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

This paper presents a new distributed processing approach to "direct" blind equalization of single-input multi-output (SIMO) channels. Under mild conditions, it is shown 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. The "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.