Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network.

中文翻译：

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使用强化学习的应急蜂窝网络分布式无人机基站定位。

由于自然灾害的不可预测性，每当发生灾难时，至关重要的是不仅要准备应急救援队，而且要有功能正常的通信网络基础设施。因此，为了防止人员伤亡，至关重要的是网络运营商能够尽快部署应急基础设施。从这个意义上讲，通过使用无人机（无人驾驶飞机）来部署智能，移动和适应性网络被认为是紧急情况的一种可能选择。本文提出了一种基于强化学习的智能解决方案，以便在紧急情况下找到多个无人机小蜂窝（DSC）的最佳位置。提出的解决方案的主要目标是使系统所覆盖的用户数量最大化，而无人机则受回程和无线电接入网络的限制。结果表明就所考虑的所有指标而言，Q学习解决方案的性能大大优于所有其他方法。因此，智能DSC被认为是一种很好的选择，以便能够快速有效地部署紧急通信网络。