IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming

doi:10.12142/ZTECOM.202201005

ZTE Communications ›› 2022, Vol. 20 ›› Issue (1): 28-35.DOI: 10.12142/ZTECOM.202201005

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IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming

GUAN Xinrong¹, WU Qingqing²()

^1.College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China
^2.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China

Received:2021-11-01 Online:2022-03-25 Published:2022-04-06
Contact: WU Qingqing
About author:GUAN Xinrong received both the B.Eng. degree in communications engineering and Ph.D. degree in communications and information systems from the College of Communications Engineering, PLA University of Science and Technology, China in 2009 and 2014, respectively. From 2014, he worked as a lecturer at the College of Communications Engineering, Army Engineering University of PLA, China. His current research interests include physical layer security, wireless key generation, and intelligent reflecting surfaces. He has coauthored more than 40 IEEE papers with one ESI highly cited paper. He has served as a reviewer of several IEEE journals.|WU Qingqing (qingqingwu@um.edu.mo) received the B.Eng. and Ph.D. degrees in electronic engineering from South China University of Technology and Shanghai Jiao Tong University (SJTU), China in 2012 and 2016, respectively. He is currently an assistant professor with the State Key Laboratory of Internet of Things for Smart City, University of Macau, China. From 2016 to 2020, he was a research fellow in the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests include intelligent reflecting surfaces, unmanned aerial vehicle (UAV) communications, and MIMO transceiver design. He has coauthored more than 100 IEEE papers with 23 ESI highly cited papers and eight ESI hot papers, which have received more than 9 000 Google citations. He was listed as a World’s Top 2% Scientist by Stanford University in 2020 and a Clarivate ESI Highly Cited Researcher in 2021.
Supported by:
The work of GUAN Xinrong was supported by the National Natural Science Foundation of China(62171461);Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(BK20212001);The work of WU Qingqing was supported by the Macau Science and Technology Development Fund, Macau SAR(0119/2020/A3);the Guangdong NSF(2021A1515011900);the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University(2021D15)

Abstract

Abstract:

Intelligent reflecting surface (IRS), with its unique capability of smartly reconfiguring wireless channels, provides a new solution to improving spectrum efficiency, reducing energy consumption and saving deployment/hardware cost for future wireless networks. In this paper, IRS-enabled spectrum sharing is investigated, from the perspectives of interference modeling, efficient channel estimation and robust passive beamforming design. Specifically, we first characterize the interference in a spectrum sharing system consisting of a single primary user (PU) pair and a single secondary user (SU) pair, and extend it to the large-scale network by leveraging the Poisson point process (PPP). Then, we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels. Moreover, the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy. Finally, we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts. It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

Key words: intelligent reflecting surface, spectrum sharing, channel estimation, passive beamforming

GUAN Xinrong, WU Qingqing. IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming[J]. ZTE Communications, 2022, 20(1): 28-35.

Figures/Tables 7

Figure 1 IRS-enabled spectrum sharing with different setups

Figure 2 IRS-enabled spectrum sharing with single primary-user/secondary-user (PU/SU) pair

Figure 3 IRS-enabled spectrum sharing in large-scale network

Figure 4 Cascaded channel decouple based channel estimation for IRS-enabled spectrum sharing system

Figure 5 A channel estimation and data transmission protocol for the IRS-enabled spectrum sharing system in Fig. 2

Figure 6 Two simulation setups with strong cross link interference

Figure 7 Achievable SU rate versus maximum transmitting power at secondary transmitter (ST)

References 18

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IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming

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