Adaptive Technique for CI/MC-CDMA System using Combined Strategy of Genetic Algorithms and Neural Network

Abstract

Multicarrier Code Division Multiple Access (MC-CDMA) is one of the most promising techniques for high bit rate and high user capacity transmission in future broadband mobile services. The use of carrier interferometry (CI) codes further improves this user capacity relative to the conventional spreading codes. Genetic Algorithms (GA) may be used to find the optimum transmitted powers that maximize the channel transmission capacity as well as to reduce bit error rate (BER) values. On the other hand, neural networks (NN) are trained to optimize the weight factors in minimum mean square error combining receiver (MMSEC) via back propagation type algorithm. Optimum values of weight factors give stable decision variables that lead to improved receiver performance without having the knowledge of channel state information (CSI) and transmit signal powers. Decision variables are then used for realization of an efficient block parallel interference cancellation (BPIC) as multiuser detection (MUD).  Simulation results show that BER performance using GA-NN is better than any other existing works.