Energy efficiency analysis of Drone Small Cells positioning based on reinforcement learning

Abstract

This work proposes an algorithm to optimize the positioning and the transmit power of Drone Small Cells (DSCs) based on Q-learning, a technique where the agents learn to maximize a given reward. We consider two different rewards in this work, the first focusing on coverage, while the second maximizes the lifetime. Then, the Q-learning solution determines the best positioning of the DSC in the 3D space, as well as the optimal transmit power. Results show that the optimization of the transmit power is of paramount importance to reduce the outage probability. In addition, we show that the second reward can considerably increase the network lifetime with a small penalty to the coverage.