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Adaptive minimum bit-error-rate filtering

Adaptive minimum bit-error-rate filtering
Adaptive minimum bit-error-rate filtering
Adaptive filtering has traditionally been developed based on the minimum mean square error (MMSE) principle and has found ever-increasing applications in communications. The paper develops adaptive filtering based on an alternative minimum bit error rate (MBER) criterion for communication applications. It is shown that the MBER filtering exploits the non-Gaussian distribution of filter output effectively and, consequently, can provides significant performance gain in terms of smaller bit error error (BER) over the MMSE approach. Adopting the classical Parzen window or kernel density estimation for a probability density function (p.d.f.), a block-data gradient adaptive MBER algorithm is derived. A stochastic gradient adaptive MBER algorithm is further developed for sample-by-sample adaptive implementation of the MBER filtering. Extension of the MBER approach to adaptive nonlinear filtering is also discussed.
1350-245X
76-85
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2004) Adaptive minimum bit-error-rate filtering. IEE Proceedings - Vision, Image and Signal Processing, 151 (1), 76-85. (doi:10.1049/ip-vis:20040301).

Record type: Article

Abstract

Adaptive filtering has traditionally been developed based on the minimum mean square error (MMSE) principle and has found ever-increasing applications in communications. The paper develops adaptive filtering based on an alternative minimum bit error rate (MBER) criterion for communication applications. It is shown that the MBER filtering exploits the non-Gaussian distribution of filter output effectively and, consequently, can provides significant performance gain in terms of smaller bit error error (BER) over the MMSE approach. Adopting the classical Parzen window or kernel density estimation for a probability density function (p.d.f.), a block-data gradient adaptive MBER algorithm is derived. A stochastic gradient adaptive MBER algorithm is further developed for sample-by-sample adaptive implementation of the MBER filtering. Extension of the MBER approach to adaptive nonlinear filtering is also discussed.

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More information

Published date: February 2004
Additional Information: Special Issue on Non-linear and Non-Gaussian Signal Processing
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 258994
URI: https://eprints.soton.ac.uk/id/eprint/258994
ISSN: 1350-245X
PURE UUID: f32ba03e-516a-4231-b6b3-6ae8f2b4d7ef

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Date deposited: 05 Mar 2004
Last modified: 18 Jul 2017 09:27

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