感知辅助有序可靠性比特猜测随机加性噪声解码
Sensing-Aided Ordered Reliability Bits Guessing Random Additive Noise Decoding
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中文总结 AI 辅助
本文针对单输入单输出窄带衰落信道，提出感知辅助ORBGRAND解码方法，通过融合环境信息与导频观测优化LLR计算，可降低块错误率与平均查询复杂度，导频受限场景下增益显著。
中文摘要 AI 辅助
集成感知与通信（ISAC）是未来无线系统的关键使能技术，可提供环境信息以支持传统数据传输之外的任务，但其对信道解码的影响仍鲜有研究。本文研究单输入单输出窄带衰落信道下感知辅助有序可靠性比特猜测随机加性噪声解码（ORBGRAND），利用环境信息构建基于几何的信道系数先验，通过线性最小均方误差（LMMSE）估计将其与导频观测值融合，所得后验信道估计值与不确定性用于计算提供给ORBGRAND的对数似然比（LLR），优化驱动其噪声猜测过程的可靠性排序。仿真结果表明，该方法可降低块错误率、减少平均查询复杂度，在导频受限场景下增益最大。
英文摘要
Integrated sensing and communication (ISAC) is a key enabler for future wireless systems, providing environmental information that can support tasks beyond conventional data transmission. However, its impact on channel decoding remains less explored. This paper studies sensing-aided ordered reliability bits guessing random additive noise decoding (ORBGRAND) over single-input single-output narrowband fading channels. Environmental information is used to construct a geometry-based prior for the channel coefficient, which is fused with pilot observations via linear minimum mean square error (LMMSE) estimation. The resulting posterior channel estimate and uncertainty are used to compute the log-likelihood ratios (LLRs) supplied to ORBGRAND, improving the reliability ordering that drives its noise-guessing process. Simulation results demonstrate improved block error rate and reduced average query complexity, with the largest gains in pilot-limited regimes.
