Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Energy Efficient Multi-UAV Communication Using DDPG
- Authors
- Do, Q.T.; Hua, D.T.; Tran, A.T.; Cho, Sungrae
- Issue Date
- Oct-2022
- Publisher
- IEEE Computer Society
- Keywords
- energy efficiency; multi-agent reinforcement learning; UAV communication
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 1071 - 1075
- Pages
- 5
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 1071
- End Page
- 1075
- ISSN
- 2162-1233
- Abstract
- Recently, unmanned aerial vehicles (UAVs) have been widely used in wireless communication. They can serve as aerial base stations for ground users (GUs). However, they cannot work for long because of the limited energy pool, and thus can only cover a limited area of services. In this paper, we investigate a wireless communication system enabled by multiple unmanned aerial vehicles (UAVs) for downlink communication, where energy consumption is taken into consideration. Our goal is to maximize the UAV's service time and downlink throughput by using a deep-reinforcement learning method called deep-deterministic policy gradient (DDPG). Simulation results are illustrated to demonstrate the performance of the proposed method. © 2022 IEEE.
- Files in This Item
- There are no files associated with this item.
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.