A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks

TL;DR

This paper introduces an end-to-end deep learning framework for SCMA that jointly optimizes codebook design and decoding, resulting in better error rates and lower complexity for downlink communication.

Contribution

It proposes a novel autoencoder-based deep learning approach for joint design of SCMA encoder and decoder, enhancing performance over traditional methods.

Findings

Significant error rate reduction compared to conventional SCMA.

Lower computational complexity achieved with the proposed method.

Improved sparse codebook quality through end-to-end learning.

Abstract

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.