Energy-Efficient Management of Unmanned Aerial Vehicles for Underlay Cognitive Radio Systems

Ghazzai, Hakim; Ghorbel, Mahdi Ben; Kadri, Abdullah; Hossain, Md. Jahangir; Menouar, Hamid

doi:10.1109/tgcn.2017.2750721

Cited by 101 publications

(43 citation statements)

References 20 publications

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“…For the purpose of UAV-SC placement, the dominant factors of the power consumption consist of the hovering and data transmission, following [17]. The static power consumption relating to the hovering of the UAV is defined as…”

Section: Uav Power Consumption Modelmentioning

confidence: 99%

Optimization of Unmanned Aerial Vehicle Augmented Ultra-Dense Networks

Zamani

Kämmer

et al. 2020

Preprint

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In this paper, we study the integration of unmanned aerial vehicle small cells (UAV-SC) for the purpose of augmenting or temporarily restoring service to an ultra-dense cellular network. The aim is to minimize the overall power consumption of the network by jointly optimizing the number of UAV-SCs, their placement, associations and the power allocation, subject to user QoS, transmit power and fronthaul capacity constraints. As the resulting optimization problem is non-convex and computationally inefficient to solve, we investigate lower complexity alternatives. By reformulating the original problem, a linear structure can be obtained that is efficiently solved by off-the-shelf solvers. Furthermore, we also propose a meta-heuristic method that is based on particle swarm optimization. The performance of the proposed methods is evaluated via simulation studies and compared to state-of-the-art techniques. The results illustrate that the proposed methods consistently outperform conventional techniques by deploying fewer UAV-SCs and also lowering the transmit powers. Furthermore, considerable power savings were observed particularly for low QoS demands and dense scenarios.

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Section: Uav Power Consumption Modelmentioning

confidence: 99%

Optimization of Unmanned Aerial Vehicle Augmented Ultra-Dense Networks

Zamani

Kämmer

et al. 2020

Preprint

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“…According to (17), the PR outage constraint (p out = α) can be satisfied by adjusting the detection probability P D and the UAV's transmit power ρ u . If ρ u is increased to enhance the SE, the probability p UP will be higher, then P D should be larger to make sure that the outage probability is equal to α; If the detection capability of the CR module is limited, i.e., P D is small, in this case, the UAV should control the transmit power ρ u to satisfy the outage constraint.…”

Section: Hybrid Modementioning

confidence: 99%

“…The three-dimensional location of the UAV and resource allocation are optimized to minimize the energy consumption of the UAV while the data rate of primary transmission is guaranteed. In [17], the energy efficient management of UAV for underlay CR system is explored where the power allocation scheme is combined with 3D positioning optimization. The idea of the above two papers is to exploit the mobility of UAV to obtain additional DoF compared with ground nodes.…”

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confidence: 99%

SE and EE Optimization for Cognitive UAV Network Based on Location Information

Huang

et al. 2019

IEEE Access

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Unmanned Aerial Vehicle (UAV) aided communication has the potential to provide on-demand wireless services and improve the outdoor link throughput. Applications for UAVs are rapidly growing with the development of Internet of Things. Because of limited battery energy, the UAVs need time-limited spectrum access to complete data transmission. Hence there are two challenges for the UAV-based communication: 1) Spectrum-efficient design; 2) Energy-efficient design. In this paper, we investigate the optimization of spectrum efficiency (SE) and energy efficiency (EE) for cognitive UAV network based on location information. Because of high mobility, the cognitive radio (CR) based UAVs operate on different frequency bands that vary with time and space. Thus, one spectrum band that is available in one region may not be necessarily available in another region. Based on location information of the primary transmitter and the UAV, we propose a hybrid mode in which the sensing performance and UAV's transmit power can be adjusted simultaneously to satisfy the outage constraint of the primary user. The multi-objective optimization theory is used to solve the tradeoff between SE and EE. The UAV's transmit power, sensing time and sensing threshold are optimized jointly to solve the tradeoff problem. To further improve the SE and EE performance, we propose a multi-frame combined sensing scheme, in which multiple frames are bundled together. Simulation results are provided to show the SE-EE tradeoff design, to validate the effectiveness of the proposed hybrid mode, and to show the advantages of the multi-frame combined sensing scheme in EE performance. INDEX TERMS UAV, spectrum efficiency, energy efficiency, cognitive radio, multi-frame combined sensing. I. INTRODUCTION Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their wide use in many applications. Examples of the applications include: military operations, fire control, tracking and surveillance, wilderness search and many others [1]. The UAVs are equipped with cameras, sensors and communication equipment, which can be used for reconnoitering and monitoring purposes. With the advantages of high maneuverability and flexibility, the UAVs are widely The associate editor coordinating the review of this manuscript and approving it for publication was Shihao Yan. used to assist the wireless devices on the ground [2]. UAV plays an important role in high-speed 5G wireless network. The network connection can be expanded from ground to air and various services can be provided for hot spots in different locations. Compared with terrestrial communication, UAV-assisted communication has the advantages of rapid deployment, high mobility and low operating costs [3]. Unlike traditional cellular networks, the UAVs can be used as mobile base stations such that the wireless services can be provided with no infrastructure constraint [4]. The UAV-assisted wireless network has attracted much attention by the academia and industry since it can alleviate urgent

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“…Various applications emerged with the introduction of Flying Ad Hoc Networks (FANETs), including shipment of goods, home package delivery, crop monitoring, agricultural surveillance, and rescue operations [1]. Unlike traditional Mobile Ad Hoc Networks, FANET applications are generally unicastbased mainly due to energy restrictions [2].…”

Section: Introductionmentioning

confidence: 99%

UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication

Barka

Kerrache

Lagraa

et al. 2018

Journal of Advanced Transportation

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Ensuring the desired level of security is an important issue in all communicating systems, and it becomes more challenging in wireless environments. Flying Ad Hoc Networks (FANETs) are an emerging type of mobile network that is built using energy-restricted devices. Hence, the communications interface used and that computation complexity are additional factors to consider when designing secure protocols for these networks. In the literature, various solutions have been proposed to ensure secure and reliable internode communications, and these FANET nodes are known as Unmanned Aerial Vehicles (UAVs). In general, these UAVs are often detected as malicious due to an unintentional misbehavior related to the physical features of the UAVs, the communication mediums, or the network interface. In this paper, we propose a new context-aware trust-based solution to distinguish between intentional and unintentional UAV misbehavior. The main goal is to minimize the generated error ratio while meeting the desired security levels. Our proposal simultaneously establishes the inter-UAV trust and estimates the current context in terms of UAV energy, mobility pattern, and enqueued packets, in order to ensure full context awareness in the overall honesty evaluation. In addition, based on computed trust and context metrics, we also propose a new inter-UAV packet delivery strategy. Simulations conducted using NS2.35 evidence the efficiency of our proposal, called UNION, at ensuring high detection ratios > 87% and high accuracy with reduced end-to-end delay, clearly outperforming previous proposals known as RPM, T-CLAIDS, and CATrust.

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Energy-Efficient Management of Unmanned Aerial Vehicles for Underlay Cognitive Radio Systems

Cited by 101 publications

References 20 publications

Optimization of Unmanned Aerial Vehicle Augmented Ultra-Dense Networks

Optimization of Unmanned Aerial Vehicle Augmented Ultra-Dense Networks

SE and EE Optimization for Cognitive UAV Network Based on Location Information

UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication

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