Neural Networks for Interferences Suppression in DS/CDMA with Rayleigh Fading Channel and Power Control Error

Abstract

The exponential chip weighting waveforms have been designed with the purpose of multiple access interferences (MAI) rejection based on the concept that the optimum despreading sequence in reference emphasizes the transitions in the received spreading signal of interest. The despreading sequence weighted by exponential chip waveforms was determined by only one parameter. The objective of this study, was to introduce neural networks (NN) to facilitate the computing of the bit error rate (BER) performance of a direct sequence code division multiple access (DS/CDMA) system over a Rayleigh multipath fading with power control error, for both coherent and noncoherent receivers, by producing the despreading sequences weighted (WDS) by exponential chip weighting waveforms. Numerical results show that the parameter values of the exponential chip weighting waveforms produced by the proposed network are nearly optimal and satisfactory in viewpoint of the achieved bit error rate (BER) performance.