Blind Equalization of Simo Channels via Spatio Temporal Anti Hebbian Learning Rule

This paper presents a new distributed processing ap proach to direct blind equalization of Single Input Multiple Out put 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 learn ing 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 con rm the validity and high performance of the proposed neural network with the associated learning algo rithm INTRODUCTION Blind equalization is an emerging eld of fundamental research for numer ous applications in digital communication cable HDTV global positioning system and some biomedical applications In blind equalization of SIMO channels it is assumed that the ith sensor output xi k is generated from a PORTION OF THISWORKWAS SUPPORTED BYKOREA SCIENCE ANDENGI NEERING FOUNDATION UNDER THE CONTRACT AND BY BRAIN SCIENCE INSTITUTE RIKEN JAPAN linear time invariant lter as