Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit their data simultaneously over a common wireless communication channel,by spreading each data by distinct codes. At the receiver, the individual data are detected by appropriate decoding. In this paper, a new smart receiver is proposed for detecting DS-CDMA signals based on a multi-layer Feed Forward Neural Network (FFNN). The proposed receiver detects the transmitted data when the received signal is distorted due to channel noise, near-far effect and Rayleigh fading. The channel state information is indirectly captured during the training of the FFNN and hence the conventional channel state estimation using pilot signal or training sequences is eliminated. Experimental results show that the performance of the proposed receiver in terms of detection accuracy is superior to similar competitive demodulators.
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