HGNN-Based Energy-Efficient Beamforming and Power Control for Cellular-Connected UAV Communications with Finite Resolution ADC/DACs

In this paper, we investigate energy-efficient beamforming and power control for network-assisted full-duplexenabled cellular-connected unmanned aerial vehicle (UAV) communications supporting heterogeneous uplink/downlink (UL/DL) services; E-UAVs upload high-rate payloads in the UL, while UUAVs receive low-latency control signals in the DL. We consider a lightweight UAV antenna architecture with a small number of phase shifters (PSs) for antenna on/off control and low-resolution analog-to-digital/digital-to-analog converters (ADCs/DACs). Then, we formulate an EE maximization problem by jointly optimizing PS deactivation and the E-UAV transmit power, subject to heterogeneous UL/DL QoS requirements. To reduce channelestimation complexity and signaling overhead (SO), we propose a distributed heterogeneous graph neural network. Simulation results demonstrate that the proposed scheme achieves higher EE with lower SO and complexity than existing schemes.