A New Frequency Hopping Strategy Based on Federated Reinforcement Learning for FANET
- Resource Type
- Conference
- Authors
- Ye, Yuanfan; Lei, Ming; Zhao, Minjian
- Source
- 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) Vehicular Technology Conference (VTC2021-Fall), 2021 IEEE 94th. :1-5 Sep, 2021
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Vehicular and wireless technologies
Simulation
Conferences
Reinforcement learning
Autonomous aerial vehicles
Collaborative work
Ad hoc networks
Flying ad-hoc network
federated learning
deep Q-network
frequency hopping
anti-jamming
- Language
- ISSN
- 2577-2465
The flying ad-hoc network (FANET) is widely applied to unmanned aerial vehicles (UAV s) but it is vulnerable to the frequency jamming in reality. Therefore, this paper proposes a federated deep Q-network (DQN) based frequency hopping strategy to solve the problem of periodic frequency jamming. We developed a DQN mechanism with an exploration-exploitation epsilon-greedy policy, directed by a federated learning mechanism to obtain a frequency hopping strategy. The simulation results show that our proposed algorithm has better convergence and decision accuracy performance compared with the DQN based frequency hopping strategy. And the performance will improve when the number of UAVs increases.