Multi-agent reinforcement learning based 3D trajectory design in aerial-terrestrial wireless caching networks

Yu Jia Chen, Kai Min Liao, Meng Lin Ku, Fung Po Tso, Guan Yi Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes.

Original languageEnglish
Article number9473012
Pages (from-to)8201-8215
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Unmanned aerial vehicles (UAVs)
  • multi-agent reinforcement learning
  • trajectory design
  • wireless caching

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