Co-channel interference receiver Number:7,092,452 from the United States Patent and Trademark Office (PTO) owispatent

Title: Co-channel interference receiver

Abstract: A digital receiver automatically detects and non-coherently demodulates a multiplicity of interfering digitally modulated signals transmitted simultaneously at approximately the same carrier frequency. The receiver includes one or more antenna inputs (e.g., polarization and/or space diverse), a parameter estimator module, and a multiuser detector for estimating the data transmitted by each interfering signals and adapted to operate with at least one of a MUD algorithm with partially quantized prior information and a MUD algorithm based on prewhitened data.

Patent Number: 7,092,452 Issued on 08/15/2006 to Taylor,   et al.

---

Inventors:	Taylor; Matthew A (Weare, NH), MacLeod; Robert B (Nashua, NH), Learned; Rachel E (Waltham, MA), Niedzwiecki; Joshua D (Manchester, NH), Brommer; Karl D (Hampton Falls, NH), McElwain; Thomas P (Merrimack, NH)
Assignee:	Bae Systems Information and Electronic Systems Integration INC (Nashua, NH)
Appl. No.:	10/423,740
Filed:	April 25, 2003

---

Related U.S. Patent Documents

---

	Application Number	Filing Date	Patent Number	Issue Date
	10228787	Aug., 2002	6947502
	10105918	Mar., 2002
	60398451	Jul., 2002
	60372956	Apr., 2002

---

Current U.S. Class:	375/267 ; 375/341; 375/343; 375/347; 375/350
Current International Class:	H04B 7/02 (20060101)
Field of Search:	375/260,262,267,340,341,346,347,348,349,350,343

---

References Cited [Referenced By]

---

U.S. Patent Documents

	5371471		December 1994		Chennakeshu et al.
	5506861		April 1996		Bottomley
	5569439		October 1996		Cardini et al.
	5790606		August 1998		Dent
	5867478		February 1999		Baum et al.
	5982813		November 1999		Dutta et al.
	6122269		September 2000		Wales
	6144710		November 2000		Chen et al.
	6172970		January 2001		Ling et al.
	6198775		March 2001		Khayrallah et al.
	6219341		April 2001		Varanasi
	6377611		April 2002		Hwang
	6466566		October 2002		De Gaudenzi et al.
	2002/0037061		March 2002		Learned
	2002/0037737		March 2002		Learned et al.

Other References

Wang, Xiaodong, Interative (Turbo) Soft Interference Cancellation and Decoding for Coded CDMA, IEEE Transactions on Communications, Jul. 1999, pp. 1046-1061, vol. 47, No. 7. cited by other . Fu, Hongyi, Blind Channel and Carrier Offset Estimation in Multiuser CDMA, IEEE, 2002, pp. 21-25. cited by other . Alexander, Paul, D., Interative Multiuser Interference Reduction: Turbo CDMA, IEEE Transactions on Communications, Jul. 1999, pp. 1008-1014, vol. 47, No. 7. cited by other . Verdu, Sergio, Minimum Probability of Error for Asynchronous Gaussian Multiple-Access Channels, IEEE Transactions on Information Theory, Jan. 1986, pp. 85-96 vol. IT-32, No. 1. cited by other . Lupas, Ruxandra, Linear Multiuser Detectors for Synchronous Code-Divison Multiple-Access Channels, IEEE Transactions on Information Theory, Jan. 1989, pp. 123-136, vol. 35, No. 1. cited by other . Lupas, Ruxandra, Near-Far Resistance of Multiuser Detectors in Asynchronous Channels, IEEE Transactions on Communications, Apr. 1990, pp. 496-508, vol. 38, No. 4. cited by other . Varanasi, Mahesh, K., Near-Optimum Detection Synchronous code-Division Multiple-Access Systems, IEEE Transactions on Communications, May 1991, pp. 725-736, vol. 39, No. 5. cited by other . Duel-Hallen, Alexandra, Decorrelating Decision-Feedback Multiuser Detector for Synchronous Code-Division Multi-Access Channel, IEEE Transactions on Communications, Feb. 1993, pp. 285-290, vol. 41, No. 2. cited by other . Wei, Lei, Synchronous DS-SSMA System with Improved Decorrelating Decision-Feedback Multiuser Detection, IEEE Transactions on Vehicular Technology, Aug. 1994, pp. 767-772, vol. 43, No. 3. cited by other . PCT International Search Report dated Jul. 11, 2003 of International Application No. PCT/US03/12917 filed Apr. 25, 2003. cited by other.

Primary Examiner: Vo; Don N.
Attorney, Agent or Firm: Maine & Asmus

---

Government Interests

---

STATEMENT OF GOVERNMENT INTEREST

The present invention was made with United States Government support under a United States Government Contract. The United States Government has certain rights in this invention.

---

Parent Case Text

---

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/398,451, filed Jul. 24, 2002. This application is a continuation-in-part of U.S. application Ser. No. 10/228,787 filed Aug. 26, 2002, which claims priority to U.S. Provisional Application No. 60/372,956, filed Apr. 16, 2002. This application is also a continuation-in-part of U.S. application Ser. No. 10/105,918, filed Mar. 25, 2002. This application is related to U.S. application Ser. No. 10/423,695, filed Apr. 25, 2003, This application is related to U.S. application Ser. No. 10/423,655, filed Apr. 25, 2003. Each of these applications is herein incorporated in its entirety by reference.

---

Claims

---

What is claimed is:

1. A co-channel interference receiver comprising: a multiuser detector module adapted to receive a complex signal that contains information from K co-channel interfering signals in the same frequency and time space, and configured to operate with at least one of a low complexity linear MMSE algorithm with partially quantized prior information and a low complexity M-algorithm based on prewhitened data; and a parameter estimation module adapted to receive the complex signal, and to generate estimated signature waveforms of each of the K co-channel interfering signals; wherein the estimated signature waveforms are provided to the multiuser detector thereby enabling demodulation of the K co-channel interfering signals.

2. The receiver of claim 1 wherein the multiuser detector module and the parameter estimation module are each adapted to receive a plurality of complex signals.

3. The receiver of claim 1 further comprising: an analog front end operatively coupled to the multiuser detector module and the parameter estimation module, the analog front end adapted to receive one or more composite waveform signals each from a plurality of transmitters, and to convert each received composite waveform signal to a corresponding complex signal.

4. The receiver of claim 3 wherein the analog front end includes: one or more analog to digital converters, each adapted to convert a received composite waveform to a digital waveform; and one or more downconverters, each operatively coupled to a respective analog to digital converter, and adapted to translate frequency associated with a received composite waveform to a lower frequency.

5. The receiver of claim 3 wherein the analog front end includes: one or more antennas each configured to receive a corresponding composite waveform signal from the plurality of transmitters.

6. The receiver of claim 5 wherein the one or more antennas is a singly polarized antenna.

7. The receiver of claim 5 wherein the one or more antennas is a dual polarized antenna adapted with two polarization ports, thereby providing polarization diversity.

8. The receiver of claim 5 wherein the one or more antennas include two or more dual polarized antennas, each adapted with two polarization ports, thereby providing space and polarization diversity.

9. The receiver of claim 1 wherein the multiuser detector module is configured to operate with at least the low complexity linear MMSE algorithm with partially quantized prior information, and comprises: a turbo MUD adapted to provide estimates of individual bits for each of the K co-channel interfering signals, wherein the estimates are iteratively applied in a feedback loop that includes an error correction module until an error rate associated with the bits drops below a predetermined figure; a combiner module operatively coupled to the turbo MUD, and adapted to combine recomputed bit estimates output by the turbo MUD with quantized bit values on a next iteration; and a thresholding module coupled to the output of the error correction module, and adapted to assign a quantized value for each bit estimate above a predetermined threshold, and to pass through those quantized bit values to the combiner module, thereby enabling partially quantized prior information.

10. The receiver of claim 9 wherein the error correction module on each subsequent iteration processes a combination of recomputed bit estimates output by the turbo MUD and quantized bit values output by the thresholding module, and provides its output back to the turbo MUD through the thresholding module, thereby reducing the number of uncertain bit estimates with every iteration.

11. The receiver of claim 1 wherein the multiuser detector module is configured to operate with at least the low complexity M-algorithm based on prewhitened data, and comprises: a matched filter adapted to prewhiten complex signals received by the receiver, thereby partially decoupling users from multiple access interference; a whitener designer module operatively coupled to the parameter estimator, and adapted to develop a model of each received complex signal based on parameter estimates from the parameter estimator, and to compute an asynchronous whitener module that whitens filtered data output by the matched filter; and a symbol hypothesis testing module operatively coupled to the whitener designer module, and configured to receive whitened data output by the asynchronous whitener module, the symbol hypothesis testing module adapted to conduct symbol hypothesis testing based on sequential evaluation of metric characterizing likelihood of hypotheses.

12. The receiver of claim 11 wherein the whitener designer module utilizes a correlation matrix provided by the parameter estimation module to compute a diagonally loaded Cholesky Factorization, which is used for whitening in the whitening module, and is also used in hypothesis testing in the symbol hypothesis testing module.

13. The receiver of claim 11 wherein the whitener designer module employs a QR factorization using Householder transformations.

14. The receiver of claim 11 wherein the whitener designer module employs Hyperbolic Householder transformations to efficiently update the asynchronous whitener module when only received energies and/or phases change between symbol periods.

15. The receiver of claim 11 wherein the whitener designer module employs a square-root factorization.

16. The receiver of claim 11 wherein the asynchronous whitener module employs a correlation matrix square-root factorization produced by the whitener designer module to whiten data.

17. The receiver of claim 16 wherein the asynchronous whitener module employs a bank of filters defined by the inverse of the conjugate transpose of the correlation matrix square-root factorization.

18. The receiver of claim 16 wherein the correlation matrix square-root factorization has a triangular structure, and the asynchronous whitener module employs back-substitution.

19. The receiver of claim 11 wherein the symbol hypothesis testing module employs a correlation matrix square-root factorization produced by the whitener designer module in metrics to sequentially evaluate the bit hypotheses in a decision tree which is implemented using breadth-first techniques.

20. The receiver of claim 11 wherein parameter estimates of the parameter estimation module are used to model a channel associated with each received complex signal, thereby enabling application of the matched filter and development of an asynchronous decorrelating filter bank.

21. The receiver of claim 1 wherein the parameter estimation module is adapted to estimate signal parameters including at least one of timing, signal amplitudes, phases, polarizations, and identify of active channels.

22. The receiver of claim 1 wherein the parameter estimation module comprises: a training sequence locator module adapted to estimate a training sequence location index in each frame of the received complex signal; a noise estimator module adapted to calculate an estimate of an average noise power in the received complex signal in accordance with the training sequence location index; a signature waveform estimator module adapted to estimate signature waveforms unique to each user in the received complex signal in accordance with the training sequence location index and a transformation matrix; an active user tester module operatively coupled to an output of the noise estimator module and to an output of the signature waveform estimator module, the active user tester module adapted to determine a number of active users associated with the received complex signal; and a transformation matrix rebuilder module operatively coupled to the active user tester module and to pre-stored known training sequences for each user, the transformation matrix rebuilder module adapted to generate the transformation matrix used by the signature waveform estimating module.

23. A co-channel interference receiver comprising: a multiuser detector module adapted to receive a complex signal that contains information from K co-channel interfering signals, and configured to operates with at least one of a low complexity linear MMSE algorithm with partially quantized prior information and an algorithm based on prewhitened data, wherein said algorithm based on prewhitened data is selected from at least one of an M-algorithm and a T-algorithm; and a parameter estimation module adapted to receive the complex signal, and to generate estimated signature waveforms of each of the K co-channel interfering signals, wherein the estimated signature waveforms are provided to the multiuser detector thereby enabling demodulation of the K co-channel interfering signals.

24. A method for receiving a complex signal that contains information from K co-channel interfering signals, the method comprising: estimating signature waveforms of each of the K co-channel interfering signals; and processing the complex signal based on the signature waveforms with at least one of: a low complexity linear MMSE algorithm with partially quantized prior information, wherein the low complexity linear MMSE algorithm includes eliminating from each processing iteration consideration of those bits having an estimate value that exceeds a predetermined threshold, and wherein bit estimates exceeding the threshold are considered certain; and a low complexity M-algorithm based on prewhitened data.

25. The method of claim 24 wherein the low complexity M-algorithm algorithm based on prewhitened data includes: filtering the complex signal, thereby partially decoupling users from multiple access interference and providing prewhitened data; developing a model of the received complex signal based on parameter estimates; computing an asynchronous whitener based on the model for whitening the prewhitened data; and conducting symbol hypothesis testing based on sequential evaluation of metric characterizing likelihood of hypotheses.

---

Description

---

FIELD OF THE INVENTION

The invention relates to wireless communications, and more particularly, to a wireless digital signal receiver for applications where recovery of digital signals corrupted by co-channel interference from similarly modulated interfering signals is desirable.

BACKGROUND OF THE INVENTION

Wireless networks are employed for communication between various devices, such as cell phones and computers. Digitally modulated signals such as binary phase shift keyed and quadrature phase shift keyed signals are transmitted between nodes of the network. Examples include satellite communications networks where terminals transmit through satellite transponders, terrestrial systems where terminals transmit through repeating towers, and indoor local area networks where terminals transmit through central repeating elements.

Computer elements connected to these networks provide a variety of user services. Examples include telephone traffic with digital voice encoding, video conferencing, wide area computer network connectivity, and internet service. In such applications, it is desirable to maximize the network traffic capacity in a given bandwidth in the presence of interference and noise.

To that end, a variety of schemes exist for efficiently partitioning the network elements into communication channels. For example, frequency domain multiple access (FDMA) schemes assign each network terminal to a separate, non-overlapping frequency band. Time domain multiple access (TDMA) schemes assign each terminal to a separate non-overlapping time slot. Code division multiple access (CDMA) schemes assign each terminal to a separate modulating waveform so that the cross correlation between each terminal is negligible.

Such schemes are inefficient in that given sufficient signal to noise ratio or coding redundancy, more communicators could use the allocated bandwidth if provided with a means for detecting the excess signal margin, as well as a means for demodulating signals in the presence of interference. In short, despite the advancements in wireless transmission and reception, conventional systems do not properly account for the real world wireless communication signals that suffer from signal degradation such as interference and multipath problems.

More specifically, a real world multiuser system includes a number of independent users simultaneously transmitting signals. Each of these transmissions are associated with real-time problems of multipath and co-channel interference that manifest in the received signals. Multipath occurs when a signal proceeds to the receiver along not one but many paths so that the receiver encounters echoes having different and randomly varying delays and amplitudes. Co-channel interference refers to signals received from other users.

A multiuser detection (MUD) receiver can be used to jointly demodulate co-channel interfering digital signals. In general, MUD refers to the detection of data in non-orthogonal multiplexes. MUD processing increases the number of information bits available per chip or signaling dimension for interference limited systems. Optimal MUD based on the maximum likelihood principle operates by comparing the received signal with the entire number of possibilities that may have occurred at the ensemble of transmitters, to give rise to the waveform received at the receiver.

However, for multiuser detectors that examine a larger capacity of signal, the computations are complex and time-consuming, thus making real-time operation impractical. Reduced complexity approaches based on conventional tree-pruning algorithms help to some extent. However, performance of such multiuser detection algorithms degrades as the parameter M (pruning factor) is decreased, but M governs the number of computations required. Thus, to combat improper pruning, basic tree-pruning must ensure that M is large enough. As a result, conventional pruning methods are still associated with increased complexity, particularly when the number of interfering signals is moderate to large.

What is needed therefore are techniques for allowing multiple users to operate in the same communication channel. Such techniques should accurately separate co-channel signals and reduce complex processing.

BRIEF SUMMARY OF THE INVENTION

One embodiment of the present invention provides a co-channel interference receiver. The receiver includes a multiuser detector module that is adapted to receive a complex signal that contains information from K co-channel interfering signals. The receiver further includes a parameter estimation module that is adapted to receive the complex signal, and to generate estimated signature waveforms of each of the K co-channel interfering signals. The estimated signature waveforms are provided to the multiuser detector thereby enabling demodulation of the K co-channel interfering signals. Note that the multiuser detector module and the parameter estimation module can each be adapted to receive a plurality of complex signals.

The multiuser detector module is configured to operate, for example, with an algorithm with partially quantized prior information. Alternatively, the multiuser detector module is configured to operate with an algorithm based on prewhitened data. Alternatively, the receiver may be configured to operate with both algorithms. In one such embodiment, the algorithm with partially quantized prior information is a low complexity linear MMSE algorithm. In another such embodiment, the algorithm based on prewhitened data in one of an M-algorithm and T-algorithm.

The receiver may further include an analog front end that is operatively coupled to the multiuser detector module and the parameter estimation module. The analog front end is adapted to receive one or more composite waveform signals each from a plurality of transmitters, and to convert each received composite waveform signal to a corresponding complex signal. In one such embodiment, the analog front end includes one or more analog to digital converters, each adapted to convert a received composite waveform to a digital waveform, and also includes one or more downconverters, each operatively coupled to a respective analog to digital converter, and adapted to translate frequency associated with a received composite waveform to a lower frequency.

The analog front end may further include one or more antennas each configured to receive a corresponding composite waveform signal from the plurality of transmitters. In one such embodiment, the one or more antennas is a singly polarized antenna. Alternatively, the one or more antennas is a dual polarized antenna adapted with two polarization ports, thereby providing polarization diversity. Alternatively, the one or more antennas include two or more dual polarized antennas, each adapted with two polarization ports, thereby providing space and polarization diversity.

In an embodiment where the multiuser detector module is configured to operate with the low complexity linear MMSE algorithm with partially quantized prior information, the multiuser detector module includes a turbo MUD module, a combiner module, an error correction module, and a thresholding module. The turbo MUD module is adapted to provide estimates of individual bits for each of the K co-channel interfering signals. The estimates are iteratively applied in a feedback loop, which includes the error correction module, until an error rate associated with the bits drops below a predetermined figure. The combiner module is operatively coupled to the turbo MUD, and is adapted to combine recomputed bit estimates output by the turbo MUD with quantized bit values on a next iteration. The thresholding module is operatively coupled to the output of the error correction module, and is adapted to assign a quantized value for each bit estimate above a predetermined threshold, and to pass through those quantized bit values to the combiner module, thereby enabling partially quantized prior information. In one such embodiment, the error correction module on each subsequent iteration processes a combination of recomputed bit estimates output by the turbo MUD and quantized bit values output by the thresholding module, and provides its output back to the turbo MUD through the thresholding module, thereby reducing the number of uncertain bit estimates with every iteration.

In an embodiment where the multiuser detector module is configured to operate with the low complexity M-algorithm based on prewhitened data, the multiuser detector module includes a matched filter, a whitener designer module, an asynchronous whitener module, and a symbol hypothesis testing module. The matched filter is adapted to prewhiten complex signals received by the receiver, thereby partially decoupling users from multiple access interference. The whitener designer module is operatively coupled to the parameter estimator, and is adapted to develop a model of each received complex signal based on parameter estimates from the parameter estimator, and to compute an asynchronous whitener module that whitens filtered data output by the matched filter. The symbol hypothesis testing module is operatively coupled to the whitener designer module, and configured to receive whitened data output by the asynchronous whitener module. The symbol hypothesis testing module is adapted to conduct symbol hypothesis testing based on sequential evaluation of metric characterizing likelihood of hypotheses. In one such embodiment, the whitener designer module employs a square-root factorization. For example, the whitener designer module utilizes a correlation matrix provided by the parameter estimation module to compute a diagonally loaded Cholesky Factorization, which is used for whitening in the whitening module, and is also used in hypothesis testing in the symbol hypothesis testing module. Alternatively, the whitener designer module employs a QR factorization using Householder transformations. In addition, the whitener designer module can employ Hyperbolic Householder transformations to efficiently update the asynchronous whitener module when only received energies and/or phases change between symbol periods.

The parameter estimates of the parameter estimation module can be used to model a channel associated with each received complex signal, thereby enabling application of the matched filter and development of an asynchronous decorrelating filter bank. In one embodiment, the parameter estimation module includes a training sequence locator module, a noise estimator module, and a signature waveform estimator module, an active user tester module, a transformation matrix rebuilder module. The training sequence locator module is adapted to estimate a training sequence location index in each frame of the received complex signal. The noise estimator module is adapted to calculate an estimate of an average noise power in the received complex signal in accordance with the training sequence location index. The signature waveform estimator module is adapted to estimate signature waveforms unique to each user in the received complex signal in accordance with the training sequence location index and a transformation matrix. The active user tester module is operatively coupled to an output of the noise estimator module and to an output of the signature waveform estimator module, and is adapted to determine a number of active users associated with the received complex signal. The transformation matrix rebuilder module is operatively coupled to the active user tester module and to pre-stored known training sequences for each user, and is adapted to generate the transformation matrix used by the signature waveform estimating module.

Another embodiment of the present invention provides a method for receiving a complex signal that contains information from K co-channel interfering signals. The method includes estimating signature waveforms of each of the K co-channel interfering signals, and processing the complex signal based on the signature waveforms with at least one of: a low complexity linear MMSE algorithm with partially quantized prior information, and a low complexity M-algorithm based on prewhitened data. In one such embodiment, the low complexity linear MMSE algorithm with partially quantized prior information includes eliminating from each processing iteration consideration of those bits having an estimate value that exceeds a predetermined threshold, wherein bit estimates exceeding the threshold are considered certain. In another such embodiment, the low complexity M-algorithm based on prewhitened data includes filtering the complex signal, thereby partially decoupling users from multiple access interference and providing prewhitened data. The method proceeds with developing a model of the received complex signal based on parameter estimates, computing an asynchronous whitener based on the model for whitening the prewhitened data, and conducting symbol hypothesis testing based on sequential evaluation of metric characterizing likelihood of hypotheses.

The features and advantages described herein are not all-inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and not to limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system block diagram of a Communication System having a Multiuser Detection Receiver configured in accordance with one embodiment of the present invention.

FIG. 2 illustrates a Multiuser Detection Receiver having a dual polarized antenna in accordance with another embodiment of the present invention.

FIG. 3 illustrates a Multiuser Detection Receiver having more than one antenna, each antenna having one or two polarizations in accordance with another embodiment of the present invention.

FIG. 4 is a diagram of the frame structure underlying the received baseband signal for the case of two or more co-channel interfering signals for a given diversity port signal and also shows training sequence sliding search windows for use in the training sequence locator, in accordance with one embodiment of the present invention.

FIG. 5 is a block diagram of the Parameter Estimator configured in accordance with one embodiment of the present invention.

FIG. 6 is a flow chart of a Training Sequence Locator component configured in accordance with one embodiment of the present invention.

FIG. 7 is a block diagram of a Noise Estimator component configured in accordance with one embodiment of the present invention.

FIG. 8 is a flow chart of the Signature Waveform Estimator component configured in accordance with one embodiment of the present invention.

FIG. 9 is a flow chart for an Active User Tester component configured in accordance with one embodiment of the present invention.

FIG. 10 is a flow chart for an Active User Test For Diversity Port "p" in accordance with one embodiment of the present invention.

FIG. 11 is a diagrammatic representation of iterative processing of incoming data streams in a Turbo MUD in accordance with one embodiment of the present invention.

FIG. 12 is a block diagram illustrating a Turbo MUD system configured in accordance with one embodiment of the present invention.

FIG. 13 is a diagrammatic illustration of example thresholding performed by the system of FIG. 12.

FIG. 14 is a block diagram illustrating example thresholding and combining performed by the system of FIG. 12.

FIG. 15A is a schematic diagram of two of the bits in an example K by L matrix of FIG. 14 having certain values.

FIG. 15B is a diagrammatic illustration of example processing of the values from the matrix of FIG. 14.

FIG. 15C is a diagrammatic illustration of MUD signals which are processed on a next iteration combined with unprocessed signals so as to provide the error correction unit of FIG. 14 with appropriate estimates.

FIG. 16 implements an M-algorithm based on prewhitened data in accordance with an embodiment of the present invention.

FIG. 17 demonstrates the symbol hypothesis testing based on sequential evaluation of metric characterizing likelihood of hypotheses in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In present day communication systems, a central controller normally assigns one communicator to each channel, during a communication channel setup period. Channels may be some combination of a time slot, a frequency, and a spreading code. In most systems, channels are re-used in distant regions, thereby giving rise to co-channel interference. If there is a large distance between the regions wherein the re-use occurs, then signal attenuation reduces the co-channel interference to tolerable levels. This is a necessary result, as conventional receivers cannot demodulate a signal in the presence of significant co-channel interference.

A Co-Channel Interference Receiver configured in accordance with the principles of the present invention can jointly demodulate two or more signals transmitted on the same channel. Systems utilizing an embodiment of the Co-Channel Interference Receiver could use a similar channel setup format, but the channel assignments would not be limited to a single communicator per channel in any given region. More specifically, when all available channels are filled with one user per channel, the central controller can begin filling new channel requests by adding the new communicator to an already occupied channel.

This will slightly degrade the bit error rate of the first communicator. The central controller may optionally direct the transmitters on this channel to increase transmit power to bring the bit error rate back down. Alternatively, the central controller may optionally direct the transmitters and receivers on this channel to decrease the bandwidth to bring the bit error rate back down.

In addition, the receiver of the present invention is configured to receive data using spatially diverse and or polarization diverse antennas. Both concepts are within the scope of the invention, as well as the use of a single antenna. Encompassing more than one polarization port allows the transmitter to transmit in both polarizations and in the event of significant multipaths or electromagnetic scattering, both polarizations may be received and processed even though only one was transmitted. Polarization and/or space diversity increases the number of dimensions in the signal space, which effectively increases the distance between constellation points. As a result, the bit error rate is improved.

Embodiments of the invention can be used in several applications. One such application is a co-channel communications system for airborne-to-ground communications, where the system simultaneously receives signals from several independent communication networks transmitting similarly modulated data on nearly identical carrier frequencies. The main beam area coverage can be low density (e.g., 10,000 square kilometers) or high density (e.g., 100 square kilometers). Consider, for example, a main beam covering 28 base stations and a frequency reuse factor of 7, where there are 8 users per base station, with 4 of the base stations operating on the same frequency. Such a system would receive up to 224 users' downlink signals, and a variety of signal types originating from diverse sources would need to be processed by a single receiver.

Another example co-channel system application is a terrestrial frequency communications receiver simultaneously receiving signals from elements of the same communication network employing frequency reuse. The communications network might be, for instance, a packet radio network, a cell phone network, or a wireless local area network. Due to inadvertent positioning of the network elements, the network is degraded by interference.

While the discussion herein illustrates wireless communication applications, the principles of the present invention are equally applicable to wired cable systems and local area networks, read/write operations of a disc drive or other storage devices, satellite communications, and any application that benefits from manipulating digital data from among many multiple sources.

Co-Channel Interference Receiver

Referring to FIG. 1, a system block diagram is shown of a Communication System 10 comprising a Multiuser Detection (MUD) Receiver 12 and a plurality of User Transmitters 11.sub.1 to 11.sub.K, which are all simultaneously transmitting co-channel, interfering digital signals, all on the same frequency, all using the same type of modulation scheme such as digital phase shift key (PSK) or quadrature amplitude modulated (QAM) signals, with the same nominal data rate.

Each of the User Transmitters 11.sub.1 to 11.sub.K has a unique, known training sequence. The training sequences are roughly aligned as received at a Receiver Antenna 13, so that the training sequences mostly overlap. This type of synchronization is normally provided in communication systems through the use of a synchronization signal transmitted from a unit co-located with the MUD receiver 12. Alignment of the symbol transitions is not required.

In this particular embodiment, the MUD Receiver 12 an analog front end that includes the Antenna 13, a Signal Sampler 14, and a Downconverter 16, and the output (e.g., baseband signals or other lower frequency versions of the received signals) of the Downconverter 16 are fed to a Multiuser Detector 18 and a Parameter Estimator 20 which estimates the signature waveforms for each user.

K signals from the User Transmitters 11.sub.1 to 11.sub.K are received by the Antenna 13 as the sum of the signals from Transmitters 11.sub.1 to 11.sub.K. The Antenna 13 is a singly polarized antenna with a single connection to the Signal Sampler 14. This connection is made, for example, by a transmission line or Waveguide 22 that connects from one Antenna 13 to one Signal Sampler 14.

The Signal Sampler 14 may be embodied by an analog-to-digital converter (A/D). The output of the Signal Sampler 14 is a Snapshot 15 of the sampled waveform (R) received from the Antenna 13 and this Snapshot 15 is composed of at least the number of samples in two frames of data. Alternately, the snapshot 15 may be composed of the number of samples in several frames of data. The Snapshot 15 is fed to a Downconverter 16, which is typically used in digital radios to translate the frequency of the received signal, R, to baseband. The output 17 of the Downconverter 16 is a complex baseband signal, r(n, 1), which contains information from all K co-channel interfering signals in the same frequency and time space.

The baseband signal, r(n, 1), is sent to the Parameter Estimator 20. The Multiuser Detector 18 jointly demodulates the co-channel interfering digital signals, using information provided by the Parameter Estimator 20. The Parameter Estimator 20 uses knowledge (stored in Memory 19) of the unique training symbols transmitted by User Transmitters 11.sub.1 to 11.sub.K, and contained in the composite received signal r(n, 1) to solve for the signature waveforms of the K signals. The term "signature waveform" is herein used to denote the impulse response of the channel through which the signal passes. The term "channel" is used herein to include not only the propagation channel and antenna effects, but also any filtering used in the transmitters 11.sub.1 to 11.sub.K and Receiver 12 front end. In addition, in a direct sequence spread spectrum system, it would also include the spreading code.

The optimal Multiuser Detector 18 is one that minimizes the mean square error between the received signal and all possible combinations of each users transmitted data symbols transformed by their unique signature response. This optimal Multiuser Detector 18 can be expressed mathematically as follows:

.times..times..di-elect cons..OMEGA..times..times..times..function..times..function..function..ti- mes. ##EQU00001## where .OMEGA.=the constraint set of all possible combinations of transmitted data symbols. A number of low complexity MUD algorithms are described herein.

The purpose of the Parameter Estimator 20 is to supply the Multiuser Detector 18 with the information needed to solve Equation 1. The signature waveforms 30, which are unique to each user and each diversity port, describe the transformation of each user's transmitted symbols as they propagate from Transmitters 11.sub.1 to 11.sub.K to Receiver 12. This includes pulse shape filtering on the Transmitters 11.sub.1 to 11.sub.K and receiver filtering on the Receiver 12. Some multiuser detectors may also require information about the location of the training sequence in each frame of data for synchronization, and they may also require information about the noise power in the received signal to make better estimates of the transmitted symbols for each user. The Parameter Estimator 20 may be configured to calculate such parameters, and therefore, will operate with any Multiuser Detector 18 that requires these inputs.

In one embodiment, the Parameter Estimator 20 generates outputs that occur once per snapshot and contain parameter estimates for each frame of data in that snapshot. These parameter estimates include estimated signature waveforms 30, s.sub.ka(n, p, m), for each diversity port (p), frame (m), and active user (k.sub.a). The outputs also include an estimated noise power 26 {circumflex over (.sigma.)}.sup.2(p), which is a scalar that represents the average power of the noise and a training sequence index 28, .tau..sub.TS, which is a pointer to the location of the training sequence in each frame of the snapshot 15. The outputs also include an active users vector 29 (u(k)) that contains the state of each user, k. State refers to the user being "actively transmitting" or "not transmitting".

The outputs of the Parameter Estimator 20 are sent to the MUD 18, which also receives the r(n, 1) baseband signal 17, and produces separate streams of transmitter I symbols 39 to transmitter K symbols 38 for signal 1, signal 2, up to signal K which correspond to each of the K co-channel interfering signals sent by Transmitters 11.sub.1 to 11.sub.K The system may further include additional post-MUD processing componentry (not shown) adapted to receive the outputs of the MUD 18, such as frequency mismatch compensation modules, block error decoding modules, demultiplexing or depacketizing modules, and routing modules.

Polarization Diversity MUD

FIG. 2 illustrates a Multiuser Detection Receiver having a dual polarized antenna in accordance with another embodiment of the present invention. The inclusion of a Dual Polarized Antenna 40 provides more information to the Multiuser Detector 18, to make better symbol decisions, thereby reducing the symbol error rate. This extra information derives from the fact that the signals received by orthogonally polarized antenna ports travel through effectively different channels. The Parameter Estimator 20 will provide to the Multiuser Detector 18 the signature waveforms 30 for each user "k" received by both antenna ports (p=1,2). Hence, there will be K.times.2 signature waveforms 30 passed to the Multiuser Detector 18. Therefore, the MUD 18 will have twice as many equations compared to only one polarization, but the same number of symbols for which to solve.

The use of a dual polarized antenna will be of benefit, for example, in the following two cases: first, where the signal is transmitted in dual orthogonal polarizations, and second, where electromagnetic scattering causes significant cross polarized energy to be received at the Receive Antenna 40, even though only one polarization was transmitted.

Space and Polarization Diversity MUD

FIG. 3 illustrates a Multiuser Detection Receiver having more than one antenna, each antenna having one or two polarizations in accordance with another embodiment of the present invention. The inclusion of extra antenna ports provides even more information to the MUD 18, enabling it to make better symbol decisions, thereby reducing the symbol error rate. In order to provide added benefit, the extra antennas must be space diverse. In other words, the antennas must be spaced far enough apart that they provide a significantly different propagation channel. In this case, the Parameter Estimator 20 processes the signal r(n,p), where p=1, 2, . . . P and produces K.times.P signature waveforms 30 to pass to MUD 18.

Frame Structure and Training Sequence Sliding Search Windows

FIG. 4 is a diagram of the frame structure underlying the received baseband signal r(n,p) for the case of multiple (K) co-channel interfering signals for a given diversity port signal "p", showing a sequence of framed segments f.sub.m(n,p) having a received composite training sequence .beta.(n,p), at the same location of each frame segment, and also shows training sequence sliding search windows (l.sub.m(.tau.,p)) for use in the training sequence locator.

In one embodiment, the received composite training sequence, .beta.(n,p), is defined as the complex baseband version of the sum of each users training sequence, b.sub.k(n), convolved (indicated by an asterisk) with its respective signature waveform, s.sub.k(n,p), plus additive white Gaussian noise, w(n,p). This relation is defined mathematically as follows:

.beta..function..times..function..function..function. ##EQU00002##

FIG. 4 also shows the training sequence sliding search windows, l.sub.m(.tau.,p), for use in the training sequence locator. In particular, the training sequence sliding search windows are used by the Detection Statistic Calculator 90.sub.p, which is part of the Training Sequence Locator 56 (FIG. 6). These sliding search windows are L samples long, where L is the number of samples in a received composite training sequence, .beta. (n,p). The first index of each sliding search window is separated by F samples, where F is the number of samples in a frame of data. Each sliding search window, l.sub.m(.tau.,p), is moved across a corresponding frame of received data, f.sub.m(n,p), shifted one sample at a time for a total of F sample shifts. For each sample shift, .tau., the data in each sliding search window, l.sub.m(.tau.,p), is used by the Detection Statistic Calculator 90 to calculate the corresponding value of the detection statistic.

Parameter Estimation

FIG. 5 is a block diagram of the Parameter Estimator configured in accordance with one embodiment of the present invention. Parameter Estimator 20 is shown comprising software modules which include a Training Sequence Locator 56, a Noise Estimator 52, a Signal Estimator Loop 57, and an Initial Transformation Matrix Builder 63. The Signal Estimator Loop 57 further includes a Signature Waveform Estimator 58, an Active Users Tester 60, a Transformation Matrix Selector 61, and a Transformation Matrix Rebuilder 62. Alternative embodiments and configurations may implement similar functionality.

The Training Sequence Locator 56 is used to estimate the location index, .tau..sub.TS, in each frame of received data, f.sub.m(n,p), of the composite received training sequence, .beta.(n,p), and the Noise Estimator 52 is used to calculate an estimate of the average noise power ({circumflex over (.sigma.)}(p).sup.2) in the received signal r(n,p) for each diversity port, p. The Signature Waveform Estimator 58 is used to estimate the characteristic signature waveforms, s.sub.k(n, p, m), unique to each user K, and each diversity port p, for each frame m, in the received snapshot.

The output of the Signature Waveform Estimator 58 is fed to an Active Users Tester 60 which detects which users signals are present in the given snapshot, and provides an output to a Transformation Matrix Rebuilder 62 which rebuilds the Transformation Matrix (T.sub.r2) that is used in the Signature Waveform Estimator 58. This matrix is rebuilt by using only the training sequences, b.sub.k(n), of the active users as calculated by the Active Users Tester 60.

The output of the Transformation Matrix Rebuilder 62 is fed to a Transformation Matrix Selector 61 which selects the output T.sub.r1 from an Initial Transformation Matrix Builder 63 or the output T.sub.r2 from the Transformation Matrix Rebuilder 62 to send to the Signature Waveform Estimator 58. In this particular embodiment, the Transformation Matrix Selector 61 always selects T.sub.r1 for the initial estimate of the signature waveforms in the given snapshot, and always selects T.sub.r2 for all subsequent recalculations of the signature estimates for the same snapshot of data. This allows the Signature Waveform Estimator 58 to calculate a better estimate of the characteristic signature waveforms, s.sub.ka(n, p, m), for only the active users as determined by the Active User Tester 60.

This process of performing the Signature Waveform Estimator 58, performing the Active User Tester 60, and running the Transformation Matrix Rebuilder 62, is referred to as the Signature Estimation Loop 57. The Signature Estimation Loop 57 can be repeated until the output of the Active User Tester 60 calculated on the previous iteration equals the output of the Active User Tester 60 on the current iteration. It is also possible to set the maximum number of Signature Estimators Loops 57 in the Parameter Estimation 20 component.

Note that with each iteration through this Loop 57, the number of signature waveforms at the output of the Signature Waveform Estimator 58 is equal to the number of active users calculated on the previous iteration. Further note that on the first iteration, the number of signature estimates is equal to the total number of possible users, K. Once the final signature estimates of the active users are calculated, the resulting waveforms are passed as outputs of the Parameter Estimator 20 along with the user states vector, u(k) that reports which users are active in the current snapshot.

The Initial Transformation Matrix Builder 63 receives known training sequence data, b.sub.k(n), for each user, which may be prestored, for example, in a Memory 19 of the Multiuser Detection Receiver 12. Each user's training sequence data is used to build the Initial Transformation Matrix, T.sub.r.sub.1, which is fed to the Transformation Matrix Selector 61.

The Noise Estimator 52 estimates the noise power in the incoming signal, r(n,p) for all p=1,2, . . . P, diversity ports and feeds the information to the Active User Tester 60 and the MUD 18. This estimation is typically done once per snapshot wherein the snapshot is at least the number of samples in two frames, but need not be done as often if the noise power is changing slowly or not at all. Note that the accuracy of the Noise Estimator 52 improves as the number of composite training sequence estimates, {circumflex over (.beta.)}.sub.m(n, p), increases. To increase the number of composite training sequence estimates, the number of frames, f.sub.m(n,p), in the received complex baseband signal, r(n,p), must increase. This results in an increased snapshot size. Alternatively, the Training Sequence Selector 56 must store the composite training sequence estimates, {circumflex over (.beta.)}.sub.m(n, p), for multiple snapshots of received data, and calculate the estimated noise power using the total number of stored composite training sequence estimates {circumflex over (.beta.)}.sub.m(n, p).

The Training Sequence Locator 56 determines the position of the training sequence in each frame, f.sub.m(n,p) of the received snapshot vector, r(n,p) and feeds this information in the form of a sample index, .tau..sub.TS, referred to as the Training Sequence Location Index 28, to the MUD 18. In addition, the position of the training sequence in the received snapshot is fed to the Noise Estimator 52 and to the Signature Waveform Estimator 58 where it is used to determine which section of each frame, f.sub.m(n,p), in r(n,p) to process in order to determine the average noise power estimate, {circumflex over (.sigma.)}(p).sup.2 and signature estimates s.sub.k(n, p, m), respectively. The Signature Waveform Estimator 58 estimates the signature waveforms s.sub.k(n, p, m) in each frame, m, of each K individual co-channel interfering signal in the composite received input signal, r(n,p), for each diversity port p, and outputs this information to the Active User Tester 60 and MUD 18.

FIG. 6 is a flow chart of a Training Sequence Locator component configured in accordance with one embodiment of the present invention. The Training Sequence Locator 56 component is configured to locate the training sequence in the received complex baseband signal, r(n,p), received from each diversity port, (p=1,2 . . . ,P), processing a minimum of two frames of received data. Because the number of samples in a transmitted frame of data (F) is known to the receiver, each received frame, f.sub.m(n,p), in r(n,p) repeats at the same time rate as a transmitted frame of data. Therefore, the location of the composite training sequence, .beta.(n,p), will be the same for each frame, f.sub.m(n,p), of received data. However, because the transmitted and received frames of data are not initially synchronized, the beginning of a transmitted frame of data may not start at the beginning of a frame, f.sub.m(n,p), of received data. This means that the location of the training sequence in each frame of received data also may not start at the beginning of each frame.

The Training Sequence Locator 56 finds the location of the training sequence in each frame of received data. To do this, a sliding search window vector, l.sub.m(.tau.,p), that is L samples long (the same length as the received composite training sequence) is applied simultaneously through each frame of received data, and the correlation between each combination of windowed frames is computed and then averaged in a Detection Statistic Calculator 90. The result is a detection statistic, d.sub.p(.tau.), which is exactly the length of a frame of received data (F samples long). Because the payload data is uncorrelated from frame to frame, the detection statistic will have a very low value when the sliding search windows are over the payload data in each frame. On the other hand, the composite training sequence, .beta.(n,p), is highly correlated from frame to frame. Therefore, the detection statistic will be very high when the sliding search windows are over the composite training sequence in each frame. Thus, the location .tau..sub.p, of the peak in the detection statistic, d.sub.p(.tau.), will be the location of the training sequence in each frame sequence, f.sub.m(n,p).

The inputs to the Training Sequence Locator 56 component are complex baseband received signals, r(n,p), from each diversity port, (p=1,2, . . . P). An estimate of the training sequence location index, .tau..sub.p, is calculated separately for each diversity port signal, r(n,p) by the Detection Statistic Calculator 90. The first step in estimating the training sequence location index, .tau..sub.p, is to provide the received signal, r(n,p), to the Detection Statistic Calculator 90, for calculating the detection statistic, d.sub.p(.tau.), using that received signal. As previously stated, each element of this detection statistic is generated by calculating the correlation coefficients, .rho..sub.ij(.tau.,p), for each combination of sliding search windows for a given training sequence sample index, .tau.. Once each combination of correlation coefficients is calculated, they are averaged and output as the value of the detection statistic, d.sub.p(.tau.), for the specified value of .tau.. The step by step calculations needed to perform this process in accordance with one embodiment of the present invention are as follows:

Step 1. Define the sliding search window, l.sub.m(.tau.,p) for each frame of received data in the given signal, r(n,p), for the given search window sample index, .tau..

.function..tau..function..tau..function..tau..function..tau..A-inverted..t- imes..times. ##EQU00003##

Step 2. Calculate the energy, e.sub.m (.tau.,p), in each sliding search window, l.sub.m(.tau.,p): e.sub.m(.tau.,p)=l.sub.m(.tau.,p).sup.Hl.sub.m(.tau.,p), .A-inverted.m=1,2, . . . ,M (4)

Step 3. Calculate the correlation coefficient, .rho..sub.ij(.tau.,p), for each combination of sliding search windows:

.rho..function..tau..times..tau..function..tau..function..tau..function..t- au..times..A-inverted..times..times..times..times..times. ##EQU00004##

Step 4. Calculate the detection statistic, d.sub.p(.tau.), for the given search window sample index, .tau., for diversity port, p, by averaging the corresponding correlation coefficients:

.function..tau..times..times..rho..function..tau..times. ##EQU00005##

This process (steps 1 4) is repeated for each search window sample index, {.tau.=1,2, . . . ,F} and for each diversity port {p=1,2, . . . P}.

Still referring to FIG. 6, once the detection statistic, d.sub.p(.tau.), for each diversity ports received signal, r(n,p), is calculated, it is fed to a Training Index Finder 92, where the estimated location, .tau..sub.p, of the training sequence for each diversity port signal is calculated by finding the sliding search window index, .tau., that maximizes the detection statistic. The Training Index Finder 92, calculates the following equation for each detection statistic, d.sub.p(.tau.):

.tau..times..tau..di-elect cons..times..times..function..tau..A-inverted..times. ##EQU00006##

Next, a Confidence Metric Calculator 94, calculates a confidence metric from each detection statistic. This is done by calculating the peak to rms value of each detection statistic. This process can be implemented, for instance, by performing the following calculation for each detection statistic, d.sub.p(.tau.).

.function..function..tau..function..function..tau..A-inverted..times. ##EQU00007##

As previously stated, this entire detec