The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless back-haul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

中文翻译：

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通过考虑自动覆盖漏洞检测，无线回传和用户需求来优化3D UAV基站的位置

技术先进设备的数量不断增加，使得网络覆盖规划对于网络运营商而言是一项艰巨的任务。发射机和最终用户之间的传输质量必须是最佳的，以实现任何设备中的最佳性能。此外，覆盖漏洞的存在对于运营商来说也是一个持续存在的问题，在整个运营阶段都是不可忽视的。网络运营商覆盖区域中的任何覆盖漏洞都将阻碍通信应用并降低运营商服务的声誉。当前，存在用于检测覆盖漏洞的技术，例如驱动器测试或最小化驱动器测试。但是，这些方法有很多局限性。极端的成本 有关无线电环境和高时间消耗的过时信息无法完全满足要求。为了克服这些问题，我们利用无人飞行器（UAV）和Q学习来自动检测给定区域中的覆盖孔，然后通过考虑与核心网络的无线回程来部署基于UAV的基站（UAV-BS）和用户需求。这种机器学习机制将帮助无人机消除人在环（HiTL）模型。稍后，我们针对3D UAV-BS在各种角度位置的放置制定了一个优化问题，以最大化与UAV-BS相关联的用户数量。总而言之，我们在本文中说明了一种经济高效且节省时间的方法，可以检测覆盖孔并按需提供覆盖。我们利用无人飞行器（UAV）和Q学习来自动检测给定区域中的覆盖孔，然后考虑与核心网络和用户需求的无线回程，从而部署基于UAV的基站（UAV-BS）。这种机器学习机制将帮助无人机消除人在环（HiTL）模型。稍后，我们针对3D UAV-BS在各种角度位置的放置制定了一个优化问题，以最大化与UAV-BS相关联的用户数量。总而言之，我们在本文中说明了一种经济高效且节省时间的方法，可以检测覆盖孔并按需提供覆盖。我们利用无人飞行器（UAV）和Q学习来自动检测给定区域中的覆盖漏洞，然后考虑与核心网络和用户需求的无线回程，从而部署基于UAV的基站（UAV-BS）。这种机器学习机制将帮助无人机消除人在环（HiTL）模型。稍后，我们针对3D UAV-BS在各种角度位置的放置制定了一个优化问题，以最大化与UAV-BS相关联的用户数量。总而言之，我们在本文中说明了一种经济高效且节省时间的方法，可以检测覆盖孔并按需提供覆盖。