FPGA Implementation of an Adaptive Equalizer with TLBO and PSO Variant

Abstract

This paper presents the Hardware implementation of two algorithms, Particle Swarm Optimization with Time Varying Inertia Weight and Teaching Learning-Based Optimization, which are applied to an Adaptive Equalizer with a Field Programmable Gate Array. Before the hardware implementation, MATLAB simulation results are also presented with a parameter like mean square error. Error convergence graphs of two algorithms have been observed for signal-to-noise ratios 10, 20, and 30 dB. The frequency response of the channel, equalizer, and of cascaded system is also presented. After MATLAB simulation, the same algorithm is implemented on Field Programmable Gate Array and a complete analysis is presented based on different parameters, resource utilization, execution time, frequency, and power required. Register transfer level views and simulation waveforms are also presented. By overall observation from Software and Hardware results, it is observed that Teaching Learning-Based Optimization algorithm is giving a better performance than Particle Swarm Optimization with Time Varying Inertia Weight algorithm.