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A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network

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Abstract

The accurate channel state information (CSI) is important to realize the high-reliability and high-efficiency transmission. So far, most of the conventional methods reconstruct CSI on non-RS position by the interpolation. However, high-speed mobility leads to the significant nonlinear channel variation and the performance of conventional methods is so poor that the reliability of transmission deteriorates a lot. In this paper, a novel real-time channel prediction algorithm based on convolutional neural network (CNN) is proposed, which uses the latest reference signal (RS) for online training and extracts the temporal features of channel, followed by prediction employing the optimal model. For high-speed moving scenario, the proposed algorithm is conducted in Orthogonal Frequency Division Multiplexing (OFDM) systems, e.g., long-term evolution (LTE) and fifth generation (5G) systems to track the fast time-varying and non-stationary channel via the real-time RS-based training algorithm, and obtains the accurate CSI without modification of the radio frame. Evaluated by experiments, the proposed algorithm outperforms conventional methods a lot, and more improvement could be achieved in the higher speed.

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Acknowledgements

This work was supported by Fundamental Research Funds for the Central Universities (2020JBZD005), the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010157002, the Royal Society Newton Advanced Fellowship under Grant NA191006 the Open Research Fund from Shenzhen Research Institute of Big Data under Grant No.2019ORF01006 and NSFC under Grant 61725101 and Grant U1834210.

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Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

    Lei Xiong

  2. Frontiers Science Center for Smart High-speed Railway System, Beijing, China

    Lei Xiong

  3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

    Zhengyu Zhang & Dongpin Yao

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  1. Lei Xiong
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  2. Zhengyu Zhang
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  3. Dongpin Yao
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Correspondence to Zhengyu Zhang.

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Xiong, L., Zhang, Z. & Yao, D. A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network. Wireless Netw 28, 621–634 (2022). https://doi.org/10.1007/s11276-021-02849-y

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