可重构智能表面通信系统的渐进信道估计方法

doi:10.12305/j.issn.1001-506X.2022.03.32

Abstract

Abstract:

Aiming at the problem that the reflection coefficient is randomly configured in the existing channel estimation methods of reconfigurable intelligent surface (RIS), resulting in low signal-to-noise ratio at the receiver, a progressive channel estimation method with specific reflection coefficient is proposed. In this method, the optimal reflection coefficient at the current time is calculated by using the incomplete channel state information (CSI). And then, the optimal reflection coefficient and the random reflection coefficient are used to generate the mixed reflection coefficient according to the weighting coefficient. The RIS configures the hybrid reflection coefficient at the next time, and the receiver uses the relaxed minimum mean square error (RMMSE) channel estimation method to update the incomplete channel state information. Simulation results show that the proposed method can not only obtain accurate channel estimation results, but also continuously increase the signal-to-noise ratio of the receiver and reduce the pilot overhead in the estimation process, so as to improve the spectral efficiency of RIS-assisted wireless communication system.

Key words: reconfigurable intelligent surface (RIS), channel estimation, mixed reflection coefficients, relaxed minimum mean square error (RMMSE), signal-to-noise ratio

CLC Number:

TN929.5

Jian DANG, Yewei LI, Yongdong ZHU, Rongbin GUO, Zaichen ZHANG, Liang WU. Progressive channel estimation method for RIS-assisted communication system[J]. Systems Engineering and Electronics, 2022, 44(3): 998-1006.

Figures/Tables 11

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References 17

1	MARCO D R , ZAPPONE A , DEBBAH M , et al. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead[J]. IEEE Journal on Selected Areas in Communications, 2020, 38 (11): 2450- 2525. doi: 10.1109/JSAC.2020.3007211
2	WU Q Q , ZHANG R . Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58 (1): 106- 112. doi: 10.1109/MCOM.001.1900107
3	LIANG Y C, LONG R Z, ZHANG Q Q, et al. Large intelligent surface/antennas (LISA): making reflective radios smart[EB/OL]. [2021-05-05]. https://arxiv.org/abs/1906.06578.
4	BASAR E , MARCO D R , JULIEN D R , et al. Wireless communications through reconfigurable intelligent surfaces[J]. IEEE Access, 2019, 7, 116753- 116773. doi: 10.1109/ACCESS.2019.2935192
5	HU S , RUSEK F , EDFORS O . Beyond massive MIMO: the potential of data transmission with large intelligent surfaces[J]. IEEE Trans.on Signal Processing, 2018, 66 (10): 2746- 2758. doi: 10.1109/TSP.2018.2816577
6	HUANG C W , ZAPPONE A , ALEXANDROPOULOS G C , et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Trans.on Wireless Communications, 2019, 18 (8): 4157- 4170. doi: 10.1109/TWC.2019.2922609
7	PAN C H , REN H , WANG K Z , et al. Multicell MIMO communications relying on intelligent reflecting surfaces[J]. IEEE Trans.on Wireless Communications, 2020, 19 (8): 5218- 5233. doi: 10.1109/TWC.2020.2990766
8	TAHA A, ALRABEIAH M, ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning[EB/OL]. [2021-05-05]. https://arxiv.org/abs/1904.10136.
9	ALEXANDROPOULOS G C, VLACHOS E. A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2020: 9175-9179.
10	WANG Z R , LIU L , CUI S G . Channel estimation for intelligent reflecting surface assisted multiuser communications: framework, algorithms, and analysis[J]. IEEE Trans.on Wireless Communications, 2020, 19 (10): 6607- 6620.
11	ZHOU Z Y , GE N , WANG Z C , et al. Joint transmit precoding and reconfigurable intelligent surface phase adjustment: a decomposition-aided channel estimation approach[J]. IEEE Trans.on Communications, 2021, 69 (2): 1228- 1243. doi: 10.1109/TCOMM.2020.3034259
12	WANG P L , FANG J , DUAN H P , et al. Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems[J]. IEEE Signal Processing Letters, 2020, 27, 905- 909. doi: 10.1109/LSP.2020.2998357
13	JIA C L, CHENG J Q, GAO H, et al. High-resolution channel estimation for intelligent reflecting surface-assisted mmWave communications[C]//Proc. of the IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020.
14	HAN Y , TANG W K , JIN S , et al. Large intelligent surface-assisted wireless communication exploiting statistical CSI[J]. IEEE Trans.on Vehicular Technology, 2019, 68 (8): 8238- 8242. doi: 10.1109/TVT.2019.2923997
15	陈淑菁. 大规模MIMO信道估计与信号检测理论方法研究[D]. 江苏: 东南大学, 2018.
	CHEN S J. Channel estimation and signal detection for massive MIMO systems[D]. Jiangsu: Southeast University, 2018.
16	张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004.
	ZHANG X D . Matrix analysis and applications[M]. Beijing: Tsinghua University Press, 2004.
17	WU Q Q , ZHANG R . Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Trans.on Wireless Communications, 2019, 18 (11): 35394- 5409.

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Progressive channel estimation method for RIS-assisted communication system

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