用于单通道同频信号分离的低复杂度序贯检测框架
Low-Complexity Sequential Detection Framework for Single-Channel Co-Frequency Signal Separation
- School of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对单通道同频信号分离的高复杂度问题，本文提出基于序贯检测的低复杂度框架及SDS、SO-SDS算法，可在保证性能或仅适度损失LLR精度的前提下降低计算复杂度，且优势随调制阶数提升而增大。
AI中文摘要：
单通道同频信号（SCCFSs）的实际分离受限于基准算法过高的计算复杂度。为解决该问题，本文提出一种基于序贯检测（SD）的低复杂度分离框架，将信号分离转化为网格图上的序贯路径搜索问题。为支持硬判决检测和对数似然比（LLR）提取，本文在该框架内开发了两种算法：基于序贯检测的分离（SDS）算法及其软输出变体（SO-SDS）。此外，SDS采用加窗策略结合动态剪枝，将计算资源集中于高概率路径，从而高效检测传输符号序列。基于SDS的SO-SDS进一步引入状态完备性验证机制（SCVM）以估计比特LLR，为后续软解码提供支持。数值结果表明，与基准算法相比，SDS在不降低分离性能的前提下实现了显著的复杂度降低，而SO-SDS则在仅出现适度LLR精度损失的情况下获得了可观的计算量节省。值得注意的是，所提算法相对于基准算法的计算复杂度优势会随调制阶数的提高而大幅增长。
英文摘要：
Practical separation of single-channel co-frequency signals (SCCFSs) is hindered by the prohibitive computational complexity of benchmark algorithms. To address this issue, we propose a low-complexity separation framework based on sequential detection (SD), in which signal separation is cast as a sequential path search over a trellis. To support both hard-decision detection and log-likelihood ratio (LLR) extraction, we develop two algorithms within this framework: the SD-based separation (SDS) algorithm and its soft-output variant (SO-SDS). Furthermore, SDS employs a windowing strategy combined with dynamic pruning to concentrate computational resources on high-probability paths, thereby enabling efficient detection of transmitted symbol sequences. Building upon SDS, SO-SDS further incorporates a state completeness verification mechanism (SCVM) to estimate bit LLRs, thus facilitating subsequent soft decoding. Numerical results show that, compared to benchmark algorithms, SDS achieves significant complexity reduction without degrading separation performance, while SO-SDS offers notable computational savings with only modest LLR accuracy loss. Notably, the computational complexity advantage of the proposed algorithms over benchmark algorithms grows substantially with increasing modulation order.
