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Least Bit Error Rate Adaptive Multiuser Detection

Least Bit Error Rate Adaptive Multiuser Detection
Least Bit Error Rate Adaptive Multiuser Detection
Linear detector required at direct-sequence code division multiple access (DS-CDMA)communication systems is classically designed based on the minimum mean squares error (MMSE) criterion, which can efficiently be implemented using the standard adaptive algorithms, such as the least mean square (LMS) algorithm. As the probability distribution of the linear detector's soft output is generally non-Gaussian,the MMSE solution can be far away from the optimal minimum bit error rate (MBER) solution. Adopting a non-Gaussian approach naturally leads to the MBER linear detector. Based on the approach of Parzen window or kernel density estimation for approximating the probability density function (p.d.f.), a stochastic gradient adaptive MBER algorithm, called the least bit error rate (LBER), is developed for training a linear multiuser detector. A simplified or approximate LBER (ALBER) algorithm is particularly promising, as it has a computational complexity similar to that of the classical LMS algorithm. Furthermore, this ALBER algorithm can be extended to the nonlinear multiuser detection.
3-540-40575-5
389-408
Springer-Verlag
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wang, L.P.
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wang, L.P.

Chen, S. (2003) Least Bit Error Rate Adaptive Multiuser Detection. In, Wang, L.P. (ed.) Soft Computing in Communications. Sof Computing for Communications (01/01/03) Springer-Verlag, pp. 389-408.

Record type: Book Section

Abstract

Linear detector required at direct-sequence code division multiple access (DS-CDMA)communication systems is classically designed based on the minimum mean squares error (MMSE) criterion, which can efficiently be implemented using the standard adaptive algorithms, such as the least mean square (LMS) algorithm. As the probability distribution of the linear detector's soft output is generally non-Gaussian,the MMSE solution can be far away from the optimal minimum bit error rate (MBER) solution. Adopting a non-Gaussian approach naturally leads to the MBER linear detector. Based on the approach of Parzen window or kernel density estimation for approximating the probability density function (p.d.f.), a stochastic gradient adaptive MBER algorithm, called the least bit error rate (LBER), is developed for training a linear multiuser detector. A simplified or approximate LBER (ALBER) algorithm is particularly promising, as it has a computational complexity similar to that of the classical LMS algorithm. Furthermore, this ALBER algorithm can be extended to the nonlinear multiuser detection.

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More information

Published date: 2003
Additional Information: submitted in March 2002 as a chapter for the book "Soft Computing in Communications" Chapter: 18
Venue - Dates: Sof Computing for Communications, 2003-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 258216
URI: http://eprints.soton.ac.uk/id/eprint/258216
ISBN: 3-540-40575-5
PURE UUID: 2f3957ab-84cc-4b7a-9bbe-1172e84ba4bc

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Date deposited: 19 Sep 2003
Last modified: 06 Sep 2022 18:13

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Contributors

Author: S. Chen
Editor: L.P. Wang

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