Reinforcement Learning Based Anti-Jamming FANET Routing with QoS Guarantee

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop, but the quality of service (QoS) and energy efficiency have to be enhanced against jamming due to the inaccurate path quality estimation. In this paper, we propose an RL based anti-jamming FANET routing with QoS guarantee to optimize the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions, the received jamming power and the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination. The performance bound is derived in terms of network topology and channel gain based on the Nash equilibrium of the cooperative game among the UAVs. Simulation results provide the performance gain of the throughput and energy consumption over the benchmarks.