Deep Reinforcement Learning for Channel and Power Allocation in UAV-enabled IoT Systems

Abstract

Unmanned aerial vehicles (UAVs) have recently been proposed as moving base stations to collect data from ground IoT nodes in remote areas. Since IoT nodes are normally battery-limited, energy efficiency is an important metric in IoT systems. In order to improve energy efficiency in UAV-enabled IoT systems, it is necessary to allocate both channels and transmit power properly for IoT nodes. Motivated by the superior performance of deep reinforcement learning (DRL) in decision-making tasks, we propose a DRL-based channel and power allocation framework in a UAV-enabled IoT system. With the proposed framework, the UAV-BS is able to intelligently allocate both channels and transmit power for uplink transmissions of IoT nodes to maximize the minimum energy-efficiency among all the IoT nodes. Simulation results validate the effectiveness of the proposed algorithm and show its superiority over the- state-of-the-arts.