Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach

Recently, backscatter communication (BC) has been introduced as a green paradigm for Internet of Things (IoT). Meanwhile, unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to enhance the performance of BC system thanks to their high mobility and flexibility. In this paper, we investigate the problem of energy efficiency (EE) for an energy-limited backscatter communication (BC) network, where backscatter devices (BDs) on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor. Specifically, we first reformulate the EE optimization problem as a Markov decision process (MDP) and then propose a deep reinforcement learning (DRL) algorithm to design the UAV trajectory with the constraints of the BD scheduling, the power reflection coefficients, the transmission power, and the fairness among BDs. Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.