A novel MLP based on compensation method for the effects of High Power Amplifier N onlinearities in Non-Linear SCMA systems

As Sparse code multiple access (SCMA) has proved to be a fascinating research in order to meet the requirements of future wireless communication systems. To reach high power efficiency, wireless communication systems are equipped with high power amplifiers (HPAs). In this paper, we investigate the effects of distortions due to high power amplifiers (HPA) nonlinearities. We study the performance of amplified SCMA systems, in terms of bit error rate (BER). Message passing algorithm (MPA) is considered for SCMA detectors. BER performance is derived and evaluated for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Numerical results and comparisons are provided for several system parameters, such as the input back-off (IBO). Indeed, we propose a new distortion cancellation technique based on feed-forwarded neural networks (FNNs) to restore the system performance via eliminating the HPA nonlinearities at transmitter and receiver sides. It is confirmed that the proposed pre-distorter and post-distorter with neural network exhibit a good performance improvement of quality of the transmission. Specifically, post-distortion based on NNs shows a better BER performance, which is almost close to the one of the linear system.