RE-ORA: Residual Energy-Aware Online Random Access for Improving the Lifetime of Slotted ALOHA-Based Swarming Drone Networks

Lim, Suhyeon; Chae, Seong Ho; Lee, Howon

doi:10.1109/access.2021.3066979

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…θ ij is the elevation angle between GCS i and UT j, and can be calculated as θ ij = arcsin h j d ij , where h j denotes the altitude of UT j. From Equation (6), the NLoS probability between GCS i and UT j can be obtained as P NLoS ij = 1−P LoS ij . Using Equations ( 1)-( 6), the average path loss of the A2G link between GCS i and UT j considering the LoS and NLoS probabilities can be described as…”

Section: A Air-to-ground (A2g) Channel Modelmentioning

confidence: 99%

“…In particular, the limited battery lifetime of UAVs shortens the time UAVs can operate [3], [4]. Considering this battery problem, many studies have been conducted to improve UAV's energy efficiency (EE) [5], [6] [7]. Specifically, in [5], the authors proposed a multi-agent reinforcement learning…”

Section: Introductionmentioning

confidence: 99%

“…(RL)-based UAV deployment and power control algorithm for maximizing EE in multi-UAV wireless networks. Additionally, the authors of [6] proposed an online random access protocol by adjusting the packet transmission opportunities based on the residual energy of drones in S-ALOHA-based swarming drone networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Frequency Reuse and Power Control in Multi-UAV Wireless Networks: Hierarchical Multi-Agent Reinforcement Learning Perspective

Lee

Lim

Chae³

et al. 2022

IEEE Access

Self Cite

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To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.INDEX TERMS Unmanned aerial vehicle, optimal frequency reuse, transmit power control, energy efficiency, hierarchical multi-agent Q-learning, multi-UAV wireless network.

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Section: A Air-to-ground (A2g) Channel Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Frequency Reuse and Power Control in Multi-UAV Wireless Networks: Hierarchical Multi-Agent Reinforcement Learning Perspective

Lee

Lim

Chae³

et al. 2022

IEEE Access

Self Cite

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show abstract

“…In recent years, drone technologies have attracted much attention [1][2][3] for object tracking [4], the transportation of goods [5,6], traffic surveillance [7], and remote sensing [8]. Moreover, drone technologies are expected to be useful in responding to natural disasters [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Deployment Method with Connectivity for Drone Communication Networks

Osumi

Kimura

Hirata

et al. 2023

Drones

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In this paper, we consider a drone deployment problem in situations where the number of drones to be deployed is small compared to the number of users on the ground. In this problem, drones are deployed in the air to collect information, but they cannot collect information from all ground users at once due to the limitations of their communication range. Therefore, the drones need to continue to move until they collect the information for the all ground users. To efficiently realize such drone deployment, we propose two deployment methods. One is an integer linear programming (ILP)-based deployment method and the other is an adjacent deployment method. In the ILP-based deployment method, the positions of the drones at each point in time are determined by solving an ILP problem in which the objective function is the total number of users from whom data can be collected. In contrast, in the adjacent deployment method, drones are sequentially deployed in areas with probabilities determined according to the number of user nodes in adjacent areas at which other drones are already deployed. Through numerical experiments, we show that these deployment methods can be used to efficiently collect data from user nodes on the ground.

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Performance Analysis of Cooperative Dynamic Framed Slotted-ALOHA With Random Transmit Power Control in A2G Communication Networks

Lee

Chae³

et al. 2022

IEEE Access

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RE-ORA: Residual Energy-Aware Online Random Access for Improving the Lifetime of Slotted ALOHA-Based Swarming Drone Networks

Cited by 5 publications

References 16 publications

Optimal Frequency Reuse and Power Control in Multi-UAV Wireless Networks: Hierarchical Multi-Agent Reinforcement Learning Perspective

Optimal Frequency Reuse and Power Control in Multi-UAV Wireless Networks: Hierarchical Multi-Agent Reinforcement Learning Perspective

Deployment Method with Connectivity for Drone Communication Networks

Performance Analysis of Cooperative Dynamic Framed Slotted-ALOHA With Random Transmit Power Control in A2G Communication Networks

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