A fast adaptive multiuser detector for DS-CDMA communications based on an artificial neural network architecture

A fast training algorithm for artificial neural networks using a feedforward multilayer perceptron architecture is presented. The application of this algorithm to the problem of multiuser detection in the synchronous DS CDMA channel is investigated. The performance of this multiuser detector is shown to be very close to the single user bound. The training algorithm is compared with the conventional gradient descent-backpropagation algorithm. It is demonstrated that the new training algorithm converges significantly faster than the backpropagation algorithm, while keeping the performance constant. Finally, a pre-training algorithm is proposed in order to further reduce the length of the required training sequence.