Thesis On Channel Equalization Using Genetic Algorithm

Thesis On Channel Equalization Using Genetic Algorithm-68
To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer.The simulation results also analyze the superiority in linear and nonlinear channel conditions. Introduction Channel equalization [1] [2] plays a pivotal role in high speed digital transmissions to recover the effect of inter symbol interference (ISI).

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The derivative free algorithms find the global minima by passing through local and global search processes.

PSO is one of the derivative free optimization algorithms which search the minima locally and globally.

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While comparing to genetic algorithm, PSO needs minimum iterations to converge to minimum mean square error (MSE) in channel equalization [1] .

To find the best optimum weights of equalizer, PSO updates the weights in each iteration using global best value and local best value [1] .

The channel output is added with the random additive white Gaussian noise (AWGN).

The noise sequence has zero-mean and variance 0.001.

Initially, the weights are randomly selected from the search space for P number of particles.

Using the randomly selected weights, the fitness function is calculated and based on it, is updated and forwarded for next iterations.

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