Algorithm Parameters Selection Method With Deep Learning for EP MIMO Detector

Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector achieves exceptional performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, based on our studies, the EP MIMO detector cannot achieve superior performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection on the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). To obtain the initial variance and damping factors which lead to better performance, we adopt a deep learning scheme, the iterative process of MEPD is unfolded to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios.

Automated summary

An EP-based MIMO detector may not achieve superior performance due to empirical parameter selection, thus a modified EP MIMO detector (MEPD) with deep learning parameter selection is proposed. By training the parameters offline, MEPD outperforms the original detector in various MIMO scenarios and shows better robustness in practical scenarios.