移动边缘计算中的无人机三维部署和内容缓存优化方法  

UAV 3D deployment and content caching optimization for mobile edge computing

作　　者：唐焕博 陈星[1,2,3] 张建山[2,4] Tang Huanbo;Chen Xing;Zhang Jianshan(College of Computer&Data Science,Fuzhou University,Fuzhou 350116,China;Fujian Key Laboratory of Network Computing&Intelligent Information Processing,Fuzhou University,Fuzhou 350116,China;Key Laboratory of Spatial Data Mining&Information Sharing,Ministry of Education,Fuzhou 350002,China;College of Computer&Control Engineering,Minjiang University,Fuzhou 350108,China)

机构地区：[1]福州大学计算机与大数据学院,福州350116 [2]福州大学福建省网络计算与智能信息处理重点实验室,福州350116 [3]教育部空间数据挖掘与信息共享教育部重点实验室,福州350002 [4]闽江学院计算机与控制工程学院,福州350108

出　　处：《计算机应用研究》2024年第4期1143-1149,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(62072108);福建省自然科学基金杰青项目(2020J06014);中央引导地方科技发展资金资助项目(2022L3004);福厦泉国家自主创新示范区协同创新平台项目(2022FX5);福建省科技经济融合服务平台项目(2023XRH001)。

摘　　要：随着无线网络中的移动数据流量爆炸式增长,支持高速缓存的无人机被应用于移动计算领域充当边缘服务器,为网络中的用户提供按需服务。为了在满足其他资源约束的条件下,给用户带来更好的体验,通过联合优化无人机部署、缓存放置和用户关联以实现最小化所有用户的内容访问时延,并为用户提供质量不同的内容缓存服务。针对多无人机和地面基站协同提供缓存服务的场景,提出了一种基于迭代优化的联合优化算法。该算法通过迭代求解由目标问题分解得到的三个子问题的方式来获得具有收敛性保证的次优解决方案。首先,采用基于连续凸近似的算法求解无人机部署子问题;其次,采用基于贪心的算法求解内容缓存子问题;然后,利用基于罚函数的连续凸近似算法求解用户关联子问题;最后,对上述过程重复迭代,得到目标问题的一个次优解。多次仿真实验验证了所提算法的有效性和可行性。仿真结果表明,与基准算法相比,所提联合优化算法在平均内容访问时延、缓存命中率两方面均具有更好的性能。With the explosive growth of mobile data traffic in wireless networks,cache-enabled unmanned aerial vehicles(UAVs)are being used in mobile computing to serve as edge servers to provide on-demand services to users in the network.To bring better quality of experience to users while satisfying other resource constraints,it jointly optimized UAVs deployment,cache placement and user association to minimize content access latency for all users and provided users with content caching services of varying quality.This paper proposed a joint optimization algorithm based on alternating optimization for the scenario where multiple UAVs and ground base stations collaborate to provide caching services.The algorithm achieved a sub-optimal solution with a convergence guarantee by iteratively solving the three sub-problems obtained from the decomposition of the objective problem.Firstly,a successive convex approximation-based algorithm solved the UAV deployment sub-problem.Secondly,it used a greedy-based algorithm to solve the content caching sub-problem.Then it used a successive convex approximation algorithm based on the penalty function to solve the user access sub-problem.Finally,it repeated the above procedure to obtain a sub-optimal solution to the target problem.Several simulation experiments verified the effectiveness and feasibility of the proposed algorithm.The simulation results show that the proposed joint optimization algorithm performs better regarding average content access latency and cache hit ratio than the benchmark algorithm.

关 键 词：移动边缘计算 无人机三维部署 内容缓存 用户关联 凸优化 

分 类 号：TP393.0[自动化与计算机技术—计算机应用技术]