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Can Adaptive Feedforward Control Improve Operation of Cloud Services?

机译:自适应前馈控制能否改善云服务的运营?

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We target the problem of providing 5G network connectivity in rural zones by means of Base Stations (BSs) carried by Unmanned Aerial Vehicles (UAVs). Our goal is to schedule the UAVs missions to: i) limit the amount of energy consumed by each UAV, ii) ensure the coverage of selected zones over the territory, ii) decide where and when each UAV has to be recharged in a ground site, iii) deal with the amount of energy provided by Solar Panels (SPs) and batteries installed in each ground site. We then formulate the RURALPLAN optimization problem, a variant of the unsplittable multicommodity flow problem defined on a multiperiod graph. After detailing the objective function and the constraints, we solve RURALPLAN in a realistic scenario. Results show that RURALPLAN is able to outperform a solution ensuring coverage but not considering the energy management of the UAVs.
机译:我们的目标是通过无人驾驶飞机(UAV)携带的基站(BS)在农村地区提供5G网络连接的问题。我们的目标是安排无人飞行器执行以下任务:i)限制每架无人飞行器消耗的能量; ii)确保在整个区域内选定区域的覆盖范围; ii)确定每台无人飞行器必须在何时何地充电,iii)处理每个地面站点中安装的太阳能电池板(SP)和电池提供的电量。然后,我们制定RURALPLAN优化问题,这是在多周期图上定义的不可分裂的多商品流问题的变体。详细说明目标函数和约束条件后,我们将在实际情况下求解RURALPLAN。结果表明,RURALPLAN能够胜过确保覆盖范围但不考虑无人机能源管理的解决方案。

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