Krusienski, D.J. (2004) Enhanced Structured Stochastic Global Optimization Algorithms for IIR and Nonlinear Adaptive Filtering. Ph.D. Thesis, Department of Electrical Engineering, The Pennsylvania State University, University Park.

has been cited by the following article:

• TITLE: Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

  AUTHORS: D. C. Diana, S. P. Joy Vasantha Rani

  KEYWORDS: Adaptive Channel Equalization, Decision Feedback Equalizer, Inertia Weight, Mean Square Error, Particle Swarm Optimization

  JOURNAL NAME: Circuits and Systems, Vol.7 No.12, October 31, 2016

  ABSTRACT: 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.