Algorithm Parameters Selection Method With Deep Learning for EP MIMO Detector
| Autor: | Jianhao Hu, Hang Chen, Guoqiang Yao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Iterative and incremental development
Computer Networks and Communications business.industry Computer science Deep learning Detector MIMO Aerospace Engineering Approximation algorithm Moment (mathematics) Expectation propagation Automotive Engineering Artificial intelligence Electrical and Electronic Engineering business Algorithm Selection (genetic algorithm) Computer Science::Information Theory |
| Zdroj: | IEEE Transactions on Vehicular Technology. 70:10146-10156 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2021.3103568 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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