The multi-objective deployment optimization of UAV-mounted cache-enabled base stations

Abstract

The deployment of unmanned aerial vehicle (UAV)-mounted base stations is emerging as an effective solution for providing wireless communication service to ground terminals (GTs) which have failed to be associated with ground base stations for some reason. Meanwhile, with the propose of reducing the transmission latency and easing the load of backhaul links between UAVs and the core network, UAVs are equipped with the ability of caching popular contents in the storage of base stations. In this paper, we investigate the efficient deployment problem of UAVs (such as transmitting power, number of UAVs, locations and caching) while guaranteeing the quality of service requirements. In this case, the UAV plays the role of a coordinator to provide high-quality communication service for GTs as well as maximize the benefit of caching. However, there exists an intractable issue that UAVs need to consider the optimization problem of multiple performance metrics with various types of optimization variables. To tackle the challenge, we propose a reinforcement learning-based approach to solve the multi-objective deployment problem while maintaining the optimal tradeoff between power consumption and backhaul saving. Numerical results evaluate the performance of the proposed algorithm.