Joint detection, channel estimation, and interference cancellation in downlink MC-CDMA communication systems using complex-valued multilayer neural networks

Data detection in the presence of interference is one of the main challenges in multicarrier code division multiple access (MC-CDMA) communication systems. In this paper, a new detection technique for downlink MC-CDMA systems is proposed. This technique uses complex-valued multilayer neural networks at the receiver side. With the new definition for desired responses (±(1+J) instead of ±1, where \( J = \sqrt {{ - 1}} \)), the convergence rate is increased (in the training process) regarding to saturation of imaginary output and the performance is increased because of increasing Euclidean distance of output neuron inputs in two states of desired outputs (with factor of \( \sqrt {2} \)). The performance of the proposed method is improved further by considering two various saturation coefficients (in the activation function of output layer) in the training and test processes. Since the last performance improving lead to low convergence rate, this effect is compensated by correcting the coefficient of training rate in the output layer. Simulation results confirm the high convergence rate, low computational complexity, and also good performance of the proposed method in wide range of SNRs.