发表机构
Northeastern University; Massachusetts Institute of Technology（东北大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出软输出GRAND-AM（SOGRAND-AM），一种宏符号级联合多用户检测与解码框架，生成校准的逐位后验错误概率，支持软判决外解码器，相比硬输入GRAND-AM在误码率上获得约2分贝增益。
AI 中文摘要
猜测随机加性噪声解码辅助宏符号（GRAND-AM）为非正交多址（NOMA）系统提供了一种基于编码的解决方案，实现了联合多用户检测和纠错。GRAND-AM在符号错误率方面优于先前方法，同时提供硬检测输出。我们引入了软输出GRAND-AM（SOGRAND-AM），这是一种宏符号级联合多用户检测与解码框架，可生成校准的逐位后验错误概率。SOGRAND-AM扩展了GRAND-AM，通过启用软判决外前向纠错解码器，与现有解码器透明集成。与使用GRAND-AM的硬输入相比，SOGRAND-AM在外软输入解码器输出端的误码率上实现了约2分贝的改进。
英文摘要
Guessing Random Additive Noise Decoding-Aided Macrosymbol (GRAND-AM) provides a coding-based solution for non-orthogonal multiple access (NOMA) systems, enabling joint multiuser detection and error correction. GRAND-AM outperforms prior methods in symbol error rates while providing hard-detection output. We introduce soft-output GRAND-AM (SOGRAND-AM), a macrosymbol-level joint multiuser detection and decoding framework which generates calibrated bitwise a posteriori error probabilities. SOGRAND-AM extends GRAND-AM by enabling soft-decision outer forward error correction decoders, transparently integrating with existing decoders. SOGRAND-AM achieves approximately 2 dB improvement in bit error rate at the output of the outer soft-input decoder compared to hard input with GRAND-AM.
