SCMA Inspired Sparse Vector Coding: An Enhanced URLLC Transmission Scheme

2026-07-11

AI Summary

This work addresses the insufficient reliability of existing sparse vector coding (SVC) schemes in ultra-reliable low-latency communication (URLLC) and their failure to exploit the inherent sparsity and constellation structure of sparse code multiple access (SCMA). To overcome these limitations, the paper proposes a novel SCMA-inspired SVC scheme, termed SCMA-SVC, which integrates SCMA’s sparse patterns with multidimensional constellation design to enlarge the minimum Euclidean distance of the codebook. Furthermore, random phase rotation is introduced to achieve full diversity gain over Rayleigh fading channels. This approach uniquely incorporates SCMA’s multiuser coding gain and constellation shaping gain into SVC, jointly optimizing structural sparsity and diversity performance. Experimental results demonstrate that the proposed method significantly outperforms existing SVC schemes in both AWGN and Rayleigh fading channels, achieving lower error rates, while its low-complexity near-maximum-likelihood decoder closely approaches optimal performance.

Abstract

Sparsity is inherently exploited in sparse code multiple access (SCMA) and sparse vector coding (SVC), yet the interaction between these two has not been explored before. It is intriguing to ask if one can be used to improve the other, and vice versa. In this work, we present a novel SCMA inspired SVC scheme, called SCMA-SVC, for enhanced ultra-reliable low-latency communications. Our key idea is to exploit the sparse pattern and multidimensional constellation nature of SCMA, with which one is able to further enlarge the minimum Euclidean distance (MED) of the corresponding SVC codebooks. Such an innovation allows us to harvest the multiuser coding gain and the constellation shaping gain which are pertinent to SCMA. Moreover, by applying random phase rotations to the sparse vectors, it is shown that the proposed SCMA-SVC achieves full diversity order over Rayleigh fading channels. Under maximum likelihood (ML) decoding, the proposed SCMA-SVC demonstrates remarkable error rate performances over both Gaussian and Rayleigh fading channels. Additionally, we develop a low-complexity decoder that exploits the structural sparsity of SCMA-SVC while maintaining near-ML performance. Simulation results demonstrate that the proposed SCMA-SVC achieves significantly improved reliability over the existing SVC variants.

Problem

• URLLC
• Sparse Vector Coding
• SCMA
• Reliability
• Diversity

Innovation

• SCMA-SVC
• sparse vector coding
• minimum Euclidean distance
• full diversity
• low-complexity decoding