摘要
近年来,有源可重构智能表面(ARIS)技术获得了学术界的广泛关注。然而,ARIS在多RIS辅助无线通信系统中的应用还缺乏相关研究。针对此问题,该文提出基于双RIS辅助的无线通信系统模型。模型假设基站(BS)和用户之间的直连链路受阻,仅通过RIS形成的反射链路进行通信。在此基础上,根据ARIS与被动RIS(PRIS)的不同组合情况,提出4种RIS组合模型。模型的目标是优化基站波束赋形、RIS的相移矩阵和功率分配因子,以最大化系统通信容量。由于该优化问题为非凸问题,该文采用了交替优化算法(AO)与连续凸逼近(SCA)对问题进行处理。仿真结果表明,无论基站发射功率高或低,TAAR组合模型的性能均显著优于传统单ARIS配置。
Objective With the continuous advancement of research on Reconfigurable Intelligent Surfaces(RIS),various application scenarios have emerged.Among these,Active Reconfigurable Intelligent Surfaces(ARIS)attracts significant attention from the academic community.While some studies focus on dual Passive RIS-assisted communication systems,others investigate dual RIS-assisted systems incorporating ARIS.Existing literature consistently demonstrates that dual RIS configurations outperform single RIS setups in terms of achievable Signal-to-Noise Ratio(SNR),power gain,and energy transfer efficiency,with dual RIS systems achieving approximately ten times higher energy transfer efficiency.However,most existing studies on RIS focus on optimizing the performance of reflection coefficients in one or more distributed RIS-aided systems,primarily serving users within their respective coverage areas,without sufficiently addressing the benefits of single-reflection links.While dual RIS systems can effectively mitigate the limitations of antenna numbers and improve transmission reliability and efficiency,single-reflection links can still significantly enhance channel capacity,especially under low transmission power conditions.This paper proposes a novel approach wherein dual-reflection links and two single-reflection links jointly serve users.The goal is to maximize the downlink capacity of dual RIS-assisted Multiple-Input Single-Output(MISO)systems by strategically configuring the interaction between the two RISs.Methods In this paper,four combinatorial models of RIS are investigated:the Transmitter-PRIS PRISReceiver(TPPR),Transmitter-ARIS PRIS-Receiver(TAPR),Transmitter-PRIS ARIS-Receiver(TPAR),and Transmitter-ARIS ARIS-Receiver(TAAR).The optimization objective of all models is to maximize the communication rate by optimizing the antenna beamforming vector of the base station and the phase shift matrix of the RIS.Due to the coupling of the three variables in the objective function,the model is non-convex,making it difficult to obtain an optimal solution.To address the coupling problem,the Alternating Optimization(AO)algorithm is employed,where one phase shift vector is fixed while the other is optimized alternately.To tackle the non-convex problem,SCA is applied to iteratively approximate the optimal solution by solving a series of convex subproblems.Results and Discussions Building on the research methods outlined above and employing the SCA and AO algorithms,experimental results are obtained.The system capacity of each combination model increases with rising amplification power(Fig.2).However,once the amplification factor reaches a certain threshold,the capacity curves of all models begin to flatten due to the constraints imposed by the maximum amplification power.Further demonstration of the system capacity performance of different combination models as transmit power increases is shown in(Fig.3).Across all dual-RIS combination models,system capacity improves with higher transmit power and outperforms the Single-Active model in all scenarios.In(Fig.3(a)),under low transmit power conditions,regions of the curves corresponding to higher amplification power overlap due to the constraint of the amplification factor.As transmit power increases,system capacity stabilizes,which can be attributed to the proximity of ARIS to the base station,allowing it to receive stronger signals.Under high transmit power,system capacity continues to improve due to the influence of PRIS.Unlike ARIS,PRIS reflects the optimized signal path without being constrained by amplification power.Consequently,as transmit power increases,the signal strength received by PRIS is enhanced.In(Fig.3(b)),system capacity increases with transmit power,showing trends similar to those in(Fig.3(a)).In the TPAR combined model,the amplification factor constraint dominates,causing the system capacity curves to exhibit similar behavior across different amplification power levels.Under low transmit power,the signal strength at ARIS does not exceed the maximum amplification power budget.As transmit power increases,the amplification power constraint increasingly affects system capacity,leading to a gradual slowdown in the curve’s upward trend until it flattens.At high transmit power levels,the system capacity curve of the TPAR model levels off due to the low signal strength received by ARIS when it is positioned farther from the base station.This positioning necessitates higher transmit power to overcome the amplified power constraint.Thus,it is recommended that ARIS be deployed as close to the user as possible.In(Fig.3(c)),the TAAR combined model leverages the characteristics of both ARIS and PRIS in a dual ARIS-assisted scenario.Under low transmit power conditions,significant capacity gains are achieved.However,at high transmit power,the system capacity is constrained by the maximum amplification power of ARIS and eventually levels off.The system capacity trends in(Fig.3(a))and(Fig.3(b))consistently increase with higher transmit power.This is because both combination models integrate the advantages of PRIS and ARIS,ensuring high performance under both high and low transmit power conditions.In(Fig.3(d)),where ARIS is positioned on the user side,comparison with(Fig.3(c))reveals that,under high transmit power,the system capacity of both combination models is nearly identical,regardless of the amplification power level.This suggests that in strong transmit power scenarios,the additional gains from ARIS are limited.Conclusions This paper provides an in-depth analysis of the optimization of dual RIS-assisted MISO communication systems,confirming their superiority over single RIS configurations.However,several potential research directions remain unexplored.Most current studies assume ideal channel models,whereas real-world applications often involve complex channel conditions that significantly affect system performance.Future research could investigate the performance of dual RIS systems under these practical conditions,paving the way for more robust and applicable solutions.
作者
谢文武
张沁可
梁锡涛
刘晨宇
余超
王骥
XIE Wenwu;ZHANG Qinke;LIANG Xitao;LIU Chenyu;YU Chao;WANG Ji(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China;College of Physical Science and Technology,Central China Normal University,Wuhan 430079,China)
出处
《电子与信息学报》
北大核心
2025年第2期353-362,共10页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62472169)
湖南省自然科学基金(2023JJ50045,2024JJ7218,2024JJ7219)
湖南省教育厅项目(22B0676,23C0217)
湖南省大学生创新创业项目(S202410543061)。
关键词
双RIS
MISO
交替优化算法
凸逼近
Double Reconfiguration Intelligent Surface(RIS)
Multiple-Input Single-Output(MISO)
Alternating Optimization(AO)algorithm
Convex approximation
作者简介
谢文武:男,副教授,研究方向为5G&6G基带算法、NFC等;张沁可:女,硕士生,研究方向为RIS、凸优化;梁锡涛:男,硕士生,研究方向为5G/6G无线通信;刘晨宇:男,硕士生,研究方向为RIS、凸优化;通信作者:余超:男,讲师,研究方向为NOMA、凸优化,12012017@hnist.edu.cn;王骥:男,副教授,研究方向为5G/6G无线通信。
