GRAND-Assisted Random Linear Network Coding in Wireless Broadcasts

作者：Rina Su, Qifu Tyler Sun, Mingshuo Deng, Jinhong Yuan · 发表于：IEEE Transactions on Communications · 年份：2025 · DOI：10.1109/tcomm.2025.3631721 · 被引用次数：2 · 研究领域：Cooperative Communication and Network Coding、Wireless Networks and Protocols、Mobile Ad Hoc Networks

In the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over GF(2L) is known to asymptotically achieve the optimal completion delay with increasingL. Effective utilization of guessing random additive noise decoding (GRAND) at the physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only a few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over GF(2L) forL≥ 1. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. Moreover, we propose a novel GRAND-assisted GF(2L)-RLNC scheme whose computational overhead introduced by GRAND is negligible. In particular, a subset of GF(2L) is carefully designed for random coding coefficient selection. For the novel scheme, we theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay. Numerical results demonstrate not only a reduction in average completion delay for the novel scheme, but also the advantage of random coding coefficient selection from the specially designed subset for GRAND-assisted RLNC schemes.