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针对室内可见光通信（Visible Light Communication, VLC）系统中非正交多址接入（Non-Orthogonal Multiple Access,NOMA）的功率分配问题，提出一种差异化服务质量（Quality of Service, QoS）保障的功率分配（Differentiated QoS Guarantee Power Allocation, DQGPA）算法。建立NOMA-VLC系统模型，并推导用户的数据传输速率表达式。采用泊松过程对数据到达过程进行建模，并计算其有效带宽。综合考虑功率分配、差异化QoS保障与用户公平性，构建了以系统和速率最大化为目标的优化问题。引入精英引导策略和自适应学习因子对鲸鱼优化算法（Whale Optimization Algorithm, WOA）进行改进，并基于改进的WOA(Improved Whale Optimization Algorithm, IWOA)设计功率分配算法。利用Matlab仿真验证IWOA改进策略的有效性，进一步证明了所提算法在系统和速率、用户公平性及差异化QoS保障的综合优越性，并分析系统带宽、视场角（Field of View, FOV）和用户数量等参数变化对系统性能的影响。
Abstract：To address the power allocation issue in Non-Orthogonal Multiple Access(NOMA) for indoor Visible Light Communication(VLC) systems, considering quality of service(QoS), a differentiated QoS-guaranteed power allocation(DQGPA) algorithm is proposed. The NOMA-VLC system model is established, and the expression for the data transmission rate of user is derived. The data arrival process is modeled using the Poisson process, and its effective bandwidth is calculated. Considering power allocation, differentiated QoS guarantee, and user fairness comprehensively, an optimization problem is formulated to maximize the sum of system rates. An elite-guided strategy and an adaptive learning factor are introduced to improve the Whale Optimization Algorithm(WOA). Based on the improved Whale Optimization Algorithm(IWOA), a power allocation algorithm is designed. Matlab simulations are conducted to verify the effectiveness of the IWOA improvement strategy and the superiority of the proposed power allocation algorithm. Furthermore, the impact of changes in system parameters, such as system bandwidth, Field of View(FOV), and the number of users, on system performance is analyzed.
[1]齐林,李宗锋,崔积学.可见光通信干扰信号在低复杂度网络中的定位研究[J].激光杂志, 2026, 47(03):172-176.
[2]HOU Y, WANG Z, Li Z, et al. Laser-based mobile visible light communication system[J]. Sensors, 2024, 24(10):3086.
[3]吕静轩,卢宁宁,张海鹏,等.极化码编译码技术研究及在6G中的应用[J].无线电通信技术,2025,51(06):1369-1378.
[4]徐美欣,张峰,赵黎,等.联合DNN信道估计与虚拟时间反转信道均衡的NOMA-VLC系统[J].光通信技术, 2025, 49(01):44-51.
[5]李碧玲,宋荣方.可重构折射面辅助下行NOMA系统和速率最大化[J].南京邮电大学学报(自然科学版), 2025, 45(05):50-56.
[6]刘星,王旭东,吴楠.基于功率域NOMA的可见光CAP混合调制[J].移动通信, 2025, 49(02):108-114+131.
[7]何滨江,张峰,赵黎,等. NOMA-VLC系统中基于信道容量的功率分配方法[J].佳木斯大学学报(自然科学版), 2025, 43(01):20-24.
[8]张峰,侯林欣,何滨江,等.基于CNN信噪比估计的MB分布概率整形NOMA-VLC系统研究[J].光子学报, 2025, 54(08):56-67.
[9]HAO L, CAO P, Li C, et al. A novel sum-rate maximization scheme for NOMA-VLC systems via the black widow enhanced Kepler optimization algorithm[J]. Optics Communications, 2024, 573:130979.
[10]WANG X, WANG H, DONG Z, et al. An NOMA-VLC power allocation scheme for multi-user based on sparrow search algorithm[J]. Optoelectronics Letters, 2025, 21(5):278-283.
[11]SUN Y, WANG W, LIU T, et al. Enhanced coati optimization algorithm and its application in power allocation for NOMA-VLC systems[J]. Optics Communications, 2025, 579:131577.
[12]秦雨晴,刘昊钧,张晨,等.面向用户QoS的低轨卫星混合NOMA资源调度研究[J].移动通信, 2025, 49(07):125-134.
[13]陈俐妍. QoS约束下5G网络D2D数据通信扩容算法设计[J].微型电脑应用, 2025, 41(07):60-62+66.
[14]AL-MOUSAWI A J. Wireless communication networks and swarm intelligence[J]. Wireless Networks, 2021, 27(3):1755-1782.
[15]VINOTHKUMAR K, VELMURUGAN T. Maximizing energy efficiency and system throughput using a self-adaptive penalty function with a whale optimization algorithm in social-aware device-to-device communications[J]. Results in Engineering, 2024,24:103272.
[16]YAKUBU A B, ABD EL-MALEK A H, ABO-ZAHHAD M, et al. Task offloading and resource allocation in an RIS-assisted NOMA-based vehicular edge computing[J]. IEEE Access, 2024, 12:124330-124348.
[17]黄菊,赵响,赵玉青,等.基于NOMA的可见光无线移动系统的隐蔽通信[J].光通信技术, 2025, 49(01):17-24.
[18]周围,徐锐,李倩倩,等.基于NOMA的室内可见光通信最优功率分配方案[J].数据采集与处理, 2024, 39(05):1297-1308.
[19]王艳红,雷松泽,张文娟,等.到达时间服从泊松分布的平行机队列的最优随机排序问题[J].计算机与数字工程, 2024, 52(02):403-405+422.
[20]CHANG C S, THOMAS J A. Effective bandwidth in high-speed digital networks[J]. IEEE Journal on Selected areas in Communications, 1995, 13(6):1091-1100.
[21]闫阳阳,葛文萍,陈娟.电力物联网中基于SCMA-MEC的最优功率分配与卸载决策[J].无线电通信技术, 2024, 50(04):640-646.
[22]齐铖,谢军伟,王雪,等.基于精英引导的社会学习粒子群优化算法[J].西北工业大学学报, 2024, 42(05):948-958.
[23]杨新星,王随,张晓峰,等.基于自适应学习因子的哈里斯鹰优化算法[J].雷达与对抗, 2024, 44(04):12-18.
[24]程广鹏,杨玮.考虑远端用户随机位置的协作NOMA-VLC性能分析[J].北京信息科技大学学报(自然科学版), 2026, 41(01):120-130.
[25]刘焕淋,胡阳,陈勇,等.室内VLC-NOMA网络的协作多点用户接入和功率分配[J].电子学报, 2024, 52(12):4009-4014.
[26]吴丽卓,苏颖,张静.基于流向算法的协同雷达-NOMA通信系统布站优化[J].无线电工程, 2023, 53(12):2820-2826.
[27]王莹,张峰,彭侠,等.菲涅尔变换正交啁啾调制非正交多址光通信系统[J].应用光学, 2025, 46(05):1143-1152.
[28]OTAO N, KISHIYAMA Y, HIGUCHI K. Performance of non-orthogonal multiple access with SIC in cellular downlink using proportional fair-based resource allocation[J]. IEICE transactions on communications, 2015, 98(2):344-351.
基本信息:
中图分类号:TN929.1
引用信息:
[1]杨明,刘宇涵,孙洪亮,等.面向差异化QoS保障的NOMA-VLC系统功率分配算法[J].无线电通信技术().
基金信息:
吉林省教育厅科学研究项目（JJKH20261250KJ）; 吉林省科技发展计划项目（YDZJ202401383ZYTS）
2026-05-12
2026-05-12
2026-05-12