A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface

PU Xumin; SUN Zhinan; LI Jingjie; HUANG Qiong; CHEN Qianbin

doi:10.11999/JEIT211602

Volume 44 Issue 7

Jul. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2281-2288

PU Xumin, SUN Zhinan, LI Jingjie, HUANG Qiong, CHEN Qianbin. A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602

Citation:

PU Xumin, SUN Zhinan, LI Jingjie, HUANG Qiong, CHEN Qianbin. A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602

Citation:

PDF( 3026 KB)

A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface

doi: 10.11999/JEIT211602 cstr: 32379.14.JEIT211602

PU Xumin^{1, 2, 3
,
,},
SUN Zhinan¹,
LI Jingjie¹,
HUANG Qiong¹,
CHEN Qianbin²

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (61701062), The China Postdoctoral Science Foundation (2019M651649), The Jiangsu Planned Projects for Postdoctoral Research Funds (2018K041c),The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100649)

Received Date: 2021-12-30
Rev Recd Date: 2022-06-09

Available Online: 2022-06-20

Publish Date: 2022-07-25

Abstract

Abstract

In this paper, a low complexity channel estimation algorithm is proposed, which is used to reduce the computational complexity of the millimeter wave channel estimation in the massive MIMO systems assisted by the Reconfigurable Intelligent Surfaces (RIS). In the proposed scheme, some elements of the RIS are connected to the Radio Frequency (RF) chain to estimate the channel between the base station/user and the RIS separately, which improves the flexibility of channel estimation. The zero-padding two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for angle estimation in this scenario for the first time. The path gain estimation is obtained by using the spectral peak of the two-dimensional spatial spectrum of the signal and its corresponding argument. Simulation results show that the proposed algorithm achieves excellent channel estimation performance, and based on the system parameter setting to ensure the channel estimation performance, the proposed algorithm has a strong complexity advantage.
- Channel estimation,
- Reconfigurable Intelligent Surfaces(RIS),
- Massive MIMO,
- Low complexity

FullText(HTML)

References(24)

References

[1]	WU Qingqing and ZHANG Rui. Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts[J]. IEEE Transactions on Communications, 2020, 68(3): 1838–1851. doi: 10.1109/TCOMM.2019.2958916
[2]	ZHENG Beixiong, YOU Changsheng, and ZHANG Rui. Intelligent reflecting surface assisted multi-user OFDMA: Channel estimation and training design[J]. IEEE Transactions on Wireless Communications, 2020, 19(12): 8315–8329. doi: 10.1109/TWC.2020.3021434
[3]	朱政宇, 王梓晅, 徐金雷, 等. 智能反射面辅助的未来无线通信: 现状与展望[J]. 航空学报, 2022, 43(2): 198–212. doi: 10.7527/S1000-6893.2021.25014 ZHU Zhengyu, WANG Zixuan, XU Jinlei, et al. Future wireless communication assisted by intelligent reflecting surface: State of art and prospects[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(2): 198–212. doi: 10.7527/S1000-6893.2021.25014
[4]	YUAN Xiaojun, ZHANG Y J A, SHI Yuanming, et al. Reconfigurable-intelligent-surface empowered wireless communications: Challenges and opportunities[J]. IEEE Wireless Communications, 2021, 28(2): 136–143. doi: 10.1109/MWC.001.2000256
[5]	LIASKOS C, NIE Shuai, TSIOLIARIDOU A, et al. A new wireless communication paradigm through software-controlled metasurfaces[J]. IEEE Communications Magazine, 2018, 56(9): 162–169. doi: 10.1109/MCOM.2018.1700659
[6]	BASAR E, DI RENZO M, DE ROSNY J, et al. Wireless communications through reconfigurable intelligent surfaces[J]. IEEE Access, 2019, 7: 116753–116773. doi: 10.1109/ACCESS.2019.2935192
[7]	WU Qingqing and ZHANG Rui. 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
[8]	LI Lixin, MA Donghui, REN Huan, et al. Enhanced reconfigurable intelligent surface assisted mmWave communication: A federated learning approach[J]. China Communications, 2020, 17(10): 115–128. doi: 10.23919/JCC.2020.10.008
[9]	YANG Liang, MENG Fanxu, ZHANG Jiayi, et al. On the performance of RIS-assisted dual-hop UAV communication systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 10385–10390. doi: 10.1109/TVT.2020.3004598
[10]	BAI Tong, PAN Cunhua, HAN Chao, et al. Reconfigurable intelligent surface aided mobile edge computing[J]. IEEE Wireless Communications, 2021, 28(6): 80–86. doi: 10.1109/MWC.001.2100142
[11]	朱政宇, 徐金雷, 孙钢灿, 等. 基于IRS辅助的SWIPT物联网系统安全波束成形设计[J]. 通信学报, 2021, 42(4): 185–193. doi: 10.11959/j.issn.1000-436x.2021060 ZHU Zhengyu, XU Jinlei, SUN Gangcan, et al. Secure beamforming design for IRS-assisted SWIPT internet of things system[J]. Journal on Communications, 2021, 42(4): 185–193. doi: 10.11959/j.issn.1000-436x.2021060
[12]	ZHU Zhengyu, LI Zheng, CHU Zheng, et al. Resource allocation for intelligent reflecting surface assisted wireless powered IoT systems with power splitting[J]. IEEE Transactions on Wireless Communications, 2022, 21(5): 2987–2998. doi: 10.1109/TWC.2021.3117346
[13]	WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025
[14]	WANG Peilan, FANG Jun, DUAN Huiping, 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
[15]	NADEEM Q U A, ALWAZANI H, KAMMOUN A, et al. Intelligent reflecting surface-assisted multi-user MISO communication: Channel estimation and beamforming design[J]. IEEE Open Journal of the Communications Society, 2020, 1: 661–680. doi: 10.1109/OJCOMS.2020.2992791
[16]	HU Chen, DAI Linglong, HAN Shuangfeng, et al. Two-timescale channel estimation for reconfigurable intelligent surface aided wireless communications[J]. IEEE Transactions on Communications, 2021, 69(11): 7736–7747. doi: 10.1109/TCOMM.2021.3072729
[17]	GUAN Xinrong, WU Qingqing, and ZHANG Rui. Anchor-assisted intelligent reflecting surface channel estimation for multiuser communications[C]. 2020 IEEE Global Communications Conference, Taipei, China, 2020: 1–6.
[18]	DE ARAUJO G T and DE ALMEIDA A L F. PARAFAC-based channel estimation for intelligent reflective surface assisted MIMO system[C]. The 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, Hangzhou, China, 2020: 1–5.
[19]	LIU Hang, YUAN Xiaojun, and ZHANG Y A. Matrix-calibration-based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser MIMO[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2621–2636. doi: 10.1109/JSAC.2020.3007057
[20]	CHEN Xiao, SHI Jianfeng, YANG Zhaohui, et al. Low-complexity channel estimation for intelligent reflecting surface-enhanced massive MIMO[J]. IEEE Wireless Communications Letters, 2021, 10(5): 996–1000. doi: 10.1109/LWC.2021.3054004
[21]	TAHA A, ALRABEIAH M, and ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning[J]. IEEE Access, 2021, 9: 44304–44321. doi: 10.1109/ACCESS.2021.3064073
[22]	傅友华, 陈栋. 混合智能反射表面结构辅助的毫米波通信信道估计[J]. 通信学报, 2021, 42(10): 189–196. doi: 10.11959/j.issn.1000−436x.2021197 FU Youhua and CHEN Dong. Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications[J]. Journal on Communications, 2021, 42(10): 189–196. doi: 10.11959/j.issn.1000−436x.2021197
[23]	FAN Dian, GAO Feifei, WANG Gongpu, et al. Angle domain signal processing-aided channel estimation for indoor 60-GHz TDD/FDD massive MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(9): 1948–1961. doi: 10.1109/JSAC.2017.2720938
[24]	HU Anzhong, LV Tiejun, and LU Yueming. Subspace-based semi-blind channel estimation for large-scale multi-cell multiuser MIMO systems[C]. The 2013 IEEE 77th Vehicular Technology Conference, Dresden, Germany, 2013: 1–5.