Three-dimensional aerial base station location for sudden traffic with deep reinforcement learning in 5G mmWave networks

Abstract

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.