Unmanned Aerial Vehicles with Lens Antenna Subarray

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, their strict energy constraint for simultaneously flying and communication tasks renders the exploration of energy-efficient multi-antenna techniques indispensable. Meanwhile, lens antenna subarrays (LASs) emerge as a promising energy-efficient multi-antenna structure that have not been previously harnessed for this purpose. In this paper, we propose a LAS-aided UAV to serve ground users in downlink transmission. We formulate a resource allocation problem aimed at initiating a trade-off between aggregate data rate of ground users and the power consumption of the UAV (energy efficiency) by optimizing the lens-based beamforming and flight trajectory of the UAV. To address this non-convex problem, we recast it in Markov decision process that captures its dynamic features and provides a framework to train an actor-critic agent. This agent is fine-tuned via hindsight experience replay for enhanced stabilization. As well, given the frequent mobility of the UAV, we fortify the trained agent with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20% energy efficiency gain is achieved by incorporating a 4lens LAS for UAV, compared to its single-lens architecture in literature. Simulations also demonstrate that the proposed resource allocation strategy achieves significant superiority over counterparts in literature.