Thesis On Channel Equalization Using Genetic Algorithm

The search abilities of PSO are managed by the key parameter Inertia Weight (IW).

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and are velocity and position of particle i respectively in t iteration.

At each iteration, the change in weights are calculated by Equation (1) and the weights are modified to new one using Equation (2).

Some of the time varying inertia weight modified methods are listed in Table 1. Model and Methodology Figure 1 depicts a basic block diagram used in adaptive equalization [1] .

The input is the random bipolar sequence = ±1 and channel impulse response is raised cosine pulse.

Adaptive algorithms are utilized in equalization to find the optimum coefficients.

The normal gradient based adaptive algorithms such as Least Mean Square (LMS), Recursive least squares (RLS), Affine Projection algorithm (APA) and their variants [1] [2] [3] [4] applied in channel equalization converge to local minima [5] [6] [7] while optimizing the filter tap weights.

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.

(4) (5) (6) where is the convolution of input and channel impulse response (i.e.).

The input to the receiver is (7) where is the distorted version of the input signal.


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