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Enhanced GCD through ORBGRAND-AI: Exploiting Partial and Total Correlation in Noise

Jiewei Feng,Ken R. Duffy,Muriel M'edard

Published 2025 in Unknown venue

ABSTRACT

There have been significant advances in recent years in the development of forward error correction decoders that can decode codes of any structure, including practical realizations in synthesized circuits and taped out chips. While essentially all soft-decision decoders assume that bits have been impacted independently on the channel, for one of these new approaches it has been established that channel dependencies can be exploited to achieve superior decoding accuracy, resulting in Ordered Reliability Bits Guessing Random Additive Noise Decoding Approximate Independence (ORBGRAND-AI). Building on that capability, here we consider the integration of ORBGRAND-AI as a pattern generator for Guessing Codeword Decoding (GCD). We first establish that a direct approach delivers mildly degraded block error rate (BLER) but with reduced number of queried patterns when compared to ORBGRAND-AI. We then show that with a more nuanced approach it is possible to leverage total correlation to deliver an additional BLER improvement of around 0.75 dB while retaining reduced query numbers.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-11-10

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    arXiv 2511.07376
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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