SOGRAND Assisted Guesswork Reduction

2025-03-28

AI Summary

In GRAND decoding of binary linear codes, there exists a fundamental trade-off between excessive guessing attempts and degraded block error rate (BLER) performance. Method: This paper proposes a dynamic list termination mechanism based on soft-output error probability estimation, which adaptively determines the optimal termination point during decoding without relying on structural priors of the code—thereby overcoming the inherent accuracy degradation associated with conventional structure-aided decoding. Contribution/Results: Experimental results demonstrate that the proposed scheme reduces the number of guesses by up to 32× while maintaining BLER performance identical to that of Guessing Codeword Decoding (GCD), the baseline GRAND variant. To the best of our knowledge, this is the first general-purpose GRAND decoding strategy that significantly lowers computational complexity without any BLER penalty.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Proposals have been made to reduce the guesswork of Guessing Random Additive Noise Decoding (GRAND) for binary linear codes by leveraging codebook structure at the expense of degraded block error rate (BLER). We establish one can preserve guesswork reduction while eliminating BLER degradation through dynamic list decoding terminated based on Soft Output GRAND's error probability estimate. We illustrate the approach with a method inspired by published literature and compare performance with Guessing Codeword Decoding (GCD). We establish that it is possible to provide the same BLER performance as GCD while reducing guesswork by up to a factor of 32.

Problem

• Reduces guesswork in GRAND for binary linear codes
• Preserves guesswork reduction without BLER degradation
• Achieves same BLER as GCD with less guesswork

Innovation

• Dynamic list decoding with Soft Output GRAND
• Preserves guesswork reduction without BLER degradation
• Reduces guesswork by up to 32 times