Volume 31 Issue 3
May  2022
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LIU Chunhui, WANG Meilin, DONG Zanliang, WANG Pei. Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks[J]. Chinese Journal of Electronics, 2022, 31(3): 430-441. doi: 10.1049/cje.2021.00.285
Citation: LIU Chunhui, WANG Meilin, DONG Zanliang, WANG Pei. Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks[J]. Chinese Journal of Electronics, 2022, 31(3): 430-441. doi: 10.1049/cje.2021.00.285

Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks

doi: 10.1049/cje.2021.00.285
Funds:  This work was supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” (2020AAA0108200)
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  • Author Bio:

    (corresponding author) was born in Tianjin, China, in 1984. He received the B.S., M.S., and Ph.D. degrees from Beihang University, in 2006, 2009, and 2017, respectively. Now, he is an Associate Research Fellow in Institute of Unmanned System, Beihang University. His research interests include wireless communication and artificial intelligence. (Email: liuchunhui2134@buaa.edu.cn)

    was born in Liaoning Province, China, in 1997. She received the B.S. degree from Nanjing University of Aeronautics and Astronautics in 2019. Now, she is a graduate student in Beihang University. Her research interests include wireless communication and artificial intelligence. (Email: 15841627667@buaa.edu.cn)

    was born in Hubei Province, China, in 1996. She received the B.S. degree in electronic engineering from Inner Mongolia University, and now is studying as an M.S. student in electronic engineering at Beihang University. Her research interests include wireless communication technology and communication based on machine learning. (Email: zy2002106@buaa.edu.cn)

    was born in Shaanxi Province, China, in 1997. He received the B.S. degree from Nanjing University of Aeronautics and Astronautics in 2019. Now, he is a graduate student in Beihang University. His research interests include wireless communication and artificial intelligence. (Email: 15852920896@163.com)

  • Received Date: 2021-08-13
  • Accepted Date: 2022-01-10
  • Available Online: 2022-03-04
  • Publish Date: 2022-05-05
  • To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.
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