计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (12): 285-293.doi: 10.11896/jsjkx.250100016

• 计算机网络 • 上一篇    下一篇

多簇NOMA-UAV网络的节能轨迹与资源优化

李志坷, 徐涴砯   

  1. 上海海事大学信息工程学院 上海 201306
  • 收稿日期:2025-01-03 修回日期:2025-03-27 出版日期:2025-12-15 发布日期:2025-12-09
  • 通讯作者: 徐涴砯(wpxu@shmtu.edu.cn)
  • 作者简介:(202230310025@stu.shmtu.edu.cn)
  • 基金资助:
    上海市青年科技英才扬帆计划(20YF1416700)

Energy-efficient Trajectory and Resource Optimization for Multi-cluster NOMA-UAV Networks

LI Zhike, XU Wanping   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Received:2025-01-03 Revised:2025-03-27 Published:2025-12-15 Online:2025-12-09
  • About author:LI Zhike,born in 1999,postgraduate.Her main research interests include wireless communication and UAV communication.
    XU Wanping,born in 1988,Ph.D,postgraduate supervisor.Her main research interests include information and intelligent communication technology and marine Internet.
  • Supported by:
    This work was supported by the Shanghai Sailing Program(20YF1416700).

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摘要： 针对无人机(Unmanned Aerial Vehicle,UAV)辅助多簇非正交多址(Non-Orthogonal Multiple Access,NOMA)下行网络中有限资源下的服务质量(Quality of Service,QoS)保障问题,提出了一种优化方案。UAV作为空中移动基站,向地面用户提供通信业务。由于能量有限,为使更多能量用于通信,将通信(悬停)时间作为优化变量,通过分别优化用户分簇、簇内用户功率分配和通信时间分配来最大化总吞吐量。由于其非凸性,将其分解为3个子问题,其中功率分配问题采用逐次凸逼近方法(Successive Convex Approximation,SCA)求解,而通信时间分配通过线性规划求解。首先,采用均值偏移(Mean Shift)算法进行用户分簇,相较于K-means算法,它通过计算局部密度峰值实现分簇,确保簇内用户相对集中;随后,考虑到该算法导致簇间用户数量不均衡,影响个体用户QoS,提出改进Mean Shift分簇算法,将用户数量较多的簇分裂为多个小簇;最后,为避免新增子簇增加飞行距离,提出本簇头悬停方案,并采用遗传算法(Genetic Algorithm,GA)进行轨迹优化,在保证用户QoS的前提下,通过减少UAV悬停节点的非通信能耗,来提升总吞吐量。该优化方案的计算复杂度较低,具有较强的实时性。仿真结果表明,改进Mean Shift算法的优化方案比K-means算法减少了非通信能耗,在不同的发射功率下,系统吞吐量平均提升了5.94%,在不同的用户数量下,能效平均提升了6.82%。

关键词: 无人机, 非正交多址接入, 服务质量, 能效, 轨迹优化

Abstract: An optimization scheme is proposed to ensure QoS guarantee in UAV-assisted multi-cluster NOMA downlink networks with limited resources.In this paper,the UAV serves as an airborne mobile base station to communicate with ground users.Due to limited energy,hovering time is introduced as an optimization variable to allocate more energy for communication.The total throughput is maximized by optimizing user clustering,intra-cluster power allocation,and communication time allocation.Due to its non-convexity,the optimization problem is divided into three sub-problems.The power allocation problem is addressed using the SCA method,and the communication time allocation is solved via linear programming.First of all,the Mean Shift algorithm is employed for user clustering.Unlike K-means,it clusters users by calculating local density peaks,ensuring higher intra-cluster user concentration.Then,an improved Mean Shift algorithm is proposed to balance user distribution by splitting oversized clusters,thereby ensuring individual user QoS.Finally,an original cluster head hovering scheme is introduced to avoid increasing the UAV’s flight distance due to additional sub-clusters,then GA is used for trajectory optimization,enhancing total throughput by reducing the UAV’s non-communication energy consumption while ensuring user QoS.The optimization scheme has low computational complexity and strong real-time performance.Simulation results show that the optimization scheme with an improved Mean Shift algorithm reduces the non-communication energy consumption than the K-means algorithm,and improves the system throughput by an average of 5.94% at different transmit power and energy efficiency by an average of 6.82% at different number of users.

Key words: Unmanned Aerial Vehicle, Non-orthogonal multiple access, Quality of service, Energy-efficient, Trajectory optimization

中图分类号: 

  • TN929.5
[1]FENG W,TANG J,ZHAO N,et al.NOMA-based UAV-aidednetworks for emergency communications[J].China Communications,2020,17(11):54-66.
[2]DANDAPAT J,GUPTA N,AGARWAL S,et al.Service duration maximization for continuous coverage in UAV-assisted communication system[J].IEEE Communications Letters,2022,26(10):2445-2449.
[3]YUAN X,HU Y,ZHANG J,et al.Joint user scheduling and UAV trajectory design on completion time minimization for UAV-aided data collection[J].IEEE Transactions on Wireless Communications,2023,22(6):3884-3898.
[4]TANG J M,HUANG J Q,WANG B W,et al.Resource optimization for multi-UAV assisted communication system based on user scheduling [J].Journal of Beijing University of Aeronautics and Astronautics,2025,51(4):1143-1151.
[5]KATWE M,SINGH K,SHARMA P K,et al.Energy efficiency maximization for UAV-assisted full-duplex NOMA system:user clustering and resource allocation[J].IEEE Transactions on Green Communications and Networking,2022,6(2):992-1008.
[6]WANG D,TIAN J,ZHANG H,et al.Task offloading and tra-jectory scheduling for UAV-enabled MEC networks:an optimal transport theory perspective[J].IEEE Wireless Communications Letters,2022,11(1):150-154.
[7]DAI L,WANG B,YUAN Y,et al.Non-orthogonal multiple access for 5G:solutions,challenges,opportunities,and future research trends[J].IEEE Communications Magazine,2015,53(9):74-81.
[8]GENDIA A,MUTA O,HASHIMA S,et al.Energy-efficienttrajectory planning with joint device selection and power splitting for mmWaves-enabled UAV-NOMA networks[J].IEEE Transactions on Machine Learning in Communications and Networking,2024,2(2):617-632.
[9]TANG N,TANG H,LI B,et al.Cognitive NOMA for UAV-enabled secure communications:joint 3D trajectory design and power allocation[J].IEEE Access,2020,8:159965-159978.
[10]XU Y,ZHANG T,YANG D,et al.Joint resource and trajectory optimization for security in UAV-assisted MEC systems[J].IEEE Transactions on Communications,2021,69(1):573-588.
[11]GHOMRI B I D,BENDIMERAD M Y,BENDIMERAD F T.DRL-driven optimization for energy efficiency and fairness in NOMA-UAV networks[J].IEEE Communications Letters,2024,28(5):1048-1052.
[12]FANG F,ZHANG H,CHENG J,et al.Energy-efficient resource allocation for downlink non-orthogonal multiple access network[J].IEEE Transactions on Communications,2016,64(9):3722-3732.
[13]CHU T M C,ZEPERNICK H J,DUONG T Q.NOMA-based full-duplex UAV network with K-means clustering for disaster scenarios [C]//Proceedings of the 2022 IEEE 96th Vehicular Technology Conference(VTC2022-Fall).Piscataway,NJ:IEEE,2022:1-7.
[14]HUANG Q,WANG W,LU W,et al.Resource allocation for multi-cluster NOMA-UAV networks[J].IEEE Transactions on Communications,2022,70(12):8448-8459.
[15]LIN Y,WANG K,DING Z.Unsupervised machine learning-based user clustering in THz-NOMA systems[J].IEEE Wireless Communications Letters,2023,12(7):1130-1134.
[16]COMANICIU D,MEER P.Mean shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[17]YANG Q Q,HAN Z T,PENG Y.User clustering and power allocation algorithm for Unmanned Aerial Vehicle assisted NOMA downlink[J].Journal of Beijing University of Posts and Telecommunications,2024,47(1):31-37.
[18]LIU A,LAU V K N,KANANIAN B.Stochastic successive convex approximation for non-convex constrained stochastic optimization[J].IEEE Transactions on Signal Processing,2019,67(16):4189-4203.
[19]IMMANUEL S D,CHAKRABORTY U K.Genetic algorithm:An approach on optimization [C]//Proceedings of the 2019 International Conference on Communication and Electronics Systems(ICCES).IEEE,2019:701-708.
[20]COMANICIU D,MEER P.Mean shift analysis and applications [C]//Proceedings of the Seventh IEEE International Conference on Computer Vision(ICCV).IEEE,1999:1197-1203.
[21]DING Z,SCHOBER R,POOR H V.Unveiling the importance of SIC in NOMA systems－Part 1:State of the art and recent fin-dings[J].IEEE Communications Letters,2020,24(11):2373-2377
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