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Multi-Layer Perceptrons and Conventional Adaptive Filters for Channel Estimation in CDMA System

In this paper, two different approaches multi-layer perceptron (MLP) neural network (NN) with back propagation (BP) learning algorithm and conventional adaptive filters are tested and compared in AWGN channel for code division multiple accesses (CDMA). In this work direct-sequence (DS) CDMA with binary phase shift keying (BPSK) modulation transmitted over complex base-band channel for real part of the matched-filter (MF) output and not the MF output itself should be use as sufficient statistics for further processing. Based on this observation, we drive novel improved bit error rate (BER) and mean square error (MSE) in MLP NN receiver. A pseudo noise (PN) has produced by stochastic statistical properties and PN is changed in duration of each symbol. Results show that MLP-NN has better performance than other proposed MFs in which training sequence is transmitted at the beginning of a session continuously and the initial estimation of the channel parameters is performed using the received data symbols.