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.

翻译：低复杂度多输入多输出（MIMO）检测仍然是现代无线系统中的关键挑战，尤其对于5G轻量化（RedCap）和物联网（IoT）设备而言。在此背景下，尽管在边缘设备上部署机器学习的兴趣日益增长，但仍需在严格的计算复杂度和内存限制之间取得平衡，同时支持高阶调制。除了精确的硬判决外，可靠的软信息同样至关重要，因为现代接收机依赖于软输入信道解码，这对检测器设计提出了额外要求。在本工作中，我们提出了recurSIC，一个轻量级的基于学习的MIMO检测框架。该框架在结构上受到连续干扰消除（SIC）的启发，并融入了学习处理阶段。它通过具有可调复杂度参数的多路径假设跟踪来生成可靠的软信息，同时仅需单次前向传播和极少的参数量。在现实无线场景中的数值结果表明，recurSIC以极低的复杂度实现了优异的硬判决和软检测性能，使其非常适合受限于边缘条件的MIMO接收机。