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Algorithm Parameters Selection Method With Deep Learning for EP MIMO Detector
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DOI：10.1109/TVT.2021.3103568.png)
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Abstract
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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.
Keywords:
Detectors
Training
Deep learning
Complexity theory
Approximation algorithms
Massive MIMO
Simulation
MIMO detection
expectation propagation
parameters selection
deep learning
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