QoS-Aware Reinforcement Learning Based Green Trajectory Design for UAV-Aided Backscattering

Backscatter communication (BackCom) has been gaining a lot of interest as a low-energy consumption energy harvesting solution. Here, the limited transmission range of BackCom systems constraint can now be resolved by using mobile data collectors or readers such as unmanned aerial vehicles (UAVs). In this study, we investigate a monostatic UAV-assisted BackCom system where backscatter devices (BDs) are served in a time-division multiple access (TDMA) fashion. Here we solve an energy efficiency (EE) maximization problem by jointly optimizing the transmit power allocation and the UAV's trajectory while adhering to the quality of service constraints. Since the problem is non-convex and combinatorial in nature, we employ a reinforcement learning framework that utilizes a finite-state Markov decision process. We introduce a low-complexity Ex-pected State-Action-Reward-State-Action (ESARSA) algorithm to determine the UAV's optimal trajectory with power allocation. A closed-form solution is proposed for global power optimization. In simulations, we compare the implemented ESARSA against the State-Action-Reward-State-Action (SARSA) and Q-learning algorithms and show that the proposed ESARSA algorithm can provide a 24% gain over the fixed allocation benchmark.