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.
Paper
Full text
Deep Reinforcement Learning for Channel and Power Allocation in UAV-enabled IoT Systems
Semantic Scholar · Computer Science · 2019
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.