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Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

机译:QoS保证voyifi服务的节能无人机部署

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摘要

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m as well as in a large area of 10,000 m . Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.
机译:本文制定了一个新的问题,以实现无人驾驶飞行器(无人机)的最佳放置,以便在WiFi(voyifi)服务中的无线覆盖范围内的无线覆盖范围提供给一组局限于开放区域的地面用户。我们的目标函数受到覆盖范围和VoIP语音质量的约束,并最大限度地减少了无人机中部署和能源效率之间的比率,因此提供了每小时需要更少的无人机的布局。解决方案提供要部署的UAV的数量和位置,并使用众所周知的启发式搜索方法(如遗传算法)(用于初始部署UAV),或用于粒子群优化(用于定期更新的位置) 。我们检查两个通信服务:(a)每个用户的一个双向voyifi频道; (b)单行广播voyifi通告通告。对于这些服务,我们研究了越来越多的用户在100米的小面积中获得的结果获得的结果,以及在10,000米的大面积。结果表明,无人机周转率与用户的稀疏性和每个UAV服务的用户数量有关。对于单播服务,每小时服务的无人机比率往往随着用户稀疏性而增加,无线电通信的功率代表了根据地面用户密度的总UAV能量消耗的14-16%。在大面积中,解决方案倾向于在寻求增加的覆盖范围的较高海拔地区定位无人机,这增加了由于悬停引起的能量消耗。但是,在vowifi广播通信服务中,流量稀缺,并且解决方案仅受覆盖范围而受到限制。这导致未能部署的无人机,较少的总功耗(20%至75%),并且对服务用户数量的敏感性较少。

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