Time-varying channel estimation in reconfigurable intelligent surface assisted communication system

被引：0
作者：
Shao K. [1 ,2 ,3 ]
Lu B. [1 ]
Wang G. [1 ,2 ]
机构：
[1] School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
[2] Chongqing Key Laboratory of Mobile Communications Technology, Chongqing
[3] Engineering Research Center of Mobile Communications of the Ministry of Education, Chongqing
来源：
Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Bayesian compressed sensing; channel estimation; Kalman filter; reconfigurable intelligent surface;
D O I：
10.11959/j.issn.1000-436x.2024028
中图分类号：
学科分类号：
摘要：
Aiming at the key problems need to be solved, such as cascade channel sparse representation, time-varying channel parameter tracking and signal reconstruction, for time-varying cascade channels estimation of reconfigurable intelligent surface (RIS) assisted communication system, a Khatri-Rao and hierarchical Bayesian Kalman filter (KR-HBKF) algorithm was proposed. Firstly, the Khatri-Rao product and Kronecker product transformations were used to obtain the sparse representation of RIS cascaded channels based on the sparse characteristics of channels, thus the RIS cascaded channel estimation problem was transformed into a low-dimensional sparse signal recovery problem. Then, according to the state evolution model of RIS cascaded channel, the time correlation parameter was introduced into the prediction model of HBKF algorithm, and the improved HBKF was applied to solve the problem of time-varying channel parameter tracking and signal reconstruction for completing the time-varying cascaded channels estimation. The sparsity and time correlation of the channel were comprehensively considered in the KR-HBKF algorithm, thus better estimation accuracy could be obtained with small pilot overhead. Compared with the traditional compressed sensing algorithm, the simulation results show that the proposed algorithm has about 5 dB estimated performance improvement, and better robustness performance under different time-varying channel conditions. © 2024 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：119 / 128
页数：9
相关论文
共 29 条
  • [1] SHAMASUNDAR B, DARYANAVARDAN N, NOSRATINIA A., Channel training & estimation for reconfigurable intelligent surfaces: exposition of principles, approaches, and open problems, IEEE Access, 11, pp. 6717-6734, (2023)
  • [2] SWINDLEHURST A L, ZHOU G, LIU R, Et al., Channel estimation with reconfigurable intelligent surfaces—a general framework, Proceedings of the IEEE, 110, 9, pp. 1312-1338, (2022)
  • [3] ZHENG B X, YOU C S, MEI W D, Et al., A survey on channel estimation and practical passive beamforming design for intelligent reflecting surface aided wireless communications, IEEE Communications Surveys & Tutorials, 24, 2, pp. 1035-1071, (2022)
  • [4] XUE Q, LIU Y J, SUN Y, Et al., Beam management in ultra-dense mmWave network via federated reinforcement learning: an intelligent and secure approach, IEEE Transactions on Cognitive Communications and Networking, 9, 1, pp. 185-197, (2023)
  • [5] YANG Y F, ZHENG B X, ZHANG S W, Et al., Intelligent reflecting surface meets OFDM: protocol design and rate maximization, IEEE Transactions on Communications, 68, 7, pp. 4522-4535, (2020)
  • [6] 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, 19, 12, pp. 8315-8329, (2020)
  • [7] ZHENG B X, ZHANG R., Intelligent reflecting surface-enhanced OFDM: channel estimation and reflection optimization, IEEE Wireless Communications Letters, 9, 4, pp. 518-522, (2020)
  • [8] JENSEN T L, DE-CARVALHO E., An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator, Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5000-5004, (2020)
  • [9] MA Y Y, BAI Z Q, HE B W, Et al., Enhanced deep residual shrinkage network based channel estimation in RIS communication system, Proceedings of the 2023 25th International Conference on Advanced Communication Technology (ICACT), pp. 18-22, (2023)
  • [10] 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, 69, 8, pp. 9223-9228, (2020)
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