Coordinated Learning for Handover Management in 6G Networks With Transparent UAV Relays

We focus on handover management in networks integrating traditional terrestrial ground base stations (GBSs) and non-terrestrial unmanned aerial vehicles (UAVs) serving users. In such scenario, we propose a joint management of handover of users equipment (UEs) between UAVs and GBSs as well as handover of UAVs between GBSs. Our goal is to maximize the sum capacity for UEs while avoiding redundant handovers. As an impact of handover on the future network performance is not explicit, we adopt deep deterministic policy gradient (DDPG). Furthermore, since the UAVs are usually energy constrained, we consider an energy efficient transparent relaying mode for the UAVs. However, in the transparent relaying mode, the access channel quality between the UAV relay and the UE is unknown even if such information is essential for handover. Thus, we further employ deep neural network (DNN) to predict the access channel quality. An incorporation of DDPG for handover management together with DNN for channel quality prediction can impair the sum capacity of the UEs due to an accumulation of inherent small prediction errors of DNN and DDPG. Hence, we also introduce a coordination between DNN and DDPG to suppress the accumulation of the prediction errors. Simulations demonstrate that the proposed handover management and the coordination of DDPG and DNN increase the sum capacity by up to 63% while notably reducing the number of handovers and handover failure ratio compared to the state-of-the-art works.