Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles
With the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, wh...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8805349/ |
| _version_ | 1818618455948001280 |
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| author | Guangqun Liu Yan Xu Zongjiang He Yanyi Rao Junjuan Xia Liseng Fan |
| author_facet | Guangqun Liu Yan Xu Zongjiang He Yanyi Rao Junjuan Xia Liseng Fan |
| author_sort | Guangqun Liu |
| collection | DOAJ |
| description | With the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, which exploits the computation ability of edge nodes at the cost of wireless transmission. Hence, it is of vital importance to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance. To fulfil this challenge, this paper proposes a novel prediction model based on long short-term memory (LSTM) network, which is powerful in capturing valuable information in the sequence and hence is good at analyzing the spatio-temporal correlation in the channel parameters. To validate the proposed model, we conduct extensive simulations to show that the proposed model is quite effective in the channel prediction. In particular, the proposed model can outperform the conventional ones substantially. |
| first_indexed | 2024-12-16T17:21:52Z |
| format | Article |
| id | doaj.art-b8ea60816a9e4e1e80fac8842b2a7dcb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:21:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b8ea60816a9e4e1e80fac8842b2a7dcb2022-12-21T22:23:09ZengIEEEIEEE Access2169-35362019-01-01711448711449510.1109/ACCESS.2019.29354638805349Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected VehiclesGuangqun Liu0Yan Xu1https://orcid.org/0000-0002-7895-6534Zongjiang He2Yanyi Rao3Junjuan Xia4https://orcid.org/0000-0003-2787-6582Liseng Fan5School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaWith the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, which exploits the computation ability of edge nodes at the cost of wireless transmission. Hence, it is of vital importance to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance. To fulfil this challenge, this paper proposes a novel prediction model based on long short-term memory (LSTM) network, which is powerful in capturing valuable information in the sequence and hence is good at analyzing the spatio-temporal correlation in the channel parameters. To validate the proposed model, we conduct extensive simulations to show that the proposed model is quite effective in the channel prediction. In particular, the proposed model can outperform the conventional ones substantially.https://ieeexplore.ieee.org/document/8805349/Vehicular networkedge computingchannel predictionLSTM network |
| spellingShingle | Guangqun Liu Yan Xu Zongjiang He Yanyi Rao Junjuan Xia Liseng Fan Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles IEEE Access Vehicular network edge computing channel prediction LSTM network |
| title | Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles |
| title_full | Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles |
| title_fullStr | Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles |
| title_full_unstemmed | Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles |
| title_short | Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles |
| title_sort | deep learning based channel prediction for edge computing networks toward intelligent connected vehicles |
| topic | Vehicular network edge computing channel prediction LSTM network |
| url | https://ieeexplore.ieee.org/document/8805349/ |
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