Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

doi:10.19682/j.cnki.1005-8885.2024.2005

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (1): 49-56.doi: 10.19682/j.cnki.1005-8885.2024.2005

Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

Kang Xiaofei, Wang Tian, Liang Xian

College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710600, China

收稿日期:2023-08-23 修回日期:2024-01-01 接受日期:2024-02-22 出版日期:2024-02-29 发布日期:2024-02-29
通讯作者: Corresponding author: Kang Xiaofei, E-mail: kangxiaofei@sina.com E-mail:kangxiaofei@sina.com

Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

Kang Xiaofei, Wang Tian, Liang Xian

College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710600, China

Received:2023-08-23 Revised:2024-01-01 Accepted:2024-02-22 Online:2024-02-29 Published:2024-02-29
Contact: Corresponding author: Kang Xiaofei, E-mail: kangxiaofei@sina.com E-mail:kangxiaofei@sina.com

摘要/Abstract

摘要： In response to the challenge posed by the complexity of the system and the difficulty in obtaining accurate channel state information (CSI) for millimeter wave communication assisted by intelligent reflecting surfaces (IRS), we propose a deep learning-based channel estimation scheme. The proposed scheme employs a hybrid active/passive IRS architecture, wherein the least square (LS) algorithm is initially utilized to acquire the channel estimate from the active elements. Subsequently, this estimation is interpolated to obtain a preliminary channel estimation and ultimately refined into an accurate estimate of the channel using the channel super-resolution convolutional neural network (Chan-SRCNN) deep learning network. The simulation results demonstrate that the proposed scheme surpasses LS, orthogonal matching pursuit (OMP), synchronous OMP (SOMP), and deep neural network (DNN) channel estimation algorithms in terms of normalized mean squared error (NMSE) performance, thereby validating the feasibility of the proposed approach.

关键词: intelligent reflecting surface, millimeter wave, channel estimation, deep learning

Abstract: In response to the challenge posed by the complexity of the system and the difficulty in obtaining accurate channel state information (CSI) for millimeter wave communication assisted by intelligent reflecting surfaces (IRS), we propose a deep learning-based channel estimation scheme. The proposed scheme employs a hybrid active/passive IRS architecture, wherein the least square (LS) algorithm is initially utilized to acquire the channel estimate from the active elements. Subsequently, this estimation is interpolated to obtain a preliminary channel estimation and ultimately refined into an accurate estimate of the channel using the channel super-resolution convolutional neural network (Chan-SRCNN) deep learning network. The simulation results demonstrate that the proposed scheme surpasses LS, orthogonal matching pursuit (OMP), synchronous OMP (SOMP), and deep neural network (DNN) channel estimation algorithms in terms of normalized mean squared error (NMSE) performance, thereby validating the feasibility of the proposed approach.

Key words: intelligent reflecting surface, millimeter wave, channel estimation, deep learning

Kang Xiaofei, Wang Tian, Liang Xian. Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning[J]. The Journal of China Universities of Posts and Telecommunications, 2024, 31(1): 49-56.

参考文献

References

[1] JIANG T, CHENG H V, YU W. Learning to reflect and to beamform for intelligent reflecting surface with implicit channel estimation. IEEE Journal on Selected Areas in Communications, 2021, 39(7): 1931 - 1945.

[2] DI RENZO M, ZAPPONE A, DEBBAH M, et al. Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and road ahead. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2450 - 2525.

[3] PAN C H, REN H, WANG K Z, et al. Multicell MIMO communications relying on intelligent reflecting surfaces. IEEE Transactions on Wireless Communications, 2020, 19(8): 5218 - 5233.

[4] HU X L, ZHONG C J, ZHU Y X, et al. Programmable metasurface-based multicast systems: Design and analysis. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1763 - 1776.

[5] WU Q Q, ZHANG R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394 - 5409.

[6] DANG S P, AMIN O, SHIHADA B, et al. What should 6G be? Nature Electronics, 2020, 3: 20 - 29.

[7] YOU X H, WANG C X, HUANG J, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China: Information Sciences, 2021, 64(1): Article 110301.

[8] WU Q Q, ZHANG S W, ZHENG B X, et al. Intelligent reflecting surface-aided wireless communications: A tutorial. IEEE Transactions on Communications, 2021, 69(5): 3313 - 3351.

[9] WU Q Q, ZHANG R. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network. IEEE Communications Magazine, 2020, 58(1): 106 - 112.

[10] HU X L, ZHANG R, ZHONG C J. Semi-passive elements assisted channel estimation for intelligent reflecting surface-aided communications. IEEE Transactions on Wireless Communications, 20220, 21(2): 1132 - 1142.

[11] TAHA A, ALRABEIAH M, ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning. IEEE Access, 2021, 9: 44304 - 44321.

[12] WANG Z R, LIU L, CUI S G. Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis. IEEE Transactions on Wireless Communications, 2020, 19(10): 6607 - 6620.

[13] WANG P L, FANG J, DUAN H P, et al. Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems. IEEE Signal Processing Letters, 2020, 27: 905 - 909.

[14] CHEN J, LIANG Y C, CHENG H V, et al. Channel estimation for reconfigurable intelligent surface aided multi-user mmwave MIMO systems. IEEE Transactions on Wireless Communications, 2023, 22(10): 6853 - 6869.

[15] ZHENG B X, YOU C S, ZHANG R. Intelligent reflecting surface assisted multi-user OFDMA: Channel estimation and training design. IEEE Transactions on Wireless Communications, 2020, 19(12): 8315 - 8329.

[16] ZHOU G, PAN C H, REN H, et al. Channel estimation for RIS-aided multiuser millimeter-wave systems. IEEE Transactions on Signal Processing, 2022, 70: 1478 - 1492.

[17] JIANG T, CHENG H V, YU W. Learning to reflect and to beamform for intelligent reflecting surface with implicit channel estimation. IEEE Journal on Selected Areas in Communications, 2021, 39(7): 1931 - 1945.

[18] LIU C, LIU X M, NG D W K, et al. Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications. IEEE Transactions on Wireless Communications, 2022, 21(2): 898 - 912.

[19] CHEN Z, TANG J, ZHANG X Y, et al. Offset learning based channel estimation for intelligent reflecting surface-assisted indoor communication. IEEE Journal of Selected Topics in Signal Processing, 2022, 16(1): 41 - 55.

[20] DAI L L, WEI X H. Distributed machine learning based downlink channel estimation for RIS assisted wireless communications. IEEE Transactions on Communications, 2022, 70(7): 4900 - 4909.

[21] LIU S C, GAO Z, ZHANG J, et al. Deep denoising neural network assisted compressive channel estimation for mmwave intelligent reflecting surfaces. IEEE Transactions on Vehicular Technology, 2020, 69(8): 9223 - 9228.

Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价