User-Centric UAV Deployment and Content Placement in Cache-Enabled Multi-UAV Networks

Luo, Jijun; Song, Jialun; Zheng, Fu-Chun; Gao, Lin; Wang, Tong

doi:10.1109/tvt.2022.3152246

Cited by 42 publications

(14 citation statements)

References 16 publications

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“…There are also some studies on joint considerations for security and cache placement while optimizing the mobility model of aerial nodes [126], [127]. In most of the recent works [128], [129], the formulated optimization problems are NP-hardness. To tackle these problems efficiently, authors decomposed the optimization problem into some sub-problems, including UAV deployment or trajectory problem and content placement problem.…”

Section: Unified Framework: Optimization Problem Analysismentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh

Khoshkholgh²,

Yanıkömeroğlu

et al. 2023

IEEE Open J. Commun. Soc.

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Section: Unified Framework: Optimization Problem Analysismentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh

Khoshkholgh²,

Yanıkömeroğlu

et al. 2023

IEEE Open J. Commun. Soc.

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“…In addition, UAV deployment and user association are often jointly optimized to improve system performance. Researchers in [ 10 , 11 ] utilized UAVs as aerial base stations to deliver content to users. They employed the K-means algorithm to cluster users, and positioned UAVs at the centroid of each cluster while serving the corresponding user group.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Most previous studies [ 5 , 9 , 10 , 11 ] primarily focused on UAV-enabled wireless communication networks, overlooking the computing requirements of users. Furthermore, few previous studies [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ] considered heterogeneous UAV-enabled networks, i.e., deploying UAVs with different communication and computing capabilities. Motivated by these issues, in this paper, we study the UAV deployment and user association problem to save the energy consumption of UAVs in a more extensive heterogeneous multi-UAV-enabled MEC networks.…”

Section: Introductionmentioning

confidence: 99%

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Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks

Han,

Zhou,

et al. 2023

Entropy

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Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

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“…Integrated access and backhaul in 5G presents a potential solution that can utilize UAVs to augment coverage for short durations in critical areas [6]. However, the demand for enhanced coverage can unpredictably arise at various locations and times, making conventional fixed placement strategies impractical and ineffective [7]. Furthermore, an essential consideration in UAV deployment is the balance between maintaining robust backhaul connectivity, facilitating seamless data transfer between UAVs and the central infrastructure, and optimizing fronthaul communication with ground users.…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

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User-Centric UAV Deployment and Content Placement in Cache-Enabled Multi-UAV Networks

Cited by 42 publications

References 16 publications

Self-Evolving Integrated Vertical Heterogeneous Networks

Self-Evolving Integrated Vertical Heterogeneous Networks

Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

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