Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Abstract

Sparse code multiple access (SCMA) is a promising non‐orthogonal multiple access scheme for cellular Internet of things (IoT) due to its ability to support massive connectivity, grant‐free transmission and scalability. Inspired by the recent developments of deep learning for physical layer communications, we present a design of an uplink SCMA receiver using deep learning. We propose the use of recurrent neural networks (RNNs) for joint channel estimation and multiuser data detection of uplink SCMA under time‐varying Rayleigh channel using a single deep learning structure. The use of RNNs enables the receiver to learn the time correlation between the received samples with a very low pilot density and with low complexity. Compared with the conventional SCMA receiver, the simulation results show that the proposed deep learning‐based receiver can achieve BER performance similar to that of the conventional SCMA receivers (such as sparse pilot channel estimator and message‐passing algorithm detector) with very low pilot density and with much lower complexity. Moreover, the proposed SCMA receiver shows good resilience to small changes in the receiver speed (second‐order channel statistics) which enables the proposed deep learning receiver to work over a reasonable range of receiver speeds without parameter tuning. Fine tuning the network parameters to capture the variations of the channel, during online transmission using small data sets and small training period, is also checked and provides additional BER benefits.