Guessing Decoding of Short Blocklength Codes
Qianfan Wang, Jifan Liang, Peihong Yuan, Ken R. Duffy, Muriel M\'edard, and Xiao Ma

TL;DR
This paper unifies and analyzes guessing decoding methods like GRAND and GCD for short blocklength codes, providing theoretical insights, optimality proofs, and practical guidelines for ultra-reliable low-latency communication systems.
Contribution
It offers a unified framework for guessing decoding, proves ML optimality, derives approximations, and compares performance metrics of GRAND and GCD.
Findings
Grand and GCD are optimal under certain stopping criteria.
Theoretical predictions match simulation results.
Performance regimes where each method excels are identified.
Abstract
Future beyond-5G and 6G systems demand ultra-reliable, low-latency communication with short blocklengths, motivating the development of universal decoding algorithms. Guessing decoding, which infers the noise or codeword candidate in order of decreasing (exact or approximate) likelihood, offers a universal framework applicable to short codes. In this paper, we present a unified treatment of two prominent recent families of guessing decoding: guessing random additive noise decoding (GRAND) and guessing codeword decoding (GCD). For each, we (i) present algorithmic implementations and ordering strategies; (ii) prove maximum-likelihood (ML) optimality under appropriate stopping criteria; (iii) derive saddle-point approximations for the average number of queries; and (iv) validate theoretical predictions with simulations. We further analyze the performance degradation due to limited search…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Error Correcting Code Techniques · Wireless Communication Security Techniques
