Mode selection and resource optimization for UAV-assisted cellular networks
冯大权 1郑灿健 2孔祥琦3
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作者信息
1. 深圳大学电子与信息工程学院,广东 深圳 518055
2. 哈尔滨工业大学(深圳)电子与信息工程学院,广东 深圳 518060
3. 国家无线电监测中心检测中心,北京 100041
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摘要
研究了无人机(UAV,unmanned aerial vehicle)与蜂窝网络共存下的无线通信资源分配和优化方案.为了提高网络频谱利用率,无人机用户以全双工(FD,full duplex)或半双工(HD,half duplex)设备对设备(D2D,device-to-device)技术复用蜂窝频谱资源接入网络.此外,构造了一个联合接入控制、模式选择、功率控制和资源分配优化问题最大化网络的整体吞吐量,并保证无人机用户和地面蜂窝用户的服务质量要求.为解决这个问题,首先利用凸优化的第一阶段方法分别对全双工和半双工两种设备对设备模式进行接入控制和可行性判定.然后,对可接入无人机用户对使用凸凹过程(CCCP,convex and concave procedure)迭代算法求解功率控制问题.利用该局部最优值,原优化问题可以简化为加权最大化问题.最后,通过库恩-芒克斯(KM,Kuhn-Munrkes)算法对最优信道资源进行匹配,获得系统的全局最优吞吐量值.数值结果表明,所提方案能显著提高系统性能.
Abstract
The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial ve-hicle(UAV)and cellular communication network.To improve spectrum efficiency of the system,UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Addi-tionally,a joint access control,mode selection,power control and resource allocation optimization problem was formu-lated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV us-ers and ground cellular users.Specifically,the phase 1 method in the convex optimization was adopted for access control and feasibility check,and then the convex and concave procedure(CCCP)iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value,the original optimization problem can be simplified into a weighted maximization problem.Finally,the Kuhn-Munkres(KM)algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.
关键词
无人机通信/全双工设备对设备技术/模式选择/功率控制/资源分配与优化
Key words
unmanned aerial vehicle communication/full duplex device to device technique/mode selection/power con-trol/resources allocation and optimization
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