Optimal Deployment of Tethered Drones for Maximum Cellular Coverage in User Clusters

Bushnaq, Osama M.; Kishk, Mustafa A.; Çelik, Abdulkadir; Alouini, Mohamed‐Slim; Al-Naffouri, Tareq Y.

doi:10.1109/twc.2020.3039013

Cited by 64 publications

(18 citation statements)

References 39 publications

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“…Tethered UAVs have been considered for various wireless network applications in the literature. The work in [53] presents a comparative performance analysis of untethered-UAV (U-UAV) and T-UAV-assisted cellular traffic offloading in a traffic-heavy location. They employed stochastic geometry methods to calculate joint distance distributions between users, the terrestrial BS (TBS) and UAV.…”

Section: Grid Powered Uavs: Tetheringmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Grid Powered Uavs: Tetheringmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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show abstract

“…Thus, the research fraternity has focused on investigating optimization strategies to reduce flight trajectories [9,10]. One approach that is being used to recharge is tethered UAVs [11]. In this technique, UAVs are linked to a base station through a cable to receive power supply continuously.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Review of Micro UAV Charging Techniques

et al. 2022

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The groundbreaking Unmanned Aerial Vehicles (UAVs) technology has gained significant attention from both academia and industrial experts due to several applications, such as military missions, power lines inspection, precision agriculture, remote sensing, delivery services, traffic monitoring and many more. UAVs are expected to become a mainstream delivery element by 2040 to address the ever-increasing demand for delivery services. Similarly, UAV-assisted monitoring approaches will automate the inspection process, lowering mission costs, increasing access to remote locations and saving time and energy. Despite the fact that unmanned aerial vehicles (UAVs) are gaining popularity in both military and civilian applications, they have a number of limitations and critical problems that must be addressed in order for missions to be effective. One of the most difficult and time-consuming tasks is charging UAVs. UAVs’ mission length and travel distance are constrained by their low battery endurance. There is a need to study multi-UAV charging systems to overcome battery capacity limitations, allowing UAVs to be used for a variety of services while saving time and human resources. Wired and Wireless Power Transfer (WPT) systems have emerged as viable options to successfully solve this difficulty. In the past, several research surveys have focused on crucial aspects of wireless UAV charging. In this review, we have also examined the most emerging charging techniques for UAVs such as laser power transfer (LPT), distributed laser charging (DLC), simultaneous wireless information and power transfer (SWIPT) and simultaneous light wave information and power transfer (SLIPT). The classification and types of UAVs, as well as various battery charging methods, are all discussed in this paper. We’ve also addressed a number of difficulties and solutions for safe operation. In the final section, we have briefly discussed future research directions.

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“…The offloading of MD data enables the use of UAV-MECs to minimize traffic on Macro-Cell Base stations (MBSs). Such services can (1) reduce the transmission latency of MD content requests [90], (2) save the battery of the MD [91], (3) Reduce the redundancy of common content transmissions over backhaul links that provide greater protection [92], (4) reduce transmission costs [93], (5) enhance performance [94] [95], (6) achieve greater energy efficiency for MDs, UAVs, and MNO, (7) greatly enhance the ability of the network to support time-sensitive applications [96] [97] as well as (8) improve coverage [98] [99], see fig. 1.…”

Section: Computation Offloading Utility In Uav-mecmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Optimal Deployment of Tethered Drones for Maximum Cellular Coverage in User Clusters

Cited by 64 publications

References 39 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Comprehensive Review of Micro UAV Charging Techniques

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

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