A comparative analysis of channel equalization algorithms

Channel equalization removes noise and ISI effects in the channel by training the equalizer using specific algorithms. One such algorithm is Particle Swarm Optimization (PSO) derived from the social behavior of bird flocking, fish schooling etc. It gives minimum possible value for mean square error at the expense of low convergence. Teaching Learning Based Optimization (TLBO) algorithm depends on teacher's influence on learners' output in a class. It does not require parameters like cognitive, social and inertia weight which are specific to algorithms. In this work, a comparison of Mean Square Error (MSE) is performed against above two algorithms. Simulation results show that the TLBO algorithm performs better than PSO. Also it gives lowest MSE in very less number of iterations especially when the number of variables is more.