Adaptive Blind Equalization Using Bottleneck Networks Implemented by Evolvable Hardware

We propose the use of bottleneck networks implemented by evolvable hardware for the adaptive blind equalization of digital communication channels. Blind Channel equalization is a method of recovering the original symbols by compensating for channel distortion at the receiver's end without any known training sequence for the startup period. If the non-linear channel distortion is too severe to ignore, the conventional blind equalizers su er from severe performance degradation because they are based on linear transversal lters. The proposed equalizer overcomes this di culty via the bottleneck network implemented by evolvable hardware. Using a genetic algorithm, the network on the hardware is trained to minimize an energy function based on the maximum likelihood estimation. Simulation results show that the proposed equalizers have superior performance to popular CMA blind equalizers.