References & Citations
Computer Science > Information Theory
Title: Approaching Maximum Likelihood Decoding Performance via Reshuffling ORBGRAND
(Submitted on 29 Jan 2024 (v1), last revised 28 Apr 2024 (this version, v3))
Abstract: Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance loss in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.
Submission history
From: Li Wan [view email][v1] Mon, 29 Jan 2024 08:13:14 GMT (469kb,D)
[v2] Tue, 30 Jan 2024 07:14:12 GMT (468kb,D)
[v3] Sun, 28 Apr 2024 08:52:14 GMT (494kb)
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