Reinforcement-Learning-Based Optimization on Energy Efficiency in UAV Networks for IoT

Abstract

The combination of nonorthogonal multiplex access and unmanned aerial vehicles (UAVs) can improve the energy efficiency (EE) for Internet of Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. First, with a given UAV trajectory, the Dinkelbach method-based fractional programming is adopted to obtain the optimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithm is adopted in the second stage to update the system parameters. Finally, reinforcement-learning-based optimization is introduced to obtain the best UAV trajectory.