Full Text:   <416>

Summary:  <114>

CLC number: TN929.5

On-line Access: 2024-01-26

Received: 2023-02-27

Revision Accepted: 2024-01-26

Crosschecked: 2023-08-12

Cited: 0

Clicked: 642

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yonghua QUAN

https://orcid.org/0000-0002-6181-496X

Zhong TIAN

https://orcid.org/0000-0003-3176-7290

Zhengchuan CHEN

https://orcid.org/0000-0002-2289-5621

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.12 P.1763-1775

http://doi.org/10.1631/FITEE.2300120


Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model


Author(s):  Yonghua QUAN, Zhong TIAN, Zhengchuan CHEN, Min WANG, Yunjian JIA

Affiliation(s):  School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China; more

Corresponding email(s):   qyh@cqu.edu.cn, ztian@cqu.edu.cn, czc@cqu.edu.cn

Key Words:  Reconfigurable intelligent surface, Max-min rate, Coalition-game subcarrier allocation


Yonghua QUAN, Zhong TIAN, Zhengchuan CHEN, Min WANG, Yunjian JIA. Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(12): 1763-1775.

@article{title="Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model",
author="Yonghua QUAN, Zhong TIAN, Zhengchuan CHEN, Min WANG, Yunjian JIA",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="12",
pages="1763-1775",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300120"
}

%0 Journal Article
%T Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model
%A Yonghua QUAN
%A Zhong TIAN
%A Zhengchuan CHEN
%A Min WANG
%A Yunjian JIA
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 12
%P 1763-1775
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300120

TY - JOUR
T1 - Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model
A1 - Yonghua QUAN
A1 - Zhong TIAN
A1 - Zhengchuan CHEN
A1 - Min WANG
A1 - Yunjian JIA
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 12
SP - 1763
EP - 1775
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300120


Abstract: 
reconfigurable intelligent surfaces (RISs) have the capability to change the wireless environment smartly. Considering the attenuation of subchannels and crowding users involved in the wideband system, we introduce RISs into the multi-user multi-input single-output (MU-MISO) system with orthogonal frequency division multiplexing (OFDM) for performance enhancement. Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation, transmit precoding (TPC) matrices at the base station, and RIS passive beamforming. A coalition-game subcarrier allocation (CSA) algorithm is proposed to solve space–frequency resource allocation on subcarriers, which reforms the interference topology among dense users. Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming, which improves the spectral efficiency synthetically across all subchannels in the wideband system. Simulation results indicate that the CSA algorithm provides a significant gain for dense users. Besides, the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

基于智能超表面宽带模型的下行多用户MISO-OFDM系统最大化最小速率优化

全永桦1,田中1,陈正川1,2,王敏3,4,贾云健1
1重庆大学微电子与通信工程学院,中国重庆市,400044
2西安邮电大学陕西省信息通信网络及安全重点实验室,中国西安市,710121
3重庆邮电大学光电工程学院,中国重庆市,400065
4桂林电子科技大学广西无线宽带通信与信号处理重点实验室,中国桂林市,541004
摘要:智能超表面具有智能化改变无线环境的能力。考虑到宽带系统中子信道的衰减和拥挤的用户,我们将智能超表面引入具有正交频分复用(orthogonal frequency division multiplexing,OFDM)的多用户多入单出(multi-input single-output,MISO)系统,用于增强系统性能。基于智能超表面的近似实用宽带模型,智能超表面辅助密集用户的最小速率最大化问题被表征为包含子载波分配、基站发送预编码矩阵和智能超表面无源波束形成的联合优化问题。提出联盟博弈子载波分配算法解决子载波的空频资源分配问题,改善密集用户间的干扰拓扑。利用分数规划和凸优化方法优化预编码矩阵和智能超表面无源波束形成,提高了宽带系统中所有子信道的频谱效率。仿真结果表明,联盟博弈子载波分配算法为密集用户提供了显著的速率增益。此外,所提联合优化方法展示了智能超表面在该系统中的显著优势。

关键词:智能超表面;最大化最小速率;联盟博弈子载波分配

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bogomolnaia A, Jackson MO, 2002. The stability of hedonic coalition structures. Games Econ Behav, 38(2):201-230.

[2]Boyd S, Parikh N, Chu E, et al., 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 3(1):1-122.

[3]Cai WH, Li HY, Li M, et al., 2020. Practical modeling and beamforming for intelligent reflecting surface aided wideband systems. IEEE Commun Lett, 24(7):1568-1571.

[4]Cai WH, Liu R, Li M, et al., 2022. IRS-assisted multicell multiband systems: practical reflection model and joint beamforming design. IEEE Trans Commun, 70(6):3897-3911.

[5]Cao XL, Yang B, Huang CW, et al., 2021a. AI-assisted MAC for reconfigurable intelligent-surface-aided wireless networks: challenges and opportunities. IEEE Commun Mag, 59(6):21-27.

[6]Cao XL, Yang B, Huang CW, et al., 2021b. Reconfigurable-intelligent surface-assisted aerial-terrestrial communications via multi-task learning. IEEE J Sel Areas Commun, 39(10):3035-3050.

[7]Cao XL, Yang B, Zhang HL, et al., 2021c. Reconfigurable-intelligent-surface-assisted MAC for wireless networks: protocol design, analysis, and optimization. IEEE Int Things J, 8(18):14171-14186.

[8]Chen J, Liang YC, Pei YY, et al., 2019. Intelligent reflecting surface: a programmable wireless environment for physical layer security. IEEE Access, 7:82599-82612.

[9]Chen WC, Bai L, Tang WK, et al., 2020. Angle-dependent phase shifter model for reconfigurable intelligent surfaces: does the angle-reciprocity hold? IEEE Commun Lett, 24(9):2060-2064.

[10]Cui MY, Wu ZD, Lu Y, et al., 2023. Near-field MIMO communications for 6G: fundamentals, challenges, potentials, and future directions. IEEE Commun Mag, 61(1):40-46.

[11]Dai LL, Wang BC, Wang M, et al., 2020. Reconfigurable intelligent surface-based wireless communications: antenna design, prototyping, and experimental results. IEEE Access, 8:45913-45923.

[12]di Renzo M, Zappone A, Debbah M, et al., 2020. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead. IEEE J Sel Areas Commun, 38(11):2450-2525.

[13]ElMossallamy MA, Zhang HL, Song LY, et al., 2020. Reconfigurable intelligent surfaces for wireless communications: principles, challenges, and opportunities. IEEE Trans Cogn Commun Netw, 6(3):990-1002.

[14]Esmaeili H, Ahmad AA, Nadeem QUA, et al., 2022. Fairness analysis in IRS assisted C-RAN with imperfect CSI. IEEE Globecom Workshops, p.1010-1015.

[15]Gao YL, Yong C, Xiong ZH, et al., 2020. Reconfigurable intelligent surface for MISO systems with proportional rate constraints. IEEE Int Conf on Communications, p.1-7.

[16]Grant M, Boyd S, 2014. CVX: Matlab Software for Disciplined Convex Programming. Version 2.1. Available from http://cvxr.com/cvx [Accessed on Feb. 1, 2023].

[17]Hou TW, Liu YW, Song ZY, et al., 2020a. MIMO-NOMA networks relying on reconfigurable intelligent surface: a signal cancellation-based design. IEEE Trans Commun, 68(11):6932-6944.

[18]Hou TW, Liu YW, Song ZY, et al., 2020b. Reconfigurable intelligent surface aided NOMA networks. IEEE J Sel Areas Commun, 38(11):2575-2588.

[19]Huang CW, Zappone A, Alexandropoulos GC, et al., 2019. Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans Wirel Commun, 18(8):4157-4170.

[20]Huang CW, Hu S, Alexandropoulos GC, et al., 2020a. Holographic MIMO surfaces for 6G wireless networks: opportunities, challenges, and trends. IEEE Wirel Commun, 27(5):118-125.

[21]Huang CW, Mo RH, Yuen C, 2020b. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning. IEEE J Sel Areas Commun, 38(8):1839-1850.

[22]Huang KJ, Sidiropoulos ND, 2016. Consensus-ADMM for general quadratically constrained quadratic programming. IEEE Trans Signal Process, 64(20):5297-5310.

[23]Jian MN, Gao FF, Tian Z, et al., 2019. Angle-domain aided UL/DL channel estimation for wideband mmWave massive MIMO systems with beam squint. IEEE Trans Wirel Commun, 18(7):3515-3527.

[24]Li HY, Cai WH, Liu Y, et al., 2021. Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: from practical model to reflection optimization. IEEE Trans Commun, 69(7):4807-4820.

[25]Li ZR, Gao Z, Li T, 2023. Sensing user’s channel and location with terahertz extra-large reconfigurable intelligent surface under hybrid-field beam squint effect. IEEE J Sel Top Signal Process, 17(4):893-911.

[26]Lin SE, Zheng BX, Alexandropoulos GC, et al., 2020. Adaptive transmission for reconfigurable intelligent surface-assisted OFDM wireless communications. IEEE J Sel Areas Commun, 38(11):2653-2665.

[27]Liu YW, Liu X, Mu XD, et al., 2021. Reconfigurable intelligent surfaces: principles and opportunities. IEEE Commun Surv Tut, 23(3):1546-1577.

[28]Lobo MS, Vandenberghe L, Boyd S, et al., 1998. Applications of second-order cone programming. Linear Algebra Appl, 284(1-3):193-228.

[29]Luo ZQ, Ma WK, So AMC, et al., 2010. Semidefinite relaxation of quadratic optimization problems. IEEE Signal Process Mag, 27(3):20-34.

[30]Marks BR, Wright GP, 1978. Technical note—a general inner approximation algorithm for nonconvex mathematical programs. Oper Res, 26(4):681-683.

[31]Moré JJ, 1978. The Levenberg-Marquardt algorithm: implementation and theory. Proc Biennial Conf on Numerical Analysis, p.105-116.

[32]Razaviyayn M, Hong MY, Luo ZQ, 2013. A unified convergence analysis of block successive minimization methods for nonsmooth optimization. SIAM J Optim, 23(2):1126-1153.

[33]Shen KM, Yu W, 2018a. Fractional programming for communication systems—Part I: power control and beamforming. IEEE Trans Signal Process, 66(10):2616-2630.

[34]Shen KM, Yu W, 2018b. Fractional programming for communication systems—Part II: uplink scheduling via matching. IEEE Trans Signal Process, 66(10):2631-2644.

[35]Shi QJ, Hong MY, 2020. Penalty dual decomposition method for nonsmooth nonconvex optimization—Part I: algorithms and convergence analysis. IEEE Trans Signal Process, 68:4108-4122.

[36]Smith DR, Yurduseven O, Mancera LP, et al., 2017. Analysis of a waveguide-fed metasurface antenna. Phys Rev Appl, 8(5):054048.

[37]Sun Y, Babu P, Palomar DP, 2017. Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans Signal Process, 65(3):794-816.

[38]Tang WK, Dai JY, Chen MZ, et al., 2020. MIMO transmission through reconfigurable intelligent surface: system design, analysis, and implementation. IEEE J Sel Areas Commun, 38(11):2683-2699.

[39]Tejera P, Utschick W, Bauch G, et al., 2006. Subchannel allocation in multiuser multiple-input-multiple-output systems. IEEE Trans Inform Theory, 52(10):4721-4733.

[40]Tian Z, Chen ZC, Wang M, et al., 2022. Reconfigurable intelligent surface empowered optimization for spectrum sharing: scenarios and methods. IEEE Veh Technol Mag, 17(2):74-82.

[41]Wu QQ, Zhang R, 2020. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 58(1):106-112.

[42]Yang P, Xiao Y, Xiao M, et al., 2019. 6G wireless communications: vision and potential techniques. IEEE Netw, 33(4):70-75.

[43]Yang YF, Zheng BX, Zhang SW, et al., 2020. Intelligent reflecting surface meets OFDM: protocol design and rate maximization. IEEE Trans Commun, 68(7):4522-4535.

[44]You L, Xiong JY, Ng DWK, et al., 2021. Energy efficiency and spectral efficiency tradeoff in RIS-aided multiuser MIMO uplink transmission. IEEE Trans Signal Process, 69:1407-1421.

[45]Zhang SW, Zhang R, 2020. Capacity characterization for intelligent reflecting surface aided MIMO communication. IEEE J Sel Areas Commun, 38(8):1823-1838.

[46]Zhao J, 2019. Optimizations with intelligent reflecting surfaces (IRSs) in 6G wireless networks: power control, quality of service, max-min fair beamforming for unicast, broadcast, and multicast with multi-antenna mobile users and multiple IRSs. http://arxiv.org/abs/1908.03965

[47]Zheng BX, You CS, Zhang R, 2021. Double-IRS assisted multi-user MIMO: cooperative passive beamforming design. IEEE Trans Wirel Commun, 20(7):4513-4526.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE