Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 3, Pages 2392-2399Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3093025
Keywords
Wireless communication; Control systems; Optimization; Planning; Channel models; Probability; Power system reliability; Train control systems; Kalman filter; channel model; network planning; DNN
Categories
Funding
- National Key Research and Development Program of China [2020YFB1313301]
- National Natural Science Foundation of China [61806064, 61733015, 62027809, U1934219]
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With the increasing demand for rail transit, wireless communication technologies are increasingly important in train control systems. This study utilizes deep learning technology to model wireless propagation for improved accuracy and efficiency in railway network planning.
With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.
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