基于QoE的无人机网络部署和缓存策略优化方法

QoE-based optimization method for UAV network deployment and cache strategy

为了最大限度提升用户的体验质量,设计了一个使用支持缓存的无人机进行辅助通信的蜂窝网络模型。该模型使用无人机通信和边缘缓存相结合的方式来进行流量卸载,通过联合优化无人机部署,缓存放置和用户关联以实现用户QoE的最大化,并使用平均意见得分来对其进行评估。基于问题定义,提出了一个无人机部署、缓存放置和用户关联的联合优化算法,以最大化MOS。该联合算法使用K-means聚类算法为用户创建基于用户位置的无人机部署集群,使用基于罚函数的凸优化算法以获得缓存放置策略,使用贪心算法以获得对用户的关联策略。多次仿真实验验证了所提出算法的有效性和可行性。实验结果表明,与三种基准算法相比,所提出的联合优化算法在MOS、回程流量卸载率和内容访问时延三方面均具有更好的性能。

In order to maximize the quality of experience(QoE)of users,this paper designed a cellular network model using cache-enabled unmanned aerial vehicle(UAV)for assisted communication.This model used a combination of UAV communication and edge caching to offload traffic,aiming to maximize QoE by jointly optimizing UAV deployment,cache placement and user association,and this paper used mean opinion score(MOS)to evaluate QoE.Based on the problem definition,this paper proposed a joint optimization algorithm for UAV deployment,cache placement and user association to maximize the MOS.This algorithm used K-means algorithm to create clusters for users to deploy UAV based on user locations,used penalty function-based convex optimization algorithm to obtain a cache placement strategy and greedy algorithm to obtain an association strategy for users.Several simulation experiments verify the effectiveness and feasibility of the proposed algorithm.The experimental results show that the proposed joint optimization algorithm has better performance in terms of MOS,backhaul traffic offloading and content access delay compared with the three benchmark algorithms.