A Knowledge-Based Deep Learning Detection Scheme for Downlink SCMA Systems

Abstract

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access technology for future wireless communication systems. In the SCMA detector, deep learning (DL) technology has been adopted to improve the detection efficiency. However, most previous schemes are completely data-driven designed without using prior knowledge. In this letter, we propose a knowledge-based deep learning detection scheme (K-DLD) that incorporates prior knowledge into SCMA detection to lighten the neural network. Moreover, we propose to use a thinner but deeper network to further reduce the model complexity. Simulation results show that the proposed schemes significantly reduce the computation time without any performance loss.