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Nonlinear Multi-Antenna Detection Methods

Nonlinear Multi-Antenna Detection Methods
Nonlinear Multi-Antenna Detection Methods
The paper investigates a nonlinear detection technique designed for multiple-antenna assisted receivers employed in space-division multiple-access systems. We derive the optimal solution of the nonlinear spatial processing assisted receiver for binary phase shift keying signalling, which we refer to as the Bayesian detector. It is shown that this optimal Bayesian receiver significantly outperforms the standard linear beamforming assisted receiver in terms of a reduced bit error rate, at the expense of an increased complexity, while the achievable system capacity is substantially enhanced with the advent of employing nonlinear detection. Specifically, when the spatial separation expressed in terms of the angle of arrival between the desired and interfering signals is below a certain threshold, a linear beamformer would fail to separate them, while a nonlinear detection assisted receiver is still capable of perform adequately. The adaptive implementation of the optimal Bayesian detector can be realized using a radial basis function network. Two techniques are presented for constructing block-data based adaptive nonlinear multiple-antenna assisted receivers. One of them is based on the relevance vector machine invoked for classification, while the other on the orthogonal forward selection procedure combined with the Fisher ratio class-separability measure. A recursive sample-by-sample adaptation procedure is also proposed for training nonlinear detectors based on an amalgam of enhanced $\kappa$-means clustering techniques and the recursive least squares algorithm.
1225-1237
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Czylwik, A.
3af07a3f-4743-4f42-beb2-6b32425e3aa9
Gershman, A.B.
d59ee449-6fcc-4011-986a-d5ec9afe4a93
Kaiser, T.
0469e579-aed4-4c34-9fc6-75d1a8bfeab9
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Czylwik, A.
3af07a3f-4743-4f42-beb2-6b32425e3aa9
Gershman, A.B.
d59ee449-6fcc-4011-986a-d5ec9afe4a93
Kaiser, T.
0469e579-aed4-4c34-9fc6-75d1a8bfeab9

Chen, S., Hanzo, L. and Wolfgang, A. , Czylwik, A., Gershman, A.B. and Kaiser, T. (eds.) (2004) Nonlinear Multi-Antenna Detection Methods. EURASIP Journal on Applied Signal Processing, 2004 (9), 1225-1237.

Record type: Article

Abstract

The paper investigates a nonlinear detection technique designed for multiple-antenna assisted receivers employed in space-division multiple-access systems. We derive the optimal solution of the nonlinear spatial processing assisted receiver for binary phase shift keying signalling, which we refer to as the Bayesian detector. It is shown that this optimal Bayesian receiver significantly outperforms the standard linear beamforming assisted receiver in terms of a reduced bit error rate, at the expense of an increased complexity, while the achievable system capacity is substantially enhanced with the advent of employing nonlinear detection. Specifically, when the spatial separation expressed in terms of the angle of arrival between the desired and interfering signals is below a certain threshold, a linear beamformer would fail to separate them, while a nonlinear detection assisted receiver is still capable of perform adequately. The adaptive implementation of the optimal Bayesian detector can be realized using a radial basis function network. Two techniques are presented for constructing block-data based adaptive nonlinear multiple-antenna assisted receivers. One of them is based on the relevance vector machine invoked for classification, while the other on the orthogonal forward selection procedure combined with the Fisher ratio class-separability measure. A recursive sample-by-sample adaptation procedure is also proposed for training nonlinear detectors based on an amalgam of enhanced $\kappa$-means clustering techniques and the recursive least squares algorithm.

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Published date: August 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259923
URI: https://eprints.soton.ac.uk/id/eprint/259923
PURE UUID: 2b142b66-901e-45dd-9cac-16c085970d89
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 10 Sep 2004
Last modified: 03 May 2019 00:37

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