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

LIU Chunhui; WANG Meilin; DONG Zanliang; WANG Pei

doi:10.1049/cje.2021.00.285

Volume 31 Issue 3

May 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(3): 430-441

LIU Chunhui, WANG Meilin, DONG Zanliang, et al., “Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 430-441, 2022, doi: 10.1049/cje.2021.00.285

Citation:

LIU Chunhui, WANG Meilin, DONG Zanliang, et al., “Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 430-441, 2022, doi: 10.1049/cje.2021.00.285

Citation:

PDF( 9680 KB)

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

doi: 10.1049/cje.2021.00.285

1.
Institute of Unmanned System, Beihang University, Beijing 100191, China
2.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Funds: This work was supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” (2020AAA0108200)

More Information

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

Abstract

Abstract

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.
- Time-varying channel estimation,
- Orthogonal frequency division multiplexing,
- Multipath effect,
- UAV,
- Air ground channel modelling,
- Deep learning

FullText(HTML)

References(32)

References

[1]	A. G. Burr, “The multipath problem: An overview,” in Proc. of the IEE Colloquium on Multipath Countermeasures, London, UK, pp.1/1−1/7, 1996.
[2]	W. Cong, H. Li, “Communication link of UAV based on OFDM”, 2006 China UAV Conference, Beijing, China, pp.629-633, 2015. (in Chinese)
[3]	Z. Xiao, L. Zhu, and X. G. Xia, “UAV communications with millimeter-wave beamforming: Potentials, scenarios, and challenges,” China Communications, vol.17, no.9, pp.147–166, 2020.
[4]	L. Zhu, J. Zhang, Z. Xiao, et al., “Millimeter-wave full-duplex UAV relay: Joint positioning, beam-forming, and power control,” IEEE Journal on Selected Areas in Communications, vol.38, no.9, pp.2057–2073, 2020.
[5]	L. Zhu, J. Zhang, Z. Xiao, et al., “Millimeter-wave noma with user grouping, power allocation and hybrid beamforming,” IEEE Transactions on Wireless Communications, vol.18, no.11, pp.5065–5079, 2019.
[6]	Zhenyu Xiao, Lipeng Zhu, Yanming Liu, et al., “A Survey on millimeter-wave beamforming enabled UAV communications and networking,” IEEE Communications Surveys & Tutorials, vol.24, no.1, pp.557–610, 2022. doi: 10.1109/COMST.2021.3124512
[7]	T. Hwang, Y. Y. Chen, W. Gang, et al., “OFDM and its wireless applications: A survey,” IEEE Transactions on Vehicular Technology, vol.58, no.4, pp.1673–1694, 2009. doi: 10.1109/TVT.2008.2004555
[8]	Y. Mostofi and D. C. Cox, “ICI mitigation for pilot-aided OFDM mobile systems,” IEEE Transactions on Wireless Communications, vol.4, no.2, pp.764–774, 2005.
[9]	X. J. Dong, L. Wang, and S. J. Wang, “An improved LS channel estimation algorithm,” Journal of Harbin University of Science and Technology, vol.14, no.1, pp.47–50, 2009.
[10]	J. Wang, O. Y. Wen, and S. Li, “Soft-output MMSE MIMO detector under ML channel estimation and channel correlation,” IEEE Signal Processing Letters, vol.16, no.8, pp.667–670, 2009. doi: 10.1109/LSP.2009.2021368
[11]	K. Zhang, L. S. Xue, X. Liu, et al., “Performance comparison of LS, LMMSE channel estimation method in frequency and time domain for OQA-M/OFDM system,” 2017 IEEE 17th Int. Conf. on Communication Technology, Chengdu, China, DOI: 10.1109/ICCT.2017.8359635, 2017.
[12]	H. Ye, G. Y. Li, and B. H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Communications Letters, vol.7, no.1, pp.114–72, 2017.
[13]	H. T. He, C. K. Wen, S. Jin, et al., “A model-driven deep learning network for MIMO detection,” The 6th IEEE Global Conf. on Signal and Information Processing, Anaheim, CA, USA, DOI: 10.1109/GlobalSIP.2018.8646357, 2018.
[14]	X. Gao, S. Jin, C. K. Wen, et al., “Com-Net: Combination of deep learning and expert knowledge in OFDM receivers,” IEEE Communications Letters, vol.22, no.12, pp.2627–2630, 2018. doi: 10.1109/LCOMM.2018.2877965
[15]	Y. Liao, Y. X. Hua, and H. M. Yao, “Channel estimation method based on deep learning in high-speed mobile environments,” Acta Electronica Sinica, vol.47, no.8, pp.1701–1707, 2019. (in Chinese)
[16]	Y. Liao, Y. X. Hua, and Y. L. Cai, “Deep learning based channel estimation algorithm for fast time-varying MIMO-OFDM systems,” IEEE Communications Letters, vol.24, no.3, pp.572–576, 2020. doi: 10.1109/LCOMM.2019.2960242
[17]	B. H. Liu, D. Ding, and L. Yang, “Channel compensation and signal detection of OFDM based on neural network,” Journal of Beijing University of Aeronautics and Astronautics, vol.46, no.7, pp.1363–1370, 2020.
[18]	J. Zhang, C. K. Wen, S. Jin, et al., “Artificial intelligence-aided receiver for a CP-free OFDM system: Design, simulation, and experimental test,” IEEE Access, no.7, pp.58901–58914, 2019.
[19]	J. H. Painter, S. C. Gupta, and L. R. Wilson, “Multipath modelling for aeronautical communications,” IEEE Trans. Comm., vol.21, no.5, pp.658–662, 1973. doi: 10.1109/TCOM.1973.1091704
[20]	P. Beckmann and A. Spizzichino, The Scattering of Electromagnetic Waves from Rough Surfaces, New York: Elmsford, Pergamon Press, 1987.
[21]	D. W. Matolak and R. Sun, “Air–Ground channel characterization for unmanned aircraft systems-Part I: Methods, measurements, and mod-els for over-water settings,” IEEE Transactions on Vehicular Technology, vol.66, no.1, pp.26–44, 2017. doi: 10.1109/TVT.2016.2530306
[22]	R. Sun and D. W. Matolak, “Air-ground channel characterization for unmanned aircraft systems-Part Ⅱ: Hilly and mountainous settings,” IEEE Transactions on Vehicular Technology, vol.66, no.3, pp.1913–25, 2017. doi: 10.1109/TVT.2016.2585504
[23]	D. W. Matolak and R. Sun, “Air-ground channel characterization for unmanned aircraft systems-Part Ⅲ: The suburban and near-urban environments,” IEEE Transactions on Vehicular Technology, vol.66, no.8, pp.6607–6618, 2017. doi: 10.1109/TVT.2017.2659651
[24]	R. Sun and D. W. Matolak, “Air-ground channel characterization for unmanned aircraft systems-Part IV: Airframe shadowing,” IEEE Transactions on Vehicular Technology, vol.66, no.9, pp.7643–7652, 2017. doi: 10.1109/TVT.2017.2677884
[25]	M. Sandler, A. Howard, M. Zhu, et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.4510–4520, 2018.
[26]	A. Novikov, D. Podoprikhin, A. Osokin, et al., “Tensorizing neural networks,” arXiv preprint, arXiv: 1509.06569v2, pp.442–450, 2015.
[27]	M. Courbariaux, I. Hubara, D. Soudry, et al., “Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or −1,” arXiv preprint, arXiv: 1602.02830, 2016.
[28]	S. Han, J. Pool, J. Tran, et al., “Learning both weights and connections for efficient neural network,” Twenty-ninth Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, pp.1135–1143, 2015.
[29]	X. D. Wang, B. C. Zhang, C. Li, et al., “Modulated convolutional networks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, pp.840−848, 2018.
[30]	C. H. Liu, M. L. Wang, Z. L. Dong, et al., “Channel estimation of air-ground data link based on modulation convolutional neural network,” Journal of Beijing University of Aeronautics and Astronautics, vol.48, no.3, pp.533–543, 2022.
[31]	D. N. Zhang, W. R. Ding, B. C. Zhang, et al., “Learning modulation filter networks for weak signal detection in noise,” Pattern Recognition, vol.109, article no.107590, 2021.
[32]	T. N. Sainath, O. Vinyals, A. Senior, et al., “Convolutional, long short-term memory, fully connected deep neural networks,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Queensland, Australia, pp.4580−4584, 2015.