用复数域计算提升大规模MIMO信号检测效率与精度
Deep Unfolding for MIMO Signal Detection
- 基于复数域的深度展开网络，直接处理复信号
- 仅需少量可训练参数，迭代次数少且计算量低
- 适合下一代大规模MIMO系统部署
本文提出一种基于深度展开神经网络的MIMO检测方法，采用Wirtinger微积分实现复数域运算。该方法称为动态部分收缩阈值（DPST），可在保持高可解释性的同时实现高效、低复杂度的信号检测。不同于以往依赖实数近似的方案，本方法直接在复数域操作，更贴合信号处理本质。所提算法仅需少量可训练参数，便于训练。数值结果表明，该方法在较少迭代次数下即可实现更优检测性能，同时显著降低计算复杂度，适用于下一代大规模MIMO系统。
原文摘要 · Abstract (English)
In this paper, we propose a deep unfolding neural network-based MIMO detector that incorporates complex-valued computations using Wirtinger calculus. The method, referred as Dynamic Partially Shrinkage Thresholding (DPST), enables efficient, interpretable, and low-complexity MIMO signal detection. Unlike prior approaches that rely on real-valued approximations, our method operates natively in the complex domain, aligning with the fundamental nature of signal processing tasks. The proposed algorithm requires only a small number of trainable parameters, allowing for simplified training. Numerical results demonstrate that the proposed method achieves superior detection performance with fewer iterations and lower computational complexity, making it a practical solution for next-generation massive MIMO systems.
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