Co-channel interference receiver

• US 20030198305A1
• Filed: 04/25/2003
• Published: 10/23/2003
• Est. Priority Date: 03/25/2002
• Status: Active Grant

• Current Assignee: Collision Communications, Inc. (Concert Technology Corporation)
• Original Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc)
• Inventors: Niedzwiecki, Joshua D., Brommer, Karl D., Learned, Rachel E., Taylor, Matthew A., MacLeod, Robert B., McElwain, Thomas P.
• Granted Patent: US 7,092,452 B2
• Time in Patent Office: Days
• US Class Current: 375/341
• CPC Class Codes: H04B 1/7105 Joint detection techniques, e.g. linear detectors
• CPC Class Codes: H04B 1/71055 using minimum mean squared error [MMSE] detector
• CPC Class Codes: H04L 1/0048 in conjunction with detection of multiuser or interfering signals, e.g. iteration between CDMA or MIMO detector and FEC decoder for spatial equalizer H04L25/03286
• CPC Class Codes: H04L 2025/03611 Iterative algorithms
• CPC Class Codes: H04L 25/0204 of multiple channels
• CPC Class Codes: H04L 25/0228 with direct estimation from sounding signals
• CPC Class Codes: H04L 25/0246 with factorisation
• CPC Class Codes: H04L 25/0248 Eigen-space methods
• CPC Class Codes: H04L 25/03171 Arrangements involving maximum a posteriori probability [MAP] detection
• CPC Class Codes: H04L 25/03203 Trellis search techniques
• CPC Class Codes: H04L 25/03286 with channel-decoding circuitry
• CPC Class Codes: H04L 25/03331 Arrangements for the joint estimation of multiple sequences

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.

Claims

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 I 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 an algorithm with partially quantized prior information and an 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.

24. The receiver of claim 23 wherein the algorithm with partially quantized prior information is a low complexity linear MMSE algorithm.

25. The receiver of claim 23 wherein the algorithm based on prewhitened data in one of an M-algorithm and T-algorithm.

26. 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; and

a low complexity M-algorithm based on prewhitened data.

27. The method of claim 26 wherein 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.

28. The method of claim 26 wherein 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;

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.