AI 中文总结
研究在Turbo积解码中减少猜测工作量问题，提出GSegGRAND这一广义分段GRAND方法，基于新校验结构和变换纳入更多约束，比原方法额外减少75%猜测工作量，应用于Turbo积解码时可减少88%，有望用于未来通信系统低延迟解码。
AI 中文摘要
猜测随机加性噪声解码（GRAND）可通过噪声效应猜测以接近最大似然（ML）性能有效解码任何适度冗余码。对于二元线性码，Rowshan和Yuan的分段GRAND首次表明受限猜测可减少猜测工作量。但其方法需要特定的校验矩阵结构，限制了可利用的约束数量和适用码的类别。本文引入GSegGRAND，它是分段GRAND的推广，克服了其局限性。基于新颖的校验结构和将码映射到该结构的变换，GSegGRAND能有效纳入多达log2(n)个约束，比分段GRAND额外减少75%的猜测工作量。通过扩展软输出GRAND（SOGRAND）以纳入受限猜测，为软输出解码利用这一优势，推导出GSegGRAND的精确软输出（SO）方程。将此SO应用于Turbo积解码，GSegGRAND实现高达88%的猜测工作量减少，使其成为未来通信系统中低延迟解码的有前途候选者。
英文摘要
Guessing random additive noise decoding (GRAND) can efficiently decode any moderately redundant code with near maximum likelihood (ML) performance via noise effect guessing. For binary linear codes, Rowshan and Yuan's Segmented GRAND was the first to show that constrained guessing can reduce guesswork. Although powerful, their approach requires a specific parity-check matrix structure that limits the number of constraints that can be exploited as well as the class of applicable codes. Here we introduce GSegGRAND, a generalization of Segmented GRAND that circumvents its limitations. Built on a novel parity check structure and a transformation that maps codes into this structure, GSegGRAND efficiently incorporates up to log2(n) constraints for a wide range of codes, reducing guesswork by an additional 75% over Segmented GRAND. To leverage that advantage for soft-output decoding, we derive an accurate soft-output (SO) equation for GSegGRAND by extending soft-output GRAND (SOGRAND) to incorporate constrained guessing. Applying this SO to turbo product decoding, GSegGRAND achieves up to 88% guesswork reduction, making it a promising candidate for low-latency decoding in future communication systems.
