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

TL;DR

This paper enhances GCD by integrating ORBGRAND-AI, exploiting channel correlations to improve decoding accuracy and reduce pattern queries, achieving significant BLER improvements.

Contribution

It introduces a novel integration of ORBGRAND-AI as a pattern generator for GCD, leveraging partial and total correlation for improved decoding performance.

Findings

Direct approach yields mildly degraded BLER with fewer pattern queries.

Nuanced approach leverages total correlation for an additional 0.75 dB BLER gain.

Reduced number of pattern queries compared to standard ORBGRAND-AI.

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.