Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection

• URL: http://arxiv.org/abs/2601.16586v1
• Date: Fri, 23 Jan 2026 09:37:51 GMT
• Title: Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection
• Abstract summary: recurSIC is a lightweight learning-based multiple-input multiple-output (MIMO) detection framework.<n>It generates reliable soft information via multi-path hypothesis tracking with a tunable complexity parameter.<n> Numerical results in realistic wireless scenarios show that recurSIC achieves strong hard- and soft-detection performance at very low complexity.
• Score: 4.941630596191806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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