Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming

doi:10.1109/TWC.2020.3033332

CSpace

	Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming
	Xie, Hailiang 1,2; Xu, Jie 2,3; Liu, Ya-Feng 4
	2021-02-01
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
ISSN	1536-1276
卷号	20 期号:2 页码:1379-1393
摘要	This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) network with a set of multi-antenna base stations (BSs) each communicating with multiple single-antenna users, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming vectors at the BSs and the reflective beamforming vector (with both reflecting phases and amplitudes) at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS. To solve the non-convex min-weighted-SINR maximization problem, we first present an exact-alternating-optimization approach to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program (SOCP) and semi-definite relaxation (SDR), respectively. However, the exact-alternating-optimization approach has high computational complexity, and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization approach, in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation (SCA). In addition, to further reduce the computational complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Via numerical results, it is shown that the proposed three designs achieve significantly increased min-weighted-SINR values, as compared with benchmark schemes without the IRS or with random reflective beamforming. It is also shown that the inexact-alternating-optimization design outperforms the exact-alternating-optimization one in terms of both the achieved min-weighted-SINR value and the computational complexity, while the low-complexity inexact-alternating-optimization design has much lower computational complexity with slightly compromised performance. Furthermore, we show that our proposed design can be applied to the scenario with unit-amplitude reflection constraints, with a negligible performance loss.
关键词	Array signal processing MISO communication Optimization Wireless communication Signal to noise ratio Interference Computational complexity Intelligent reflecting surface (IRS) multi-cell systems multiple-input single-output (MISO) coordinated transmit beamforming reflective beamforming optimization
DOI	10.1109/TWC.2020.3033332
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1800800] ; National Natural Science Foundation of China (NSFC)[61871137] ; National Natural Science Foundation of China (NSFC)[12022116] ; National Natural Science Foundation of China (NSFC)[12021001] ; National Natural Science Foundation of China (NSFC)[11991021] ; Guangdong Province Key Area Research and Development Program[2018xB030338001]
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000617385600047
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58165
专题	中国科学院数学与系统科学研究院
通讯作者	Xu, Jie
作者单位	1.Guangdong Univ Technol, Sch Informat Engn, Guangzhou 510006, Peoples R China 2.Chinese Univ Hong Kong Shenzhen, Future Network Intelligence Inst FNii, Shenzhen 518172, Peoples R China 3.Chinese Univ Hong Kong Shenzhen, Sch Sci & Engn, Shenzhen 518172, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math & Sci Engn Comp, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xie, Hailiang,Xu, Jie,Liu, Ya-Feng. Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2021,20(2):1379-1393.
APA	Xie, Hailiang,Xu, Jie,&Liu, Ya-Feng.(2021).Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,20(2),1379-1393.
MLA	Xie, Hailiang,et al."Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 20.2(2021):1379-1393.