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...

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Vydané v:IEEE transactions on vehicular technology Ročník 70; číslo 10; s. 10146 - 10156
Hlavní autori: Chen, Hang, Yao, Guoqiang, Hu, Jianhao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Approximation algorithms
Complexity theory
Damping
Deep learning
Detectors
expectation propagation
Machine learning
Massive MIMO
MIMO detection
Parameter robustness
parameters selection
Sensors
Simulation
Training
ISSN:0018-9545, 1939-9359
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3103568