New Search

Export article

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

Na Lin, Hongzhi Qin, Junling Shi, Liang Zhao

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.
Keywords: deep reinforcement learning / D2D caching networks / task offloading / unmanned aerial vehicles (UAVs)

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

Share this article

References (23)
    Back to Top Top