The Rayleigh Fading Channel Prediction via Deep Learning
This paper presents a multi-time channel prediction system based on backpropagation (BP) neural network with multi-hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Mea...
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| Veröffentlicht in: | Wireless communications and mobile computing 2018-01, Vol.2018 (2018), p.1-11 |
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| container_issue | 2018 |
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| container_title | Wireless communications and mobile computing |
| container_volume | 2018 |
| creator | Pan, Fei Song, Huanhuan Wu, Jinsong Wen, Hong Liao, Run-Fa Dong, Lian |
| description | This paper presents a multi-time channel prediction system based on backpropagation (BP) neural network with multi-hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances. |
| doi_str_mv | 10.1155/2018/6497340 |
| format | Article |
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| ispartof | Wireless communications and mobile computing, 2018-01, Vol.2018 (2018), p.1-11 |
| issn | 1530-8669 1530-8677 |
| language | eng |
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| source | Wiley-Blackwell Open Access Collection; Alma/SFX Local Collection; EZB Electronic Journals Library |
| subjects | Algorithms Back propagation Computer simulation Cooperation Deep learning Machine learning MIMO (control systems) Neural networks Noise control Pattern recognition Power control Propagation Wireless networks |
| title | The Rayleigh Fading Channel Prediction via Deep Learning |
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