SCMA Decoding via Deep Learning

Sparse code multiple access (SCMA) has become a highly competitive technology for future cellular systems. For the receiver of the SCMA system, besides the traditional maximum likelihood and message passing algorithm solutions, a deep neural network (DNN) method that causes whirlwinds in image recognition can reduce the computational complexity of the decoder. We expect low complexity while maintaining a satisfactory bit error rate (BER) performance. As shown in our simulations, our proposed solution has better BER performance and lower computational complexity than other previously studied DNN solutions.