Deep reinforcement learning to enhance the energy-efficient performance of UAV-enabled F-RAN - Haibo MEI; Kun YANG; Xinyu FAN (物联网学报)
Fog radio access network (F-RAN) is suitable for Internet of things applications of national important industries, such as pipeline network monitoring in wide area.However, the performance of the F-RAN based on the territorial fog access point will be affected greatly by the complicated territorial environment.This causes F-RAN not able to provide fog access service in a timely and effectively manner.To this problem, the research was proposed to utilize low altitude UAV as the fog access point to realize air ground edge communication and fog computing, which has attracted enormous research interests.How to use deep reinforcement learning (DRL) to improve the energy efficiency of UAV fog access point and extend the mission time of UAV were discussed.Deep reinforcement learning can ensure the UAV fog access point to adjust the configuration strategy timely of air ground communication and computing, including resource optimization, dynamic task offloading and caching.DRL can also optimize the UAV trajectory in 3-D space, and improve the overall performance of UAV enabled fog access network.The innovation of the research lies in the comprehensive discussion of the main optimization problems to be solved in the UAV-enabled F-RAN using DRL.The technical details were also summarized to solve the related optimization problems.Finally, the technical challenges and future research directions of the application of DRL in the UAV-enabled F-RAN were discussed.