Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design

Abstract

Sparse code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In SCDMA, transmitted symbols from multiple users are coded by their own sparse signature sequences, and a base station attempts to detect those symbols using the signature sequences. In this paper, we present a new deep-unfolded multiuser detector called a complex sparse trainable projected gradient (C-STPG) detector for SCDMA systems. Deep unfolding is a deep learning method that tunes trainable parameters in iterative algorithms using supervised data and deep learning techniques. The proposed detector provides a much superior detection performance over that of the LMMSE detector. Other advantages of the proposed detector include a low computational complexity in execution and a low training cost owing to the small number of trainable parameters. In addition, we propose a novel joint learning strategy called gradual sparsification for designing sparse signature sequences based on deep unfolding. This method is computationally efficient in optimizing a set of sparse signature sequences. Numerical results show that the gradual sparsification successfully yields sparse signature sequences with a smaller symbol error rate than those of randomly designed sparse signature sequences.