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.

Automated summary

SCMA has emerged as a competitive technology for future cellular systems. By utilizing a DNN method, the computational complexity of the decoder can be reduced, resulting in better BER performance and lower complexity compared to other DNN solutions previously studied.