Abstract
Recently, unmanned aerial vehicles (UAVs) as flying wireless communication platform have attracted much attention. Benefiting from the mobility, UAV aerial base stations can be deployed quickly and flexibly, and can effectively establish Line-of-Sight communication links. However, there are many challenges in UAV communication system. The first challenge is energy constraint, where the UAV battery lifetime is in the order of fraction of an hour. The second challenge is that the coverage area of UAV aerial base station is limited and the commercial UAV is usually expensive. Thus, covering a large target region all the time with sufficient UAVs is quite challenging. To solve above challenges, in this paper, we propose energy efficient and fair 3-D UAV scheduling with energy replenishment, where UAVs move around to serve users and recharge timely to replenish energy. Inspired by the success of deep reinforcement learning, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each user in a large region and maintains the persistent service. Simulation results reveal that UC-DDPG shows a good convergence and outperforms other scheduling algorithms in terms of data volume, energy efficiency and fairness.
Highlights
Unmanned aerial vehicle (UAV) as flying wireless communication platform is a promising technology to enhance the wireless network with its inherent attributes such as mobility, flexibility and adaptive altitude [1]
Different from the aforementioned existing works under the assumption of either 2-D or stationary UAV coverage, inspired by the success of deep reinforcement learning (DRL), we propose a UAV Control policy based on Deep Deterministic Policy Gradient (DDPG) algorithm [17] (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each ground user in a large target region, while maintaining the persistent service
In order to improve energy efficiency and guarantee service fairness, we develop a 3-D UAV deployment scheduling algorithm based on DDPG algorithm, which takes the residual energy of UAV, circuit power, communication power, mobility power and hover power into account
Summary
Unmanned aerial vehicle (UAV) as flying wireless communication platform is a promising technology to enhance the wireless network with its inherent attributes such as mobility, flexibility and adaptive altitude [1]. Different from the aforementioned existing works under the assumption of either 2-D or stationary UAV coverage, inspired by the success of DRL, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (DDPG) algorithm [17] (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each ground user in a large target region, while maintaining the persistent service. The works in [10] proposed a framework to achieve energy-efficient uplink data collection from ground IoT devices by jointly optimizing the 3-D placement, device-UAV association and uplink power control in single time slot.
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