Deep Unfolding for MIMO Signal Detection
This provides a practical solution for next-generation massive MIMO systems, though it appears incremental as it builds on prior deep unfolding methods.
The paper tackles MIMO signal detection by proposing a deep unfolding neural network with complex-valued computations using Wirtinger calculus, achieving superior detection performance with fewer iterations and lower computational complexity.
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.