Data volume demand has increased dramatically due to huge user device increasement along with the development of cellular networks. And macrocell in 5G networks may encounter sudden traffic due to dense users caused by sports or celebration activities. To resolve such temporal hotspot, additional network access point has become a new solution for it, and unmanned aerial vehicle equipped with base stations is taken as an effective solution for coverage and capacity improvement. How to plan the best three-dimensional location of the aerial base station according to the users’ business needs and service scenarios is a key issue to be solved. In this article, first, aiming at maximizing the spectral efficiency and considering the effects of line-of-sight and non-line-of-sight path loss for 5G mmWave networks, a mathematical optimization model for the location planning of the aerial base station is proposed. For this model, the model definition and training process of deep Q-learning are constructed, and through the large-scale pre-learning experience of different user layouts in the training process to gain experience, finally improve the timeliness of the training process. Through the simulation results, it points out that the optimization model can achieve more than 90% of the theoretical maximum spectral efficiency with acceptable service quality.

中文翻译：

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5G 毫米波网络中具有深度强化学习的突发流量的三维空中基站定位

由于随着蜂窝网络的发展，用户设备的巨大增加，数据量需求急剧增加。而5G网络中的宏蜂窝可能会因运动或庆典活动导致用户密集而遇到突发流量。针对这样的时间热点，增加网络接入点成为其新的解决方案，搭载基站的无人机被作为覆盖和容量提升的有效解决方案。如何根据用户的业务需求和服务场景，规划出空中基站的最佳三维位置，是一个需要解决的关键问题。在本文中，首先，以最大化频谱效率为目标，并考虑 5G 毫米波网络的视距和非视距路径损耗的影响，提出了一种用于空中基站位置规划的数学优化模型。针对该模型，构建了深度Q-learning的模型定义和训练过程，通过训练过程中不同用户布局的大规模预学习经验获取经验，最终提高训练过程的时效性。通过仿真结果表明，优化模型可以达到理论最大频谱效率的90%以上，服务质量可以接受。最终提高培训过程的及时性。通过仿真结果表明，优化模型可以达到理论最大频谱效率的90%以上，服务质量可以接受。最终提高培训过程的及时性。通过仿真结果表明，优化模型可以达到理论最大频谱效率的90%以上，服务质量可以接受。