QoS约束下的IRS-MU-OAM-OFDMA下行资源优化方法OA
IRS-MU-OAM-OFDMA Downlink Resource Optimization under QoS Constraints
针对传统轨道角动量(Orbital Angular Momentum,OAM)通信系统难以在视距信道受阻塞的非视距环境中正常工作以及无法有效保障多用户的服务质量(Quality of Service,QoS)需求问题,文中基于智能反射表面辅助技术将多用户的非视距信道转化为等效的视距信道,并在此场景下提出基于太赫兹多用户OAM正交频分多址系统下行资源优化方法.基于双层迭代资源分配算法将非凸联合优化的求解分解成外部和内部两个优化流程,基于交替优化和凸优化理论逐一求解 4 个核心子问题,实现各用户QoS差异化保障下的系统容量最大化.仿真结果表明,所提方法在通信资源充足时对各用户的QoS需求保障率为 100%.在反射单元数量为 768 时,所提系统比传统OAM系统的系统容量平均提高了 19.1%,并且误码率更低.在用户数量为 3、信噪比为 20 dB时,相较于基于相位补偿的MU(Multiuser)-OAM系统,所提系统的误码率下降了 40.5%.
In view of the problem that traditional OAM(Orbital Angular Momentum)communication systems struggle to operate normally in non-line-of-sight environments where line-of-sight channels are blocked,and fail to effectively guarantee the QoS(Quality of Service)requirements of multiple users,this study proposes a downlink re-source optimization method for terahertz multiuser OAM orthogonal frequency division multiple access systems based on intelligent reflecting surface assistance technology.The IRS technology converts the non-line-of-sight channels of multiple users into equivalent line-of-sight channels.In this scenario,a two-layer iterative resource allocation algo-rithm is used to decompose the solution of the non-convex joint optimization problem into external and internal opti-mization processes.Four core subproblems are solved one by one based on the alternating optimization and convex op-timization theories to maximize the system capacity while ensuring differentiated QoS for each user.Simulation results show that the proposed method achieves a 100%QoS requirement guarantee rate for each user when communication resources are sufficient.When the number of reflecting units is 768,the system capacity of the proposed method is on average 19.1%higher than that of the traditional OAM system,with a lower bit error rate.When the number of users is 3 and the signal-to-noise ratio is 20 dB,the bit error rate of the proposed system is 40.5%lower than that of the phase compensation-based MU(Multiuser)-OAM system.
LAN Shicai;DOU Haie;WANG Lei;YAO Jiming;XIA Zhijie
Key Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,China||State Grid Smart Grid Research Institute Co.,Ltd.,Beijing 102211,ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
信息技术与安全科学
资源优化OAM智能反射表面QoS非视距信道双层迭代联合优化交替优化
resource optimizationOAMintelligent reflective surfaceQoSnon line-of-sighttwo-layer itera-tionjoint optimizationalternating optimization
《电子科技》 2026 (2)
1-8,8
国家自然科学基金(62071255)江苏省重点研发计划(BE2023087)江苏省高等学校自然科学研究重大项目(20KJA510009)南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金(JZNY202312)National Natural Science Foundation of China(62071255)Key R&D Program of Jiangsu(BE2023087)Major Natural Science Research Pro-jects of Jiangsu Higher Education Institutions(20KJA510009)Open Research Fund of Key Laboratory of Broadband Wireless Communica-tion and Sensor Network Technology of Ministry of Education,Nanjing University of Posts and Telecommunications(JZNY202312)
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