Joint Unmanned Aerial Vehicle Location and Beamforming and Caching Optimization for Cache-Enabled Multi-Unmanned-Aerial-Vehicle Networks

被引:1
|
作者
Chen, Zikang [1 ,2 ]
Zeng, Ming [1 ,2 ]
Fei, Zesong [1 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Chongqing Innovat Ctr, Chongqing 400031, Peoples R China
关键词
cache-enabled multi-UAV network; UAV deployment; beamforming scheme; caching strategy; difference of convex; UAV; THROUGHPUT;
D O I
10.3390/electronics12163438
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the advantages such as high flexibility, low cost and easy implementation offered by unmanned aerial vehicles (UAVs), a UAV-assisted network is regard as an appealing solution to a seamless coverage, high disaster-tolerant and on-demand wireless system. In this paper, we focus on the downlink transmission in a cache-enabled UAV-assisted wireless communication network, where UAVs cache popular content from a macro base station in advance and cooperatively transfer the content to users. We aim to minimize the average transmission latency of the system and to formulate an optimization problem that jointly optimizes the UAV location, beamforming and caching strategy. However, the formulated problem is very challenging because of its non-convexity and the highly coupled optimization variables. To solve this resulting problem efficiently, we decompose it into two subproblems, namely UAV location and beamforming optimization, and UAV caching strategy optimization. The first subproblem is an NP-hard joint optimization problem, while the second one is a linear programing problem. By adopting the first-order Taylor expansion, we propose a convex optimization algorithm based on the difference-of-convex (DC) method. Specifically, we bring out a method to apply linear approximation in the DC-based algorithm, which is particularly suitable to the problems involving complicated summations. The numerical results demonstrate that the proposed DC-based iterative optimization algorithm can efficiently reduce the average transmission latency of the system.
引用
收藏
页数:16
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