Survey on Adaptive Channel Equalization Techniques using Particle Swarm Optimization

In digital communication system, symbols are generated in a source and transmitted over a channel to a receiver. Noise gets added during the transition of symbols over the channel from source to the destination. In practice the symbols are corrupted with nonlinear distortion, Inter Symbol Interference (ISI) and noise. One possibility to reduce the effect of this problem is to use a channel equalizer at the receiver. The function of the equalizer is to reconstruct the original signal from the received signal or to generate a reconstructed version of the transmitted signalo as close as possible to it. The addition of an equalizer usually reduces the bit error rate (BER): the ratio of received bits in error to total transmitted bits. The most preferred technique is adaptive equalization. Traditional adaptive equalization uses linear transversal filter to reduce the effect of ISI. This filter is generally adjusted using a known training sequence at the beginning of the transmission and Least Square Estimation or gradient descent to determine the optimal set of coefficients for the filter. In the literature many adaptive algorithms such as Least Mean Square Algorithms have been developed which are effective under the assumption that the output is a linear function of the inputs. In practice this situation is rare and when nonlinear distortion and ISI are severe, nonlinear equalizers such as neural nets, the use of particle swarm optimization (PSO) can give a better performance. The objective of this paper is to discuss some of the adaptive equalization techniques available in the literature and put forth some ideas to improve the performance of PSO based adaptive equalization techniques. Keywords— Particle Swarm Optimization, PSO, adaptive channel equalization, adaptive equalization.