Trajectory Prediction of UAVs for Relay-assisted D2D Communication Using Machine Learning

Abstract

Device-to-Device (D2D) communication has been proven an efficient technique in the present and upcoming cellular networks for improving network performance. Many a time, a direct D2D link may not be available due to longer distance or poor channel quality between two devices. Multi-hop D2D is an effective solution to overcome this limitation of direct D2D communication. Here relay devices help in forwarding data from transmitters to the receivers through single or multiple hops. However, finding suitable fixed relays and their locations is a complex problem, which does not have an efficient solution. In this paper, we have used UAVs (drones) that act as relays for forwarding data between two devices. The proposed approach serves more out of direct range D2D users resulting in a reduced churn rate of the system. We find the trajectory of such UAVs with the help of active user prediction using Neural Networks (NN) to serve all the D2D users by increasing the coverage range of D2D communications. We have estimated the number of active D2D users in every zone covered by each drone and intra and inter-drone communication trajectories. It is also shown that the packet loss ratio remains within the acceptable limit for the proposed trajectories of the UAVs by choosing a sufficient buffer length.