Optimal LAP Altitude for Maximum Coverage

Al‐Hourani, Akram; Kandeepan, Sithamparanathan; Lardner, Simon

doi:10.1109/lwc.2014.2342736

Cited by 2,415 publications

(1,966 citation statements)

References 5 publications

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“…Simulation results demonstrated that the impact of elevation angle was significant on the excess path loss. In [10], the closed-form expression was formulated for determining the coverage performance in terms of the maximum cell radius and the optimal altitude. In this study, the free space path loss model was extended using the excessive attenuation factor for different LOS and NLOS propagation conditions.…”

Section: A Deterministicmentioning

confidence: 99%

“…Such models are useful for studying large-scale fading effects in the channel [6], [7]. Hence the propagation conditions [8], [9] can provide coverage analysis and indicate the optimal UAV position [10]- [12]. The second approach is to develop a tapped delay line (TDL) model to characterize the direct path as well as the multipath components.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

680

346

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Unmanned aerial vehicles (UAVs) have gained great interest for rapid deployment in both civil and military applications. UAV communication has its own distinctive channel characteristics compared with widely used cellular and satellite systems. Thus, accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication systems. However, several challenges exist in UAV channel modeling. For example, propagation characteristics of UAV channels are still less explored for spatial and temporal variations in non-stationary channels. Also, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey on the measurement campaigns launched for UAV channel modeling using low altitude platforms and discusses various channel characterization efforts. We also review the contemporary perspective of UAV channel modeling approaches and outline some future research challenges in this domain.

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Section: A Deterministicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

680

346

View full text Add to dashboard Cite

show abstract

“…The propagation channel is modeled by probabilistic LoS, in which the probability of having a LoS connection between a user and the drone is as follows [9] …”

Section: System Modelmentioning

confidence: 99%

Dynamic Base Station Repositioning to Improve Performance of Drone Small Cells

Fotouhi

Ding

Hassan

2016

2016 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Thanks to the recent advancements in drone technology, it has become viable and cost-effective to quickly deploy small cells in areas of urgent needs by using a drone as a cellular base station. In this paper, we explore the benefit of dynamically repositioning the drone base station in the air to reduce the distance between the BS and the mobile user equipment, thereby improving the spectral efficiency of the small cell. In particular, we propose algorithms to autonomously control the repositioning of the drone in response to users activities and movements. We demonstrate that, compared to a drone hovering at a fixed location, dynamic repositioning of the drone moving with high speed can increase spectral efficiency by 15%. However, considering the tradeoff between spectral efficiency and energy efficiency of the drone, we show that 10.5% spectral efficiency gain can be obtained without negatively affecting energy consumption of the drone.

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“…The study does not explore cross-tier interference management in the presence of large scale DSCN deployment. The authors in [8], based on the results of [9], present a 3D optimization problem for DSCs with the aim to maximize the number of users to be covered by such DSCs using a numerical search algorithm to satisfy the defined quality of service (QoS) measures. The paper focuses on drone empowered future cellular networks for disaster recovery/ public safety.…”

Section: B Related Workmentioning

confidence: 99%

Drone Empowered Small Cellular Disaster Recovery Networks for Resilient Smart Cities

Hayajneh¹,

Zaidi

McLernon³

et al. 2016

2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops)

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Abstract-Resilient communication networks, which can continue operations even after a calamity, will be a central feature of future smart cities. Recent proliferation of drones propelled by the availability of cheap commodity hardware presents a new avenue for provisioning such networks. In particular, with the advent of Google's Sky Bender and Facebook's internet drone, drone empowered small cellular networks (DSCNs) are no longer fantasy. DSCNs are attractive solution for public safety networks because of swift deployment capability and intrinsic network reconfigurability. While DSCNs have received some attention in the recent past, the design space of such networks has not been extensively traversed. In particular, co-existence of such networks with an operational ground cellular network in a post-disaster situation has not been investigated. Moreover, design parameters such as optimal altitude and number of drone base stations, etc., as a function of destroyed base stations, propagation conditions, etc., have not been explored. In order to address these design issues, we present a comprehensive statistical framework which is developed from stochastic geometric perspective. We then employ the developed framework to investigate the impact of several parametric variations on the performance of the DSCNs. Without loss of any generality, in this article, the performance metric employed is coverage probability of a down-link mobile user. It is demonstrated that by intelligently selecting the number of drones and their corresponding altitudes, ground users coverage can be significantly enhanced. This is attained without incurring significant performance penalty to the mobile users which continue to be served from operating ground infrastructure.

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Optimal LAP Altitude for Maximum Coverage

Cited by 2,415 publications

References 5 publications

A Survey of Channel Modeling for UAV Communications

A Survey of Channel Modeling for UAV Communications

Dynamic Base Station Repositioning to Improve Performance of Drone Small Cells

Drone Empowered Small Cellular Disaster Recovery Networks for Resilient Smart Cities

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