Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques
2016
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 search abilities of PSO are managed by the key parameter
Inertia Weight (IW). A higher value leads to global search whereas a smaller
value shifts the search to local which makes convergence faster. Different
approaches are reported in literature to improve PSO by modifying inertia
weight. This work investigates the performance of the existing PSO variants
related to time varying inertia weight methods and proposes new strategies to
improve the convergence and mean square error of channel equalizer. Also the
position update method in PSO is modified to achieve better convergence in
channel equalization. The simulation presents the enhanced performance of the
proposed techniques in transversal and decision feedback models. The simulation
results also analyze the superiority in linear and nonlinear channel
conditions.
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