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.
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.
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Data availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
References
Bogomolnaia A, Jackson MO, 2002. The stability of hedonic coalition structures. Games Econ Behav, 38(2):201–230. https://doi.org/10.1006/game.2001.0877
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. https://doi.org/10.1561/2200000016
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. https://doi.org/10.1109/LCOMM.2020.2987322
Cai WH, Liu R, Li M, et al., 2022. IRS-assisted multi-cell multiband systems: practical reflection model and joint beamforming design. IEEE Trans Commun, 70(6):3897–3911. https://doi.org/10.1109/TCOMM.2022.3168645
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. https://doi.org/10.1109/MCOM.001.2001146
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. https://doi.org/10.1109/JSAC.2021.3088634
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. https://doi.org/10.1109/JIOT.2021.3068492
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. https://doi.org/10.1109/ACCESS.2019.2924034
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. https://doi.org/10.1109/LCOMM.2020.2993961
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. https://doi.org/10.1109/MCOM.004.2200136
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. https://doi.org/10.1109/ACCESS.2020.2977772
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. https://doi.org/10.1109/JSAC.2020.3007211
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. https://doi.org/10.1109/Tccn.2020.2992604
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. https://doi.org/10.1109/GCWkshps56602.2022.10008546
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. https://doi.org/10.1109/ICC40277.2020.9148766
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].
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. https://doi.org/10.1109/TCOMM.2020.3018179
Hou TW, Liu YW, Song ZY, et al., 2020b. Reconfigurable intelligent surface aided NOMA networks. IEEE J Sel Areas Commun, 38(11):2575–2588. https://doi.org/10.1109/JSAC.2020.3007039
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. https://doi.org/10.1109/TWC.2019.2922609
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. https://doi.org/10.1109/MWC.001.1900534
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. https://doi.org/10.1109/JSAC.2020.3000835
Huang KJ, Sidiropoulos ND, 2016. Consensus-ADMM for general quadratically constrained quadratic programming. IEEE Trans Signal Process, 64(20):5297–5310. https://doi.org/10.1109/TSP.2016.2593681
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. https://doi.org/10.1109/TWC.2019.2915072
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. https://doi.org/10.1109/TCOMM.2021.3069860
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. https://doi.org/10.1109/JSTSP.2023.3278942
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. https://doi.org/10.1109/JSAC.2020.3007038
Liu YW, Liu X, Mu XD, et al., 2021. Reconfigurable intelligent surfaces: principles and opportunities. IEEE Commun Surv Tut, 23(3):1546–1577. https://doi.org/10.1109/COMST.2021.3077737
Lobo MS, Vandenberghe L, Boyd S, et al., 1998. Applications of second-order cone programming. Linear Algebra Appl, 284(1–3):193–228. https://doi.org/10.1016/S0024-3795(98)10032-0
Luo ZQ, Ma WK, So AMC, et al., 2010. Semidefinite relaxation of quadratic optimization problems. IEEE Signal Process Mag, 27(3):20–34. https://doi.org/10.1109/Msp.2010.936019
Marks BR, Wright GP, 1978. Technical note—a general inner approximation algorithm for nonconvex mathematical programs. Oper Res, 26(4):681–683. https://doi.org/10.1287/opre.26.4.681
Moré JJ, 1978. The Levenberg-Marquardt algorithm: implementation and theory. Proc Biennial Conf on Numerical Analysis, p.105–116. https://doi.org/10.1007/BFb0067700
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. https://doi.org/10.1137/120891009
Shen KM, Yu W, 2018a. Fractional programming for communication systems—Part I: power control and beamforming. IEEE Trans Signal Process, 66(10):2616–2630. https://doi.org/10.1109/Tsp.2018.2812733
Shen KM, Yu W, 2018b. Fractional programming for communication systems—Part II: uplink scheduling via matching. IEEE Trans Signal Process, 66(10):2631–2644. https://doi.org/10.1109/TSP.2018.2812748
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. https://doi.org/10.1109/TSP.2020.3001906
Smith DR, Yurduseven O, Mancera LP, et al., 2017. Analysis of a waveguide-fed metasurface antenna. Phys Rev Appl, 8(5):054048. https://doi.org/10.1103/PhysRevApplied.8.054048
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. https://doi.org/10.1109/tsp.2016.2601299
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. https://doi.org/10.1109/JSAC.2020.3007055
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. https://doi.org/10.1109/TIT.2006.881751
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. https://doi.org/10.1109/MVT.2022.3157070
Wu QQ, Zhang R, 2020. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 58(1):106–112. https://doi.org/10.1109/Mcom.001.1900107
Yang P, Xiao Y, Xiao M, et al., 2019. 6G wireless communications: vision and potential techniques. IEEE Netw, 33(4):70–75. https://doi.org/10.1109/MNET.2019.1800418
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. https://doi.org/10.1109/TCOMM.2020.2981458
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. https://doi.org/10.1109/TSP.2020.3047474
Zhang SW, Zhang R, 2020. Capacity characterization for intelligent reflecting surface aided MIMO communication. IEEE J Sel Areas Commun, 38(8):1823–1838. https://doi.org/10.1109/jsac.2020.3000814
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
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. https://doi.org/10.1109/Twc.2021.3059945
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Yonghua QUAN, Zhong TIAN, and Zhengchuan CHEN designed the research. Yonghua QUAN processed the data and drafted the paper. Zhong TIAN and Zhengchuan CHEN helped organize the paper. All authors revised and finalized the paper.
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Yonghua QUAN, Zhong TIAN, Zhengchuan CHEN, Min WANG, and Yunjian JIA declare that they have no conflict of interest.
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Project supported by the Graduate Research and Innovation Foundation of Chongqing, China (No. CYB23050), the National Natural Science Foundation of China (Nos. 62271092 and 62001074), the Fundamental Research Funds for the Central Universities, China (No. 2023CDJXY-037), the China Postdoctoral Science Foundation (No. 2022M710534), the Natural Science Foundation of Chongqing, China (Nos. CSTB2023NSCQ-MSX0933 and CSTB2022NSCQMSX0327), the Open Fund of the Shaanxi Key Laboratory of Information Communication Network and Security, China (No. ICNS202201), and the Opening Project of the Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, China (No. GXKL06230206)
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Quan, Y., Tian, Z., Chen, Z. et al. Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model. Front Inform Technol Electron Eng 24, 1763–1775 (2023). https://doi.org/10.1631/FITEE.2300120
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DOI: https://doi.org/10.1631/FITEE.2300120