On the Development of a New Adaptive Channel Equalizer using Bacterial Foraging Optimization Technique

High speed data transmission over communication channels distort the transmitted signals in both amplitude and phase due to presence of inter symbol interference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the effects of such ISIs and attempts to recover the transmitted symbols. The multilayer perceptron (MLP), fuzzy logic (FL) and radial basis function (RBF) based equalizers are relatively new soft computing based equalizers which aim to minimize the ISI present in the channels particularly for nonlinear channels. However they suffer from long training time and undesirable local minima. In the present paper we propose a new adaptive channel equalizer using a novel bacterial foraging optimization (BFO) technique which is essentially a derivative free optimization tool. This algorithm has been suitably used to update the weights of the equalizer. The performance of the proposed equalizer has been evaluated and has been compared with its LMS based counter part. It is observed that the new equalizer offers improved performance both in terms of rate of convergence as well as bit-error-rate