Modified PSO-Based Equalizers for Channel Equalization

This work proposes a modified particle swarm optimization (PSO) as an adaptive algorithm to search for optimum equalizer weights of transversal and decision feedback equalizers. Inertia weight is one of the PSO’s critical parameters which manage the search abilities of PSO. Higher values of inertia weight improve the global search, whereas smaller values improve the local search with faster convergence. Different approaches are reported in literature to improve PSO by modifying the inertia weight. This work analyzes the performance of the existing modified PSO algorithms with different time-varying inertia weight strategies and proposes two new strategies. Detailed simulations present the enhanced performance characteristics of the proposed algorithms in transversal and decision feedback models. Also the simulation work analyzes the performance in linear and nonlinear channel conditions.