Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach

Since flying base stations (FlyBSs) are energy constrained, it is convenient for them to act as transparent relays with minimal communication control and management functionalities. The challenge when using the transparent relays is the inability to measure the relaying channel quality between the relay and user equipment (UE). This channel quality information is required for communication-related functions, such as the UE association, however, this information is not available to the network. In this letter, we show that it is possible to determine the UEs' association based only on the information commonly available to the network, i.e., the quality of the cellular channels between conventional static base stations (SBSs) and the UEs. Our proposed association scheme is implemented through deep neural networks, which capitalize on the mutual relation between the unknown relaying channel from any UE to the FlyBS and the known cellular channels from this UE to multiple surrounding SBSs. We demonstrate that our proposed framework yields a sum capacity that is close to the capacity reached by solving the association via exhaustive search.