Reinforcement Learning based Scheduling for Heterogeneous UAV Networking

With the ubiquitous deployment of 5G cellular networking in many fields, unmanned aerial vehicle (UAV) networking, as one of the main parts of the Internet of Things (IoT), is playing a pivot role in the extension of smart cities. Different from the conventional approaches, the 5G enabled UAV networking can be more capable of multiple and complex mission executions with high requirements of collaborations and incorporation. In this paper, we leverage reinforcement learning based scheduling to optimize the throughput of heterogeneous UAV networking. To improve the throughput of the heterogeneous UAV networking, we focus on the balance for the inter-and intra-networking with the reduction of collisions occurring in the time slots. With reinforcement learning enabled scheduling, we can achieve the optimum selections on link activation and time allocation. Compared with the edge coloring of Karloff, our approach can achieve a higher enhancement on the throughput. The experimental results show that our approach reaches the global optimization when t
s
and t
g
are less than 0.01. Generally, DQN achieves 57.58% improvement on average which exceeds Karloff. The proposed approach can improve the throughput of heterogeneous UAV networking significantly.