Improved Sparse Vector Code Based on Optimized Spreading Matrix for Short-Packet URLLC in mMTC

TL;DR

This paper enhances sparse vector coding for short-packet URLLC in mMTC by optimizing the spreading matrix using greedy algorithms to reduce mutual coherence, resulting in significantly improved decoding performance.

Contribution

It introduces two greedy algorithms to optimize the spreading matrix in SVC, specifically constraining it to bipolar entries for practical hardware implementation.

Findings

Improved block error rate (BLER) performance with optimized matrices.

Optimized matrices are efficient for storage and hardware realization.

Significant performance gains over existing methods.

Abstract

Recently, the sparse vector code (SVC) is emerging as a promising solution for short-packet transmission in massive machine type communication (mMTC) as well as ultra-reliable and low-latency communication (URLLC). In the SVC process, the encoding and decoding stages are jointly modeled as a standard compressed sensing (CS) problem. Hence, this paper aims at improving the decoding performance of SVC by optimizing the spreading matrix (i.e. measurement matrix in CS). To this end, two greedy algorithms to minimize the mutual coherence value of the spreading matrix in SVC are proposed. Specially, for practical applications, the spreading matrices are further required to be bipolar whose entries are constrained as +1 or -1. As a result, the optimized spreading matrices are highly efficient for storage, computation, and hardware realization. Simulation results reveal that, compared with the existing work, the block error rate (BLER) performance of SVC can be improved significantly with the optimized spreading matrices.