Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles

• Main Authors: Guangqun Liu, Yan Xu, Zongjiang He, Yanyi Rao, Junjuan Xia, Liseng Fan
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Vehicular network; edge computing; channel prediction; LSTM network
• Online Access: https://ieeexplore.ieee.org/document/8805349/

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.