Multi-user detection based on neural network for multi-carrier code division multiple access systems

Document Type : Original Article

Authors

1 Department of Electrical and Electronic Engineering, Erciyes University, Kayseri, Turkey

2 Graduate School of Natural and Applied Sciences, Erciyes University, Kayseri, Turkey.

3 Silifke-Taşucu Vocational High School, Selçuk University, ççel, Turkey.

Abstract

Abstract:
In this paper, we present fundemental linear multiuser detection (MUD) techniques and
compare them with the technique based on neural network (NN) in multicarrier code
division multiple access (MC-CDMA) systems. In a MC-CDMA system, increasing
with the number of users, receiver’s bit error rate (BER) performance goes up. Also,
the system’ s performance is effected by the power level differences among the users.
Simulation results demostrate the higher performance of NN receiver compared to
conventional receiver (matched filter) for MC-CDMA. And also, performance results
show that the NN structure, gives nearer results comparing to decorrelator and MMSE
receivers. Simulations implemented in MATLAB program and performances are
examined for synchronous communication and AWGN channels. The Levenberg-
Marquardt algorithm is used as the learning algorithm for NN.

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