Abstract
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.
Highlights
In recent years, there has been a tremendous development in the wireless communication [1]–[3], and the wireless data rate has been increasing rapidly
The training set is used as the input data for the model to predict the channel information, and we calculate the error between the predicted channel information and observed channel information by using the criterion of the normalized mean square error (NMSE), which is given by, NMSE =
In this paper, a novel channel prediction model based on deep learning approach was proposed for improving the performance of forecasting the channel state information (CSI) in the field of edge computing networks
Summary
There has been a tremendous development in the wireless communication [1]–[3], and the wireless data rate has been increasing rapidly. The typical applications include vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-infrastructure (V2I), and vehicle-tonetwork (V2N) [12] In these applications, the wireless transmission should provide high data rate services, since the nodes in the network will communicate with each other very frequently. Different from the conventional model-based algorithms, the AI algorithms are data-driven and require a vast number of data to adaptively capture the features inherented in the data In this area, deep learning has been widely used in neural networks [17], [18], and the authors in [18] proposed an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of recurrent neural networks (RNN).
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