Cellular networks are ubiquitous entities that provide major means of communication all over the world. One major challenge in cellular networks is a dynamic change in the number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is the deployment of drones that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in the development of this solution. Firstly, the drone is expected to be present around the base station where an overload would occur in the future thus requiring a prediction of traffic overload. Secondly, drones are highly constrained in their resources and can only fly for a few minutes. If the affected base station is really far, drones can never reach there. This requires the initial placement of drones in sectors where overloading can occur thus again requiring a traffic forecast but at a different spatial scale. It must be noted that the spatial extent of the region that the problem poses and the extremely limited power resources available to the drone pose a great challenge that is hard to overcome without deploying the drones in strategic positions to reduce the time to fly to the required high-demand zone. Moreover, since drone fly at a finite speed, it is important that a predictive solution that can forecast traffic surges is adopted so that drones are available to offload the overload before it actually happens. Both these goals require analysis and forecast of cellular network traffic which is the main goal of this project

翻译：蜂窝网络作为全球通信的主要基础设施，其用户数量和电信服务使用的动态变化导致部分基站出现负载过重的现象。针对这一过载问题，可部署无人机作为临时基站来分流过载基站流量的解决方案应运而生。该方案面临两大核心挑战：首先，无人机需提前部署在可能发生流量过载的基站附近，这要求实现流量过载的预测；其次，无人机资源极度受限且续航时间短（仅数分钟）。当受影响基站距离过远时，无人机将无法及时抵达。因此需要将无人机预先部署在可能发生流量过载的扇区，这同样需要流量预测，但需在不同空间尺度上进行。值得注意的是，问题涉及的空间区域范围与无人机极有限能源供给之间的矛盾，构成重大挑战。若不将无人机战略部署在能缩短飞抵高需求区域时间的优势位置，该矛盾将难以解决。此外，鉴于无人机飞行速度有限，必须采用能够预测流量激增的预测性方案，确保无人机在过载实际发生前完成分流准备。这些目标均依赖于蜂窝网络流量的分析与预测——这正是本项目的核心研究内容。