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.

翻译：已有研究提出利用码本结构来降低二元线性码的猜测随机加性噪声解码（GRAND）的猜测工作量，但代价是牺牲了块错误率（BLER）性能。本文证明，通过基于软输出GRAND的错误概率估计进行动态列表解码并适时终止，可以在保持猜测工作量减少的同时消除BLER性能的下降。我们采用一种受已发表文献启发的方法来阐述该方案，并将其性能与猜测码字解码（GCD）进行比较。结果表明，该方法在提供与GCD相同BLER性能的同时，能将猜测工作量最多降低32倍。