A neural network-based receiver for interference cancellation in multi-user environment for DS/CDMA systems

The objective of this work is to apply and investigate the performance of a neural network-based receiver for interference cancellation in multiuser direct sequence code division multiple access (DSCDMA) wireless networks. This research investigates a Receiver model which uses Neural Network receiver in combination with a conventional receiver system to provide an efficient mechanism for the Interference Suppression in DS/CDMA systems. The Conventional receiver is used for the time during which the neural network receiver is being trained. Once the NN receiver is trained the conventional receiver system is deactivated. It is demonstrated that this receiver when used along with an efficient Neural network model can outperform MMSE receiver or DFFLE receiver with significant advantages, such as improved bit-error ratio (BER) performance, adaptive operation, single-user detection in DS/CDMA environment and a near far resistant system.