality of received signal samples with the plurality of PN sequences and further configurable to provide other hardware resources; andcontrol logic to selectively configure a number of computational units from the plurality of computational units to correlate the plurality of received signal samples with the plurality of PN sequences to generate correlation results for identifying the particular PN code, the number of computational units depending upon the availability of the plurality of computational units from providing the other hardware resources.
  • 2. The communications module of claim 1, wherein the plurality of received signal samples is received in a sequential manner, the plurality of PN sequences is generated in a sequential order, and the starting positions of any two adjacent PN sequences are offset by a chip.
  • 3. The communications module of claim 2, wherein the start of each successive one of the plurality of PN sequences is determined by shifting an immediately preceding one of the PN sequences.
  • 4. The communications module of claim 3, wherein each successive one of the plurality of PN sequences is generated by shifting an immediately preceding one of the PN sequences and adding an additional chip.
  • 5. The communications module of claim 2, wherein a respective one of the plurality of received signal samples is correlated with a respective one of the plurality of PN sequences as soon as the respective one of the plurality of received signal samples is received.
  • 6. The communications module of claim 5, wherein each one of the plurality of received signal samples is discarded after being correlated with the respective one of the plurality of PN sequences.
  • 7. The communications module of claim 1, wherein each of the plurality of PN sequences has M components, and wherein the number of computational units selectively configured to correlate the plurality of received signal samples with the plurality of PN sequences is M.
  • 8. The communications module of claim 1, wherein the number of computational units which are selectively configured to correlate the plurality of received signal samples with the plurality of PN sequences is capable of being reduced when a clock rate driving the plurality of computational units is increased.
  • 9. The communications module of claim 1, wherein the number of computational units which are selectively configured to correlate the plurality of received signal samples with the plurality of PN sequences is capable of being reduced when the availability of the plurality of computational units is reduced.
  • 10. The communications module of claim 1, wherein the communications module is located in a communications device.
  • 11. The communications module of claim 10, wherein the communications device is a mobile phone for use in a CDMA communication system.
  • 12. A communications device for identifying a particular PN code among one or more PN codes embedded in a plurality of received signal samples, the communications device comprising: a receiver configured to receive a plurality of signals;a plurality of computational units selectively configurable to implement a correlator to correlate the plurality of signals with a plurality of PN sequences to generate correlation results for identifying the particular PN code, and further configurable to provide other hardware resources, the plurality of PN sequences generated by a PN sequence generator and corresponding to a plurality of PN codes; andcontrol logic to selectively configure a number of computational units of the plurality of computation units to implement the correlator, the number of the computational units depending upon availability of the plurality of computational units from providing the other hardware resources.
  • 13. The communications device of claim 12, wherein the plurality of computational units are further selectively configurable to implement a PN sequence generator to generate the plurality of PN sequences.
  • 14. The communications device of claim 13, wherein the control logic is further to selectively configure a subset of the computational units of the plurality of computation units to implement the sequence generator, the subset depending upon availability of the plurality of computational units from providing the other hardware resources.
  • 15. The communications device of claim 13, wherein one or more of the number of computational units are selectively configurable to implement the other hardware resources when not needed to implement the sequence generator or the correlator.
  • 16. The communications device of claim 12, wherein: the receiver provides the plurality of signals as a plurality of received signal samples in a sequential manner;the plurality of PN sequences is generated in a sequential order; andeach of the plurality of signal samples is correlated with a respective one of the plurality of PN sequences.
  • 17. The communications device of claim 16, wherein a respective one of the plurality of received signal samples is correlated with a respective one of the plurality of PN sequences as soon as the respective one of the plurality of received signal samples is received.
  • 18. The communications device of claim 12, wherein the plurality of received signal samples is received in a sequential manner, the plurality of PN sequences is generated in a sequential order, and the starting positions of any two adjacent PN sequences are offset by a chip.
  • 19. The communications device of claim 18, wherein the start of each successive one of the plurality of PN sequences is determined by shifting an immediately preceding one of the PN sequences.
  • 20. The communications device of claim 19, wherein each successive one of the plurality of PN sequences is generated by shifting an immediately preceding one of the PN sequences and adding an additional chip.
  • 21. The communications device of claim 12, wherein each one of the plurality of received signal samples is discarded after being correlated with the respective one of the plurality of PN sequences.
  • 22. The communications device of claim 12, wherein each of the plurality of PN sequences has M chips and the number of computational units selectively configured to implement the correlator is M or smaller.
  • 23. The communications device of claim 12, wherein the number of computational units which are selectively configured to implement the correlator is capable of being reduced when a clock rate driving the plurality of computational units is increased.
  • 24. The communications device of claim 12, wherein the number of computational units which are selectively configured to implement the correlator is capable of being reduced when the availability of the plurality of computational units is reduced.
  • 25. The communications device of claim 12, wherein the communications device is a mobile phone for use in a CDMA communication system.
  • 26. A method for implementing a communications function for identifying a particular PN code among one or more PN codes embedded in a plurality of received signal samples, the method comprising: receiving the plurality of signal samples;configuring a PN sequence generator to generate a plurality of PN sequences corresponding to a plurality of PN codes;providing a plurality of computational units configurable to correlate the plurality of received signal samples with the plurality of PN sequences and further configurable to provide other hardware resources; andselectively configuring a number of computational units from the plurality of computational units to correlate the plurality of received signal samples with the plurality of PN sequences to generate correlation results for identifying the particular PN code, the number of computational units depending upon the availability of the plurality of computational units from providing the other hardware resources.
  • 27. The method of claim 26, wherein: the plurality of signals is provided as a plurality of received signal samples in a sequential manner;the plurality of PN sequences is generated in a sequential order; andthe starting positions of any two adjacent PN sequences are offset by a chip.
  • 28. The method of claim 27, further comprising determining the start of each successive one of the plurality of PN sequences by shifting an immediately preceding one of the PN sequences.
  • 29. The method of claim 27, wherein a respective one of the plurality of received signal samples is correlated with a respective one of the plurality of PN sequences as soon as the respective one of the plurality of received signal samples is received.
  • 30. The method of claim 29, further comprising discarding each one of the plurality of received signal samples after being correlated with a respective one of the plurality of PN sequences.
  • 31. The method of claim 26, wherein the correlating comprises correlating each of the plurality of signal samples with the corresponding one of the plurality of PN sequences as soon as the signal sample is received.
  • 32. The method of claim 26, further comprising generating each successive one of the plurality of PN sequences by shifting an immediately preceding one of the PN sequences and adding an additional chip.
  • 33. The method of claim 26, wherein each of the plurality of PN sequences has M chips and the number of computational units selectively configured to correlate the plurality of received signal samples with the plurality of PN sequences is M or smaller.
  • 34. The method of claim 26, further comprising increasing the clock rate driving the plurality of computational units and, once the clock rate is increased, reducing the number of computational units.
  • 35. The method of claim 26, further comprising reducing the number of computational units which are selectively configured to correlate the plurality of received signal samples with the plurality of PN sequences when the availability of the plurality of computational units is reduced.
  • 36. The method of claim 26, selectively configuring one or more of the number of computational units from the plurality of computational units for providing the other hardware resources when not needed for generating the plurality of PN sequences or for correlating the plurality of signals.
  • 37. The method of claim 26, wherein the method is implemented in a communications device.
  • 38. The method of claim 37, wherein the communications device is a mobile phone for use in a CDMA communication system.
CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 12/141,822, filed Jun. 18, 2008, which is a continuation of U.S. patent application Ser. No. 10/067,496, filed Feb. 4, 2002, now issued as U.S. Pat. No. 7,400,668 on Jul. 15, 2008, which is a continuation-in-part application of U.S. patent application Ser. No. 09/815,122, filed Mar. 22, 2001, now issued as U.S. Pat. No. 6,836,839 on Dec. 28, 2004, the disclosures of each of the aforementioned applications are hereby incorporated by reference in their entirety as if set forth in full herein for all purposes.

US Referenced Citations (535)
Number Name Date Kind
3409175 Byrne Nov 1968 A
3665171 Morrow May 1972 A
3666143 Weston May 1972 A
3938639 Birrell Feb 1976 A
3949903 Benasutti et al. Apr 1976 A
3960298 Birrell Jun 1976 A
3967062 Dobias Jun 1976 A
3991911 Shannon et al. Nov 1976 A
3995441 McMillin Dec 1976 A
4076145 Zygiel Feb 1978 A
4143793 McMillin et al. Mar 1979 A
4172669 Edelbach Oct 1979 A
4174872 Fessler Nov 1979 A
4181242 Zygiel et al. Jan 1980 A
RE30301 Zygiel Jun 1980 E
4218014 Tracy Aug 1980 A
4222972 Caldwell Sep 1980 A
4237536 Enelow et al. Dec 1980 A
4252253 Shannon Feb 1981 A
4302775 Widergren et al. Nov 1981 A
4333587 Fessler et al. Jun 1982 A
4354613 Desai et al. Oct 1982 A
4377246 McMillin et al. Mar 1983 A
4380046 Fung et al. Apr 1983 A
4393468 New Jul 1983 A
4413752 McMillin et al. Nov 1983 A
4458584 Annese et al. Jul 1984 A
4466342 Basile et al. Aug 1984 A
4475448 Shoaf et al. Oct 1984 A
4509690 Austin et al. Apr 1985 A
4520950 Jeans Jun 1985 A
4549675 Austin Oct 1985 A
4553573 McGarrah Nov 1985 A
4560089 McMillin et al. Dec 1985 A
4577782 Fessler Mar 1986 A
4578799 Scholl et al. Mar 1986 A
RE32179 Sedam et al. Jun 1986 E
4633386 Terepin et al. Dec 1986 A
4658988 Hassell Apr 1987 A
4694416 Wheeler et al. Sep 1987 A
4711374 Gaunt et al. Dec 1987 A
4713755 Worley, Jr. et al. Dec 1987 A
4719056 Scott Jan 1988 A
4726494 Scott Feb 1988 A
4747516 Baker May 1988 A
4748585 Chiarulli et al. May 1988 A
4758985 Carter Jul 1988 A
4760525 Webb Jul 1988 A
4760544 Lamb Jul 1988 A
4765513 McMillin et al. Aug 1988 A
4766548 Cedrone et al. Aug 1988 A
4781309 Vogel Nov 1988 A
4800492 Johnson et al. Jan 1989 A
4811214 Nosenchuck et al. Mar 1989 A
4824075 Holzboog Apr 1989 A
4827426 Patton et al. May 1989 A
4850269 Hancock et al. Jul 1989 A
4856684 Gerstung Aug 1989 A
4870302 Freeman Sep 1989 A
4901887 Burton Feb 1990 A
4905231 Leung et al. Feb 1990 A
4921315 Metcalfe et al. May 1990 A
4930666 Rudick Jun 1990 A
4932564 Austin et al. Jun 1990 A
4936488 Austin Jun 1990 A
4937019 Scott Jun 1990 A
4960261 Scott et al. Oct 1990 A
4961533 Teller et al. Oct 1990 A
4967340 Dawes Oct 1990 A
4974643 Bennett et al. Dec 1990 A
4982876 Scott Jan 1991 A
4993604 Gaunt et al. Feb 1991 A
5007560 Sassak Apr 1991 A
5021947 Campbell et al. Jun 1991 A
5040106 Maag Aug 1991 A
5044171 Farkas Sep 1991 A
5090015 Dabbish et al. Feb 1992 A
5099418 Pian et al. Mar 1992 A
5129549 Austin Jul 1992 A
5139708 Scott Aug 1992 A
5144166 Camarota et al. Sep 1992 A
5156301 Hassell et al. Oct 1992 A
5156871 Goulet et al. Oct 1992 A
5165023 Gifford Nov 1992 A
5165575 Scott Nov 1992 A
5177700 Göckler Jan 1993 A
5190083 Gupta et al. Mar 1993 A
5190189 Zimmer et al. Mar 1993 A
5193151 Jain Mar 1993 A
5193718 Hassell et al. Mar 1993 A
5202993 Tarsy et al. Apr 1993 A
5203474 Haynes Apr 1993 A
5218240 Camarota et al. Jun 1993 A
5240144 Feldman Aug 1993 A
5245227 Furtek et al. Sep 1993 A
5261099 Bigo et al. Nov 1993 A
5263509 Cherry et al. Nov 1993 A
5269442 Vogel Dec 1993 A
5280711 Motta et al. Jan 1994 A
5297400 Benton et al. Mar 1994 A
5301100 Wagner Apr 1994 A
5303846 Shannon Apr 1994 A
5335276 Thompson et al. Aug 1994 A
5336950 Popli et al. Aug 1994 A
5339428 Burmeister et al. Aug 1994 A
5343716 Swanson et al. Sep 1994 A
5361362 Benkeser et al. Nov 1994 A
5367651 Smith et al. Nov 1994 A
5367687 Tarsy et al. Nov 1994 A
5368198 Goulet Nov 1994 A
5379343 Grube et al. Jan 1995 A
5381546 Servi et al. Jan 1995 A
5381550 Jourdenais et al. Jan 1995 A
5388062 Knutson Feb 1995 A
5388212 Grube et al. Feb 1995 A
5392960 Kendt et al. Feb 1995 A
5437395 Bull et al. Aug 1995 A
5450557 Kopp et al. Sep 1995 A
5454406 Rejret et al. Oct 1995 A
5465368 Davidson et al. Nov 1995 A
5475856 Kogge Dec 1995 A
5479055 Eccles Dec 1995 A
5490165 Blakeney, II et al. Feb 1996 A
5491823 Ruttenberg Feb 1996 A
5504891 Motoyama et al. Apr 1996 A
5507009 Grube et al. Apr 1996 A
5515519 Yoshioka et al. May 1996 A
5517600 Shimokawa May 1996 A
5519694 Brewer et al. May 1996 A
5522070 Sumimoto May 1996 A
5530964 Alpert et al. Jun 1996 A
5534796 Edwards Jul 1996 A
5542265 Rutland Aug 1996 A
5553755 Bonewald et al. Sep 1996 A
5555417 Odnert et al. Sep 1996 A
5560028 Sachs et al. Sep 1996 A
5560038 Haddock Sep 1996 A
5570587 Kim Nov 1996 A
5572572 Kawan et al. Nov 1996 A
5590353 Sakakibara et al. Dec 1996 A
5594657 Cantone et al. Jan 1997 A
5600810 Ohkami Feb 1997 A
5600844 Shaw et al. Feb 1997 A
5602833 Zehavi Feb 1997 A
5603043 Taylor et al. Feb 1997 A
5607083 Vogel et al. Mar 1997 A
5608643 Wichter et al. Mar 1997 A
5611867 Cooper et al. Mar 1997 A
5623545 Childs et al. Apr 1997 A
5625669 McGregor et al. Apr 1997 A
5626407 Westcott May 1997 A
5630206 Urban et al. May 1997 A
5635940 Hickman et al. Jun 1997 A
5646544 Iadanza Jul 1997 A
5646545 Trimberger et al. Jul 1997 A
5647512 Assis Mascarenhas deOliveira et al. Jul 1997 A
5667110 McCann et al. Sep 1997 A
5684793 Kiema et al. Nov 1997 A
5684980 Casselman Nov 1997 A
5687236 Moskowitz et al. Nov 1997 A
5694613 Suzuki Dec 1997 A
5694794 Jerg et al. Dec 1997 A
5699328 Ishizaki et al. Dec 1997 A
5701398 Glier et al. Dec 1997 A
5701482 Harrison et al. Dec 1997 A
5704053 Santhanam Dec 1997 A
5706191 Bassett et al. Jan 1998 A
5706976 Purkey Jan 1998 A
5712996 Schepers Jan 1998 A
5720002 Wang Feb 1998 A
5721693 Song Feb 1998 A
5721854 Ebicioglu et al. Feb 1998 A
5729754 Estes Mar 1998 A
5732563 Bethuy et al. Mar 1998 A
5734808 Takeda Mar 1998 A
5737631 Trimberger Apr 1998 A
5742180 DeHon et al. Apr 1998 A
5742821 Prasanna Apr 1998 A
5745366 Higham et al. Apr 1998 A
RE35780 Hassell et al. May 1998 E
5751295 Becklund et al. May 1998 A
5754227 Fukuoka May 1998 A
5758261 Wiedeman May 1998 A
5768561 Wise Jun 1998 A
5778439 Trimberger et al. Jul 1998 A
5784636 Rupp Jul 1998 A
5787237 Reilly Jul 1998 A
5790817 Asghar et al. Aug 1998 A
5791517 Avital Aug 1998 A
5791523 Oh Aug 1998 A
5794062 Baxter Aug 1998 A
5794067 Kadowaki Aug 1998 A
5802055 Krein et al. Sep 1998 A
5812851 Levy et al. Sep 1998 A
5818603 Motoyama Oct 1998 A
5819255 Celis et al. Oct 1998 A
5822308 Weigand et al. Oct 1998 A
5822313 Malek et al. Oct 1998 A
5822360 Lee et al. Oct 1998 A
5828858 Athanas et al. Oct 1998 A
5829085 Jerg et al. Nov 1998 A
5835753 Witt Nov 1998 A
5838165 Chatter Nov 1998 A
5845815 Vogel Dec 1998 A
5854929 Van Pract et al. Dec 1998 A
5860021 Klingman Jan 1999 A
5862961 Motta et al. Jan 1999 A
5870427 Tiedemann, Jr. et al. Feb 1999 A
5873045 Lee et al. Feb 1999 A
5881106 Cartier Mar 1999 A
5884284 Peters et al. Mar 1999 A
5886537 Macias et al. Mar 1999 A
5887174 Simons et al. Mar 1999 A
5889816 Agrawal et al. Mar 1999 A
5889989 Robertazzi et al. Mar 1999 A
5890014 Long Mar 1999 A
5892900 Ginter et al. Apr 1999 A
5892950 Rigori et al. Apr 1999 A
5892961 Trimberger Apr 1999 A
5892962 Cloutier Apr 1999 A
5894473 Dent Apr 1999 A
5901884 Goulet et al. May 1999 A
5903886 Heimlich et al. May 1999 A
5907285 Toms et al. May 1999 A
5907580 Cummings May 1999 A
5910733 Bertolet et al. Jun 1999 A
5912572 Graf, III Jun 1999 A
5913172 McCabe et al. Jun 1999 A
5917852 Butterfield et al. Jun 1999 A
5920801 Thomas et al. Jul 1999 A
5931918 Row et al. Aug 1999 A
5933642 Greenbaum et al. Aug 1999 A
5940438 Poon et al. Aug 1999 A
5949415 Lin et al. Sep 1999 A
5950011 Albrecht et al. Sep 1999 A
5950131 Vilmur Sep 1999 A
5951674 Moreno Sep 1999 A
5953322 Kimball Sep 1999 A
5956518 DeHon et al. Sep 1999 A
5956967 Kim Sep 1999 A
5959811 Richardson Sep 1999 A
5959881 Trimberger et al. Sep 1999 A
5963048 Harrison et al. Oct 1999 A
5966534 Cooke et al. Oct 1999 A
5970254 Cooke et al. Oct 1999 A
5987105 Jenkins et al. Nov 1999 A
5987611 Freund Nov 1999 A
5991302 Berl et al. Nov 1999 A
5991308 Fuhrmann et al. Nov 1999 A
5993739 Lyon Nov 1999 A
5999734 Willis et al. Dec 1999 A
6005943 Cohen et al. Dec 1999 A
6006249 Leong Dec 1999 A
6016395 Mohamed Jan 2000 A
6021186 Suzuki et al. Feb 2000 A
6021492 May Feb 2000 A
6023742 Ebeling et al. Feb 2000 A
6023755 Casselman Feb 2000 A
6028610 Deering Feb 2000 A
6036166 Olson Mar 2000 A
6039219 Bach et al. Mar 2000 A
6041322 Meng et al. Mar 2000 A
6041970 Vogel Mar 2000 A
6046603 New Apr 2000 A
6047115 Mohan et al. Apr 2000 A
6052600 Fette et al. Apr 2000 A
6055314 Spies et al. Apr 2000 A
6056194 Kolls May 2000 A
6059840 Click, Jr. May 2000 A
6061580 Altschul et al. May 2000 A
6073132 Gehman Jun 2000 A
6076174 Freund Jun 2000 A
6078736 Guccione Jun 2000 A
6085740 Ivri et al. Jul 2000 A
6088043 Kelleher et al. Jul 2000 A
6091263 New et al. Jul 2000 A
6091765 Pietzold, III et al. Jul 2000 A
6094065 Tavana et al. Jul 2000 A
6094726 Gonion et al. Jul 2000 A
6111893 Volftsun et al. Aug 2000 A
6111935 Hughes-Hartogs Aug 2000 A
6112218 Gandhi et al. Aug 2000 A
6115751 Tam et al. Sep 2000 A
6119178 Martin et al. Sep 2000 A
6120551 Law et al. Sep 2000 A
6122670 Bennett et al. Sep 2000 A
6128307 Brown Oct 2000 A
6134605 Hudson et al. Oct 2000 A
6138693 Matz Oct 2000 A
6141283 Bogin et al. Oct 2000 A
6150838 Wittig et al. Nov 2000 A
6154494 Sugahara et al. Nov 2000 A
6157997 Oowaki et al. Dec 2000 A
6158031 Mack et al. Dec 2000 A
6173389 Pechanek et al. Jan 2001 B1
6175854 Bretscher Jan 2001 B1
6175892 Sazzad et al. Jan 2001 B1
6181981 Varga et al. Jan 2001 B1
6185418 MacLellan et al. Feb 2001 B1
6192070 Poon et al. Feb 2001 B1
6192255 Lewis et al. Feb 2001 B1
6192388 Cajolet Feb 2001 B1
6195788 Leaver et al. Feb 2001 B1
6198924 Ishii et al. Mar 2001 B1
6199181 Rechef et al. Mar 2001 B1
6202130 Scales, III et al. Mar 2001 B1
6202189 Hinedi et al. Mar 2001 B1
6219697 Lawande et al. Apr 2001 B1
6219756 Kasamizugami Apr 2001 B1
6219780 Lipasti Apr 2001 B1
6223222 Fijolek et al. Apr 2001 B1
6226387 Tewfik et al. May 2001 B1
6230307 Davis et al. May 2001 B1
6237029 Master et al. May 2001 B1
6246883 Lee Jun 2001 B1
6247125 Noel-Baron et al. Jun 2001 B1
6249251 Chang et al. Jun 2001 B1
6258725 Lee et al. Jul 2001 B1
6259725 Schilling Jul 2001 B1
6263057 Silverman Jul 2001 B1
6266760 DeHon et al. Jul 2001 B1
6269075 Tran Jul 2001 B1
6272579 Lentz et al. Aug 2001 B1
6272616 Fernando et al. Aug 2001 B1
6279020 Dujardin et al. Aug 2001 B1
6281703 Furuta et al. Aug 2001 B1
6282627 Wong et al. Aug 2001 B1
6286134 Click, Jr. et al. Sep 2001 B1
6289375 Knight et al. Sep 2001 B1
6289434 Roy Sep 2001 B1
6289488 Dave et al. Sep 2001 B1
6292822 Hardwick Sep 2001 B1
6292827 Raz Sep 2001 B1
6292830 Taylor et al. Sep 2001 B1
6292938 Sarkar et al. Sep 2001 B1
6301653 Mohamed et al. Oct 2001 B1
6305014 Roediger et al. Oct 2001 B1
6311149 Ryan et al. Oct 2001 B1
6321985 Kolls Nov 2001 B1
6326806 Fallside et al. Dec 2001 B1
6346824 New Feb 2002 B1
6347346 Taylor Feb 2002 B1
6349394 Brock et al. Feb 2002 B1
6353841 Marshall et al. Mar 2002 B1
6356994 Barry et al. Mar 2002 B1
6359248 Mardi Mar 2002 B1
6360256 Lim Mar 2002 B1
6360259 Bradley Mar 2002 B1
6360263 Kurtzberg et al. Mar 2002 B1
6363411 Dugan et al. Mar 2002 B1
6366999 Drabenstott et al. Apr 2002 B1
6377983 Cohen et al. Apr 2002 B1
6378072 Collins et al. Apr 2002 B1
6381293 Lee et al. Apr 2002 B1
6381735 Hunt Apr 2002 B1
6385751 Wolf May 2002 B1
6405214 Meade, II Jun 2002 B1
6408039 Ito Jun 2002 B1
6410941 Taylor et al. Jun 2002 B1
6411612 Halford et al. Jun 2002 B1
6421372 Bierly et al. Jul 2002 B1
6421809 Wuytack et al. Jul 2002 B1
6426649 Fu et al. Jul 2002 B1
6430624 Jamtgaard et al. Aug 2002 B1
6433578 Wasson Aug 2002 B1
6434590 Blelloch et al. Aug 2002 B1
6438737 Morelli et al. Aug 2002 B1
6446258 McKinsey et al. Sep 2002 B1
6449747 Wuytack et al. Sep 2002 B2
6456996 Crawford, Jr. et al. Sep 2002 B1
6459883 Subramanian et al. Oct 2002 B2
6467009 Winegarden et al. Oct 2002 B1
6469540 Nakaya Oct 2002 B2
6473609 Schwartz et al. Oct 2002 B1
6483343 Faith et al. Nov 2002 B1
6507947 Schreiber et al. Jan 2003 B1
6510138 Pannell Jan 2003 B1
6510510 Garde Jan 2003 B1
6526570 Click, Jr. et al. Feb 2003 B1
6538470 Langhammer et al. Mar 2003 B1
6556044 Langhammer et al. Apr 2003 B2
6563891 Eriksson et al. May 2003 B1
6570877 Kloth et al. May 2003 B1
6577678 Scheuermann Jun 2003 B2
6587684 Hsu et al. Jul 2003 B1
6590415 Agrawal et al. Jul 2003 B2
6601086 Howard et al. Jul 2003 B1
6601158 Abbott et al. Jul 2003 B1
6604085 Kolls Aug 2003 B1
6604189 Zemlyak et al. Aug 2003 B1
6606529 Crowder, Jr. et al. Aug 2003 B1
6615333 Hoogerbrugge et al. Sep 2003 B1
6618434 Heidari-Bateni et al. Sep 2003 B2
6640304 Ginter et al. Oct 2003 B2
6647429 Semal Nov 2003 B1
6653859 Sihlbom et al. Nov 2003 B2
6675265 Barroso et al. Jan 2004 B2
6675284 Warren Jan 2004 B1
6691148 Zinky et al. Feb 2004 B1
6694380 Wolrich et al. Feb 2004 B1
6711617 Bantz et al. Mar 2004 B1
6718182 Kung Apr 2004 B1
6718541 Ostanevich et al. Apr 2004 B2
6721286 Williams et al. Apr 2004 B1
6721884 De Oliveira Kastrup Pereira et al. Apr 2004 B1
6732354 Ebeling et al. May 2004 B2
6735621 Yoakum et al. May 2004 B1
6738744 Kirovski et al. May 2004 B2
6748360 Pitman et al. Jun 2004 B2
6751723 Kundu et al. Jun 2004 B1
6754470 Hendrickson et al. Jun 2004 B2
6760587 Holtzman et al. Jul 2004 B2
6760833 Dowling Jul 2004 B1
6766165 Sharma et al. Jul 2004 B2
6778212 Deng et al. Aug 2004 B1
6785341 Walton et al. Aug 2004 B2
6819140 Yamanaka et al. Nov 2004 B2
6823448 Roth et al. Nov 2004 B2
6829633 Gelfer et al. Dec 2004 B2
6832250 Coons et al. Dec 2004 B1
6836839 Master et al. Dec 2004 B2
6854002 Conway et al. Feb 2005 B2
6859434 Segal et al. Feb 2005 B2
6865664 Budrovic et al. Mar 2005 B2
6871236 Fishman et al. Mar 2005 B2
6883084 Donohoe Apr 2005 B1
6894996 Lee May 2005 B2
6901440 Bimm et al. May 2005 B1
6912515 Jackson et al. Jun 2005 B2
6941336 Mar Sep 2005 B1
6980515 Schunk et al. Dec 2005 B1
6985517 Matsumoto et al. Jan 2006 B2
6986021 Master et al. Jan 2006 B2
6986142 Ehlig et al. Jan 2006 B1
6988139 Jervis et al. Jan 2006 B1
7032229 Flores et al. Apr 2006 B1
7044741 Leem May 2006 B2
7082456 Mani-Meitav et al. Jul 2006 B2
7139910 Ainsworth et al. Nov 2006 B1
7142731 Toi Nov 2006 B1
7249242 Ramchandran Jul 2007 B2
7400668 Heidari et al. Jul 2008 B2
20010003191 Kovacs et al. Jun 2001 A1
20010023482 Wray Sep 2001 A1
20010029515 Mirsky Oct 2001 A1
20010034227 Subramanian et al. Oct 2001 A1
20010034795 Moulton et al. Oct 2001 A1
20010039654 Miyamoto Nov 2001 A1
20010048713 Medlock et al. Dec 2001 A1
20010048714 Jha Dec 2001 A1
20010050948 Ramberg et al. Dec 2001 A1
20020010848 Kamano et al. Jan 2002 A1
20020013799 Blaker Jan 2002 A1
20020013937 Ostanevich et al. Jan 2002 A1
20020015435 Rieken Feb 2002 A1
20020015439 Kohli et al. Feb 2002 A1
20020023210 Tuomenoksa et al. Feb 2002 A1
20020024942 Tsuneki et al. Feb 2002 A1
20020024993 Subramanian et al. Feb 2002 A1
20020031166 Subramanian et al. Mar 2002 A1
20020032551 Zakiya Mar 2002 A1
20020035623 Lawande et al. Mar 2002 A1
20020041581 Aramaki Apr 2002 A1
20020042875 Shukla Apr 2002 A1
20020042907 Yamanaka et al. Apr 2002 A1
20020061741 Leung et al. May 2002 A1
20020069282 Reisman Jun 2002 A1
20020072830 Hunt Jun 2002 A1
20020078337 Moreau et al. Jun 2002 A1
20020083305 Renard et al. Jun 2002 A1
20020083423 Ostanevich et al. Jun 2002 A1
20020087829 Snyder et al. Jul 2002 A1
20020089348 Langhammer Jul 2002 A1
20020101909 Chen et al. Aug 2002 A1
20020107905 Roe et al. Aug 2002 A1
20020107962 Richter et al. Aug 2002 A1
20020119803 Bitterlich et al. Aug 2002 A1
20020120672 Butt et al. Aug 2002 A1
20020133688 Lee et al. Sep 2002 A1
20020138716 Master et al. Sep 2002 A1
20020141489 Imaizumi Oct 2002 A1
20020147845 Sanchez-Herrero et al. Oct 2002 A1
20020159503 Ramachandran Oct 2002 A1
20020162026 Neuman et al. Oct 2002 A1
20020167997 Kim et al. Nov 2002 A1
20020168018 Scheuermann Nov 2002 A1
20020181559 Heidari-Bateni et al. Dec 2002 A1
20020184275 Dutta et al. Dec 2002 A1
20020184291 Hogenauer Dec 2002 A1
20020184498 Qi Dec 2002 A1
20020191790 Anand et al. Dec 2002 A1
20030007606 Suder et al. Jan 2003 A1
20030012270 Zhou et al. Jan 2003 A1
20030018446 Makowski et al. Jan 2003 A1
20030018700 Giroti et al. Jan 2003 A1
20030023649 Kamiya et al. Jan 2003 A1
20030023830 Hogenauer Jan 2003 A1
20030026242 Jokinen et al. Feb 2003 A1
20030030004 Dixon et al. Feb 2003 A1
20030046421 Horvitz et al. Mar 2003 A1
20030050055 Ting et al. Mar 2003 A1
20030061260 Rajkumar Mar 2003 A1
20030061311 Lo Mar 2003 A1
20030063656 Rao et al. Apr 2003 A1
20030074473 Pham et al. Apr 2003 A1
20030076815 Miller et al. Apr 2003 A1
20030099223 Chang et al. May 2003 A1
20030102889 Master et al. Jun 2003 A1
20030105949 Master et al. Jun 2003 A1
20030110485 Lu et al. Jun 2003 A1
20030142818 Raghunathan et al. Jul 2003 A1
20030154357 Master et al. Aug 2003 A1
20030163723 Kozuch et al. Aug 2003 A1
20030172138 McCormack et al. Sep 2003 A1
20030172139 Srinivasan et al. Sep 2003 A1
20030200538 Ebeling et al. Oct 2003 A1
20030212684 Meyer et al. Nov 2003 A1
20030229864 Watkins Dec 2003 A1
20040006584 Vandeweerd Jan 2004 A1
20040010645 Scheuermann et al. Jan 2004 A1
20040015970 Scheuermann Jan 2004 A1
20040025159 Scheuermann et al. Feb 2004 A1
20040057505 Valio Mar 2004 A1
20040062300 McDonough et al. Apr 2004 A1
20040081248 Parolari Apr 2004 A1
20040086027 Shattil May 2004 A1
20040093479 Ramchandran May 2004 A1
20040168044 Ramchandran Aug 2004 A1
20040174932 Warke et al. Sep 2004 A1
20050044344 Stevens Feb 2005 A1
20050166038 Wang et al. Jul 2005 A1
20050190871 Sedarat Sep 2005 A1
20050198199 Dowling Sep 2005 A1
20060031660 Master et al. Feb 2006 A1
20060056496 Smee et al. Mar 2006 A1
Foreign Referenced Citations (52)
Number Date Country
100 18 374 Oct 2001 DE
0 301 169 Feb 1989 EP
0 166 586 Jan 1991 EP
0 236 633 May 1991 EP
0 478 624 Apr 1992 EP
0 479 102 Apr 1992 EP
0 661 831 Jul 1995 EP
0 668 659 Aug 1995 EP
0 690 588 Jan 1996 EP
0 691 754 Jan 1996 EP
0 768 602 Apr 1997 EP
0 817 003 Jan 1998 EP
0 821 495 Jan 1998 EP
0 866 210 Sep 1998 EP
0 923 247 Jun 1999 EP
0 926 596 Jun 1999 EP
1 056 217 Nov 2000 EP
1 061 437 Dec 2000 EP
1 061 443 Dec 2000 EP
1 126 368 Aug 2001 EP
1 150 506 Oct 2001 EP
1 189 358 Mar 2002 EP
2 067 800 Jul 1981 GB
2 237 908 May 1991 GB
62-249456 Oct 1987 JP
63-147258 Jun 1988 JP
4-51546 Feb 1992 JP
7-064789 Mar 1995 JP
7066718 Mar 1995 JP
10233676 Sep 1998 JP
10254696 Sep 1998 JP
11296345 Oct 1999 JP
2000315731 Nov 2000 JP
2001-053703 Feb 2001 JP
WO 8905029 Jun 1989 WO
WO 8911443 Nov 1989 WO
WO 9100238 Jan 1991 WO
WO 9313603 Jul 1993 WO
WO 9511855 May 1995 WO
WO 9633558 Oct 1996 WO
WO 9832071 Jul 1998 WO
WO 9903776 Jan 1999 WO
WO 9921094 Apr 1999 WO
WO 9926860 Jun 1999 WO
WO 9965818 Dec 1999 WO
WO 0019311 Apr 2000 WO
WO 0065855 Nov 2000 WO
WO 0069073 Nov 2000 WO
WO 0111281 Feb 2001 WO
WO 0122235 Mar 2001 WO
WO 0176129 Oct 2001 WO
WO 0212978 Feb 2002 WO
Related Publications (1)
Number Date Country
20090103594 A1 Apr 2009 US
Continuations (2)
Number Date Country
Parent 12141822 Jun 2008 US
Child 12343333 US
Parent 10067496 Feb 2002 US
Child 12141822 US
Continuation in Parts (1)
Number Date Country
Parent 09815122 Mar 2001 US
Child 10067496 US