用轻量学习框架实现低复杂度高可靠MIMO检测，适合边缘设备
Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection
- 基于SIC结构设计，通过可学习阶段生成软信息
- 单次前向传播，参数少，在真实场景中性能强
- 适合5G RedCap和物联网等资源受限设备
低复杂度多输入多输出（MIMO）检测仍是现代无线系统的关键挑战，尤其在5G缩减能力（RedCap）和物联网（IoT）设备中。随着边缘设备上部署机器学习的需求增长，必须在计算复杂度和内存约束下支持高阶调制，同时保证高精度硬检测与可靠的软信息输出。本文提出recurSIC，一种轻量级学习型MIMO检测框架，其结构受逐次干扰消除（SIC）启发，引入可学习处理阶段，通过多路径假设追踪生成可靠软信息，且仅需一次前向传播和极少参数。在真实无线场景中的数值结果表明，recurSIC在极低复杂度下实现了优异的硬检测与软检测性能，非常适合资源受限的边缘MIMO接收机。
原文摘要 · Abstract (English)
Low-complexity multiple-input multiple-output (MIMO) detection remains a key challenge in modern wireless systems, particularly for 5G reduced capability (RedCap) and internet-of-things (IoT) devices. In this context, the growing interest in deploying machine learning on edge devices must be balanced against stringent constraints on computational complexity and memory while supporting high-order modulation. Beyond accurate hard detection, reliable soft information is equally critical, as modern receivers rely on soft-input channel decoding, imposing additional requirements on the detector design. In this work, we propose recurSIC, a lightweight learning-based MIMO detection framework that is structurally inspired by successive interference cancellation (SIC) and incorporates learned processing stages. It generates reliable soft information via multi-path hypothesis tracking with a tunable complexity parameter while requiring only a single forward pass and a minimal parameter count. Numerical results in realistic wireless scenarios show that recurSIC achieves strong hard- and soft-detection performance at very low complexity, making it well suited for edge-constrained MIMO receivers.
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