UAV-Net+: Effective and Energy-Efficient UAV Network Deployment for Extending Cell Tower Coverage With Dynamic Demands

Sun, Renxin; Zhang, Dong; Ding, Lige; Zhang, Jing; Ma, Huadóng

doi:10.1109/tvt.2022.3202141

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…They used Particle Swarm Optimization (PSO) metaheuristic algorithm to find the minimum number of WiFi UAVs, their type, and their location, for providing VoWiFi service to a set of ground users, constrained to coverage and minimum voice speech quality. The authors in [34] proposed a DRL chunk selection algorithm and an energy aware DRL chunk search algorithm for selecting the optimal subset of region chunks as a search space for WiFi UAVs, and planning the optimal paths for UAVs to provide network services, and charge timely under energy constraints to maximize the total service time.…”

Section: ) Uavs Exploiting the Unlicensed Bandmentioning

confidence: 99%

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Anany,

Elmesalawy,

Ibrahim

et al. 2024

IEEE Access

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Recently, the huge growth in multimedia demand derived from smart devices and Internet of Things (IoT) development requires efficient heterogeneous networks with high data rates. To fulfill these requirements, the exploitation of Unmanned Aerial Vehicles (UAVs) enabled with multiple radio access for providing efficient connectivity should be considered, since it offers flexibility, ease of deployment, and ondemand service for wireless devices. In this paper, the idea of deploying Multiple Radio Access Technologies (Multi-RAT) base stations on a UAV is proposed, in order to utilize the unlicensed spectrum. The problem of optimizing the Multi-RAT UAV's location along with the wireless devices' association to maximize the total system throughput considering a heterogeneous LTE and WLAN ground network is investigated. To solve this problem, we propose a novel framework based on reinforcement learning and regret matching algorithms, such that the Q-learning algorithm is used to find the Multi-RAT UAV's optimum location, while regret matching is used to solve the optimum users' association. Moreover, a K-means clustering algorithm is adopted as an initialization phase to speed up the convergence of the proposed solution. Simulation results show the significance of the proposed idea of deploying Multi-RAT base stations on a UAV. Further, the performance analysis shows the effectiveness of the proposed framework by applying K-means as an initialization phase.

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Section: ) Uavs Exploiting the Unlicensed Bandmentioning

confidence: 99%

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Anany,

Elmesalawy,

Ibrahim

et al. 2024

IEEE Access

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“…The application of UAV base stations in disaster relief scenarios improves the problem of communication signals being difficult to reach in complex environments, and also makes it possible to provide all-round coverage of communication signals in disaster relief mission areas. Considering the special nature of UAV base stations and the complexity of the disaster relief environment [ 4 , 5 ], how to use the high flexibility of UAVs to plan an optimal collision-free path in the complex situation with obstacles after a disaster is the most pending problem of UAVs in disaster rescue missions.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

Zhao¹,

Gan²,

Liang³

et al. 2022

Entropy

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Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

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Energy efficient deployment of aerial base stations for mobile users in multi-hop UAV networks

Ryu,

Kim

2024

Ad Hoc Networks

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UAV-Net+: Effective and Energy-Efficient UAV Network Deployment for Extending Cell Tower Coverage With Dynamic Demands

Cited by 10 publications

References 27 publications

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

Energy efficient deployment of aerial base stations for mobile users in multi-hop UAV networks

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