Linear Neural Networks with FIR Synapses for Blind Deconvolution and Equalization
Seungjin Choi · Journal of Electrical Engineering and Information Science · 1999
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 task. A linear feedback neural network with FIR synapses 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.