Modeling and Analysis of Aerial Base Station-Assisted Cellular Networks in Finite Areas Under LoS and NLoS Propagation

Wang, Xianling; Zhang, Haijun; Tian, Yue; Leung, Victor C. M.

doi:10.1109/twc.2018.2865344

Cited by 69 publications

(61 citation statements)

References 34 publications

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“…The first, considers a typical setup where the reference receiver is located randomly in a compact C ⊂ R 2 , while BSs are uniformly randomly distributed in a disc [19] or a L-sided polygon [62] centered at the reference receiver. The second, extends the first model by considering an arbitrarily-located reference receiver in the disc [195], [234] or L-sided polygon of BSs [63]. The third setup considers an arbitrarily-located reference receiver in an arbitrarily-shaped area that contains finite BSs [64].…”

Section: H Analytical Techniquesmentioning

confidence: 99%

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

et al. 2021

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Next generation wireless networks are expected to be highly heterogeneous, multi-layered, with embedded intelligence at both the core and edge of the network. In such a context, system-level performance evaluation will be very important to formulate relevant insights into tradeoffs that govern such a complex system. Over the past decade, stochastic geometry (SG) has emerged as a powerful analytical tool to evaluate system-level performance of wireless networks and capture their tendency towards heterogeneity. However, with the imminent onset of this crucial new decade, where global commercialization of fifthgeneration (5G) is expected to emerge and essential research questions related to beyond fifth-generation (B5G) are intended to be identified, we are wondering about the role that a powerful tool like SG should play. In this paper, we first aim to track and summarize the novel SG models and techniques developed during the last decade in the evaluation of wireless networks. Next, we will outline how SG has been used to capture the properties of emerging radio access networks (RANs) for 5G/B5G and quantify the benefits of key enabling technologies. Finally, we will discuss new horizons that will breathe new life into the use of SG in the foreseeable future. For instance, using SG to evaluate performance metrics in the visionary paradigm of molecular communications. Also, we will review how SG is envisioned to cooperate with machine learning seen as a crucial component in the race towards ubiquitous wireless intelligence. Another important insight is Grothendieck toposes considered as a powerful mathematical concept that can help to solve longstanding problems formulated in SG.

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Section: H Analytical Techniquesmentioning

confidence: 99%

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

et al. 2021

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“…Denoting by s b l the horizontal distance from the reference UAV to the nearest LOS BS in φ g l , the CCDF of s b l can be calculated as The reference UAV connects with a backhaul-enabled LOS BS in φ g l to get backhaul support if the nearest LOS BS has smaller path-loss than that of the nearest NLOS BS in φ gn . Thus, the probability A b l that the reference UAV is associated with a LOS BS can be derived as follows (15). A bn is obtained with the same procedure.…”

Section: Proof Of Lemmamentioning

confidence: 99%

Stochastic Geometry Analysis of Hybrid Aerial Terrestrial Networks with mmWave Backhauling

Kouzayha

ElSawy

Dahrouj

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Service providers are considering the use of unmanned aerial vehicles (UAVs) to enhance wireless connectivity of cellular networks. To provide connectivity, UAVs have to be backhauled through terrestrial base stations (BSs) to the core network. In particular, we consider millimeter-wave (mmWave) backhauling in the downlink of a hybrid aerial-terrestrial network, where the backhaul links are subject to beamforming misalignment errors. In the proposed model, the user equipment (UE) can connect to either a ground BS or a UAV, where we differentiate between two transmission schemes according to the backhaul status. In one scheme, the UEs are served by the UAVs regardless of whether the backhaul links are good or not. In the other scheme, the UAVs are aware of the backhaul links status, and hence, only the subset of successfully backhauled UAVs can serve the UEs. Using stochastic geometry, the performance of the proposed model is assessed in terms of coverage probability and validated against Monte-Carlo simulations. Several insights are provided for determining some system parameters including the UAVs altitude and required number and the beamforming misalignment error of the backhaul link. The obtained results highlight the impact of the UAVs backhaul link on the UE experience.

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“…where n = k implies that k( s v + w) ≤ t ≤ k( s v + w) + w. Getting back to (7), we can now compute the distribution of L n (t) for a given time t as follows.…”

Section: Srwp Interference Field Characterizationmentioning

confidence: 99%

“…The authors of [6] considered a finite network of static DBSs distributed as a binomial point process (BPP) and analyzed the coverage probability of the network. In [7], a superposition of macro and aerial BSs is considered in which probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) propagation models were adopted for the channel. The authors of [8] added mobility to the BPP-modeled DBS network of [6] and designed stochastic trajectory processes for the mobility of DBSs in order to gain the same coverage profile as the static case, while improving the average fade duration.…”

Section: Introductionmentioning

confidence: 99%

Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

Banagar

Dhillon

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we present the first stochastic geometry-based performance analysis of a drone cellular network in which drone base stations (DBSs) are initially distributed based on a Poisson point process (PPP) and move according to a random waypoint (RWP) mobility model. The serving DBS for a typical user equipment (UE) on the ground is selected based on the nearest neighbor association policy. We further assume two service models for the serving DBS: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). All the other DBSs are considered as interfering DBSs for the typical UE. We introduce a simplified RWP (SRWP) mobility model to describe the movement of interfering DBSs and characterize its key distributional properties that are required for our analysis. Building on these results, we analyze the interference field as seen by the typical UE for both the UIM and the UDM using displacement theorem, which forms the basis for characterizing the average rate at the typical UE as a function of time. To the best of our knowledge, this is the first work that analyzes the performance of a mobile drone network in which the drones follow an RWP mobility model on an infinite plane.

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Modeling and Analysis of Aerial Base Station-Assisted Cellular Networks in Finite Areas Under LoS and NLoS Propagation

Cited by 69 publications

References 34 publications

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

Stochastic Geometry Analysis of Hybrid Aerial Terrestrial Networks with mmWave Backhauling

Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

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