Adaptive Channel Equalizer Using Bacterial Foraging Oriented By Particle Swarm Optimization Strategy

High speed data transmissions 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. In addition to this, channel characteristics also vary with time, which requires the equalizer to adapt to the channel. Adaptive equalization of the digital channels at the receiver removes/reduces the effects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse filter which is placed at the front end of the receiver. Its transfer function is inversed to the transfer function of the associated channel.The standard derivative based algorithms such Least-Mean-Square (LMS) and Recursive-Least-Square (RLS) suffer from local minima problem while obtaining the solution of the weights. To prevent the premature settling of the weights, evolutionary computing based update algorithm is proposed which is essentially a derivative free technique.In recent years the area of Bacterial Foraging Optimization (BFO) has drawn attention of many researchers due to its broad applicability to different fields. In this thesis, the proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in Bacterial Foraging Optimization Algorithm (BFOA) with the swarming pattern of birds in block introduced in Particle Swarm Optimization (PSO). It incorporates the merits of the two bio-inspired algorithms to update the weights of the equalizer. This combination aims to make use of PSO ability to exchange social information and BFO ability in finding a new solution, to improve the convergence speed and accuracy of the basic BFOA. The minimization of mean squared error (MSE) is achieved iteratively using the hybrid algorithm (BF-PSO). The proposed algorithm has been extensively compared with the original BFOA algorithm, the classical PSO algorithm and Genetic Algorithm (GA) in terms of speed of convergence and bit-error-rate (BER). It is demonstrated that the performance of BF-PSO based equalizer provides substantial improvement in terms of convergence rate, MSE floor level and BER. Thus it is concluded that the BF-PSO is a potential learning tool for efficient equalization of complex nonlinear channels under high noise conditions.This thesis presents also the design of the proposed adaptive equalizer as applied in communications systems. The adaptive equalizer is designed using VHDL and synthesized for FPGA implementation. The results of the HDL cosimulation indicate that the design and the VHDL source code successfully converge and provide successful channel equalization.The work is successfully achieved in the direction to provide a good adaptive channel equalizer. The designed model for BF-PSO is accurate and can work for real time applications.