Deep Unfolding for MIMO Signal Detection

• URL: http://arxiv.org/abs/2507.21152v1
• Date: Thu, 24 Jul 2025 00:48:04 GMT
• Title: Deep Unfolding for MIMO Signal Detection
• Authors: Hangli Ge, Noboru Koshizuka,
• Abstract summary: We propose a deep unfolding neural network-based MIMO detector that incorporates complex-valued computations using Wirtinger calculus.<n>The proposed algorithm requires only a small number of trainable parameters, allowing for simplified training.<n> Numerical results demonstrate that the proposed method achieves superior detection performance with fewer iterations and lower computational complexity.
• Score: 1.6881346757176976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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