Generalization and Construction of Single-Section Sparse Regression Codes
Huiqi Liu, Wai Ho Mow, Shansuo Liang · 2024
As a 5G service category, ultra-reliable low-latency communication (URLLC) raises the challenge of dramatically improving the reliability of short message transmission, for which sparse regression codes (SRCs) and their variations have emerged as promising solutions. In this paper, we propose a generalization of single-section SRCs (SRCl) by designing a sparse vector set that satisfies a certain minimum Euclidean distance constraint. The design problem is first transformed into a constant weight code (CWC) design problem. By extending the binary alphabet to an$M$-ary-phase alphabet, we generalize the CWC to$M$-ary CWC ($M$-CWC) to further increase the achievable minimum Euclidean distance. The increment is theoretically analyzed, and the anticipated performance gain of the resultant$M$-CWC-SRCI over SRCI is verified by simulation. Additionally, our simulation results show that the proposed$M$-CWC-SRCI outperforms the state-of-the-art SRCl-based schemes by about 1 dB gain in EblNo at BLER of Le - 5.