Deep reinforcement learning empowered multiple UAVs-assisted caching and offloading optimization in D2D wireless networks

Abstract

Device-to-device (D2D) content caching is a promising technology to mitigate the backhaul pressure, and reduce the contents transmission delay. In this paper, to improve the content hit rate (CHR) and the utilization efficiency of the limited caching capacity, we put forward a caching content placement strategy by predicting the user preference and the content popularity, where unmanned aerial vehicles (UAVs) are introduced into the D2D networks to provide computation offloading services to the users. A dynamic resource allocation optimization algorithm (DRAOA) is proposed to deploy UAVs and plan UAVs trajectory adaptively according to the users' task requirements. Simulation results show that the proposed caching content placement policy outperforms the existing baselines. Additionally, the DRAOA can effectively improve the network capacity and mitigate the computation delay compared to the other two DRL algorithms.