A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications

Abstract

The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development. Due to the considerable mobility of trains and the complex nature of the environment, the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics, which presents significant hurdles in terms of channel estimation. In addition, the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation. To address the aforementioned issues, this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network. In this method, the time-frequency response of the channel is considered as a two-dimensional image. The Fast Super-Resolution Convolution Neural Network (FSRCNN) is used to first reconstruct channel images. Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.

Keywords

High-speed railway / Channel estimation / OFDM system / 5G / Convolution neural network

CRediT authorship contribution statement

Qingmiao Zhang: Writing - original draft. Yuhao Zhao: Writing - original draft. Hanzhi Dong: Methodology. Junhui Zhao: Resources.

Declaration of Competing Interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.

Acknowledgements

This work presented in this paper is funded in part by the National Natural Science Foundation of China (62261024 and U2001213), in part by National Key Research and Development Project (2020YFB1807204), in part by Science and Technology Project of Education Department of Jiangxi Province (GJJ214606 and GJJ2205201), in part by Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R. China (KFKT-2022101), in part by the Jiangxi Provincial Natural Science Foundation (20212BAB212001).

References

[1] J. Zhao, X. Sun, X. Ma, H. Zhang, F.R. Yu, Y. Hu, Online distributed optimization for energy-efficient computation offloading in air-ground integrated networks, IEEE Trans. Veh. Technol. 72 (4) (2023) 5110-5124.

[2] V. Vahidi, E. Saberinia, Ofdm high speed train communication systems in 5G cellular networks, in: 2018 15th IEEE Annual Consumer Communications & Networking Conference, IEEE, 2018, pp. 1-6.

[3] J. Zhao, J. Liu, L. Yang, B. Ai, S. Ni, Future 5G-oriented system for urban rail transit: opportunities and challenges, China Commun. 18 (2) (2021) 1-12.

[4] C. Wang, Non-stationary wideband mimo channel models for high-speed train wire-less communication systems, in: 2014 International Workshop on High Mobility Wireless Communications, IEEE, 2014, pp. 4-4.

[5] J. Zhao, Q. Li, Y. Gong, K. Zhang, Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks, IEEE Trans. Veh. Technol. 68 (8) (2019) 7944-7956.

[6] A. Shaikh, M.J. Kaur, Comprehensive survey of massive mimo for 5G communi-cations, in: 2019 Advances in Science and Engineering Technology International Conferences, IEEE, 2019, pp. 1-5.

[7] J. Zhao, L. He, D. Zhang, X. Gao, A TD-DDPG algorithm based on cache assistance for task offloading in urban rail transit, IEEE Trans. Veh. Technol. 72 (8) (2023) 10671-10681.

[8] A. Saci, A. Al-Dweik, A. Shami, Y. Iraqi, One-shot blind channel estimation for OFDM systems over frequency-selective fading channels, IEEE Trans. Commun. 65 (12) (2017) 5445-5458.

[9] Y. Liao, Y. Hua, Y. Cai, Deep learning based channel estimation algorithm for fast time-varying mimo-ofdm systems, IEEE Commun. Lett. 24 (3) (2020) 572-576.

[10] R.K. Miranda, J.P.C.L. da Costa, B. Guo, A.L.F. de Almeida, G.D. Galdo, R.T. de Sousa Júnior, Low-complexity and high-accuracy semi-blind joint channel and symbol esti-mation for massive mimo-ofdm, Circuits Syst. Signal Process. 38 (2018) 1114-1136.

[11] J. Lu, X. Chen, S. Liu, P. Fan, Location-aware ici reduction in MIMO-OFDM down-links for high-speed railway communication systems, IEEE Trans. Veh. Technol. 67 (4) (2018) 2958-2972.

[12] X. Ye, G. Zheng, A. Zhang, L. You, X. Gao, Dft-based low-complexity channel estima-tion method for millimeter-wave mimo systems, Wirel. Pers. Commun. 107 (2019) 205-216.

[13] L. Ge, Y. Guo, Y. Zhang, G. Chen, J. Wang, B. Dai, M. Li, T. Jiang, Deep neural network based channel estimation for massive mimo-ofdm systems with imperfect channel state information, IEEE Syst. J. 16 (3) (2022) 4675-4685.

[14] N.M. Idrees, X. Yu, A. Springer, Optimal tracking of doubly-selective radio channels for ofdm based modern wireless systems, Phys. Commun. 35 (2019) 100739.

[15] A. Ghazal, Y. Yuan, C.-X. Wang, Y. Zhang, Q. Yao, H. Zhou, W. Duan, A non-stationary imt-advanced mimo channel model for high-mobility wireless commu-nication systems, IEEE Trans. Wirel. Commun. 16 (4) (2017) 2057-2068.

[16] S. Lyu, X. Li, T. Fan, J. Liu, M. Shi, Deep learning for fast channel estimation in millimeter-wave mimo systems, J. Syst. Eng. Electron. 33 (6) (2022) 1088-1095.

[17] X. Gao, S. Jin, C.-K. Wen, G.Y. Li, Comnet: combination of deep learning and expert knowledge in ofdm receivers, IEEE Commun. Lett. 22 (12) (2018) 2627-2630.

[18] J. Yang, C.-K. Wen, S. Jin, F. Gao, Beamspace channel estimation in mmwave sys-tems via cosparse image reconstruction technique, IEEE Trans. Commun. 66 (10) (2018) 4767-4782.

[19] Y. Dong, H. Wang, Y.-D. Yao, Channel estimation for one-bit multiuser massive mimo using conditional gan, IEEE Commun. Lett. 25 (3) (2021) 854-858.

[20] Q. Zhang, H. Dong, J. Zhao, Channel estimation for high-speed railway wireless com-munications: a generative adversarial network approach, Electronics 12 (7) (2023) 1752.

[21] Y. Liao, Y. Hua, X. Dai, H. Yao, X. Yang,Chanestnet: A deep learning based channel estimation for high-speed scenarios, in: ICC 2019-2019 IEEE International Confer-ence on Communications, IEEE, 2019, pp. 1-6.

[22] Q. Zhang, J. Zhao, A. Chen, C. Wang, Adaptive beamforming and power allocation for mmwave communication in high-speed railway, Radio Sci. 56 (3) (2021) 1-11.

[23] S. Chen, C. Liu, L. Huang, Estimation of pilot-assisted ofdm channel based on multi-resolution deep neural networks, in: 2022 IEEE International Conference on Unmanned Systems, IEEE, 2022, pp. 764-769.

[24] H. Huang, S. Guo, G. Gui, Z. Yang, J. Zhang, H. Sari, F. Adachi, Deep learning for physical-layer 5g wireless techniques: opportunities, challenges and solutions, IEEE Wirel. Commun. 27 (1) (2020) 214-222.

[25] M. Soltani, V. Pourahmadi, A. Mirzaei, H. Sheikhzadeh, Deep learning-based chan-nel estimation, IEEE Commun. Lett. 23 (4) (2019) 652-655.

[26] J. Zhang, D. Huang, Image super-resolution reconstruction algorithm based on fs-rcnn and residual network, in: 2019 IEEE 4th International Conference on Image, Vision and Computing, IEEE, 2019, pp. 241-246.

[27] K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in: 2015 IEEE International Conference on Computer Vision, IEEE, 2015, pp. 1026-1034.

[28] K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a Gaussian denoiser: residual learning of deep cnn for image denoising, IEEE Trans. Image Process. 26 (7) (2017) 3142-3155.

[29] S. Liu, Z. Gao, J. Zhang, M.D. Renzo, M.-S. Alouini, Deep denoising neural network assisted compressive channel estimation for mmwave intelligent reflecting surfaces, IEEE Trans. Veh. Technol. 69 (8) (2020) 9223-9228.