Identification of Nonlinear Communication Channel Using an Novel Particle Swarm Optimization Technique

In this paper, we explore the use of particle swarm optimization (PSO) as the key search approach of a methodology for estimating the parameters of the discrete Volterra time-series to model nonlinear communication channels. Neither the system order nor the number of time delays need to be prespecified a priori in the identification process. The proposed approach exhibits excellent anti-noise character employing a small number of sample data points. Simulation results illustrate the effectiveness of the proposed approach as compared to the genetic algorithm (GA) approach in terms of speed and accuracy.