Identification of nonlinear communication channel using evolutionary Volterra time-series

Building a suitable model for a nonlinear channel is a major problem for communication systems. Finding a suitable model for the channel depends mainly on the type of nonlinearity of the channel and the approach to which the model parameters are estimated. Traditional approaches for parameter identification have difficulty in estimating nonlinear system parameters with a limited number of measurements. We explore the use of evolutionary strategies (ESs) as the key search approach of a methodology for estimating the parameters of the discrete Volterra time series to model nonlinear communication channels. An example is presented to illustrate the effectiveness of the proposed approach as compared to the least squares approach.