Blind equalization of SIMO channels via spatio-temporal anti-Hebbian learning rule

Seungjin Choi, Andrzej Cichocki, Shunichi Amari

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

This paper presents a new distributed processing approach to `direct' blind equalization of Single Input Multiple Output (SIMO) channels. Under mild conditions, it is shown here that we can recover the original source signal up to its scaled and delayed version by decorrelating the equalizer (neural network) outputs in spatio-temporal domain. `Spatio-temporal anti-Hebbian' learning rule (simple, local, biologically plausible) is derived from an information-theoretic approach and is applied for spatio-temporal decorrelation. A linear feedback neural network with FIR synapses (trained by spatio-temporal anti-Hebbian learning rule) is proposed and is shown to be a good candidate for the equalizer. Computer simulation experiments confirm the validity and high performance of the proposed neural network with the associated learning algorithm.

Original languageEnglish
Pages93-102
Number of pages10
Publication statusPublished - 1998
Externally publishedYes
EventProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl
Duration: 31 Aug 19982 Sep 1998

Conference

ConferenceProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII
CityCambridge, Engl
Period31/08/982/09/98

Fingerprint

Dive into the research topics of 'Blind equalization of SIMO channels via spatio-temporal anti-Hebbian learning rule'. Together they form a unique fingerprint.

Cite this