Integration of Software Defined Radios and Software Defined Networking Towards Reinforcement Learning Enabled Unmanned Aerial Vehicle Networks

Advances in unmanned aerial vehicles (UAVs) enablethe development of many fields. The UAV networking capabilityis vital to the performance of the UAV fleet. However, thefluid characteristics make the UAV networking hard to meetthe requirement of quick response when some UAVs are out ofpower. In this paper, we propose an approach that integrates thesoftware defined radios (SDR) and software defined networking(SDN) to realize the update of UAV networking. We leverage theSDR to update link layer and SDN to manage the networkinglayer, which is driven by reinforcement learning. In our eval-uation, the results show that reinforcement learning can meetthe requirement of network update when some UAVs are out ofpower.