Performance Evaluation of UAV-Enabled Cellular Networks With Battery-Limited Drones

The exploitation of aerial base stations (A-BSs) in conjunction with terrestrial base stations (T-BSs) is envisioned as a promising solution to provide connectivity to devices and user-equipment (UE) in crowded situations (viz. in the sports event) and emergency situations (viz. in the disaster management). However, the use of A-BSs with existing terrestrial networks intensifies the inter-cell interference (ICI) to the devices and UEs, therefore leading to a degraded signal-to-interference-ratio (SIR). This paper addresses this issue by exploiting different radio access technology (RAT) (mmWave/microwave) for aerial and terrestrial networks. Indeed, the network connectivity is always a top priority for all applications. However, there are also some applications such as remote patient monitoring, and remote working, which requires both coverage and high data-rates. But, most of the existing research claims the trade-off between the coverage and the data-rate performance. Whereas this paper aims to improve coverage and rate simultaneously in an aerial-terrestrial networks by employing an optimal combination of mmWave and microwave RAT based on the proposed association strategy. The essential analysis of such an integrated network involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the coverage probability (CP) and average rate expressions for the proposed integrated aerial-terrestrial networks. The analysis is supported by probabilistic models-based simulations that agree closely with analytical results. The results claim that the proposed model leads to improved performance in terms of both CP and average rate. Also, the paper provides parametric analysis for CP and rate with A-BSs height and A-BSs density to enable its practical implementation in 5G/6G technologies.INDEX TERMS Aerial-terrestrial networks, millimeter-wave RAT, microwave RAT, coverage probability, average rate, stochastic geometry.

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“…For m > 0, (35) can also be expressed by the series expansion as given in (33). This completes the proof of lemma.…”

Section: Appendix C Proof Of Lemmasupporting

confidence: 52%

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

et al. 2021

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“…A comprehensive survey about channel modeling for UAVassisted communications can be found in [174]. Also, given their technical constraints combined with ground UEs QoS requirements, optimal placement of UAVs is another challenging task, which may include UAVs trajectory optimization [259], altitude optimization [173], [260], flight time optimization [259], [261], and UAVs density optimization [262].…”

Section: B Non-terrestrial Networkmentioning

confidence: 99%

“…In [261], the authors adopted a similar abstraction of vertical HetNet, as in [262], to address the question of the limited energy resources when using UAVs as a BS, as this leads to restricted flight times, and therefore forces UAVs to regularly interrupt their operations to recharge or swap their batteries.…”

Section: B Non-terrestrial Networkmentioning

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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“…The system architecture of the UAV-enabled wireless network can be modified for the sake of a longer flight time [16]. Firstly, the influence of frequently interrupting and revisiting the charging stations was studied in [17] with emphasis on signal-tonoise-ratio (SNR) and the assumption that the charging stations have infinite capacity. Authors in [7], [18], [19], studied a system where the UAV is physically connected to a ground station through a tether.…”

Section: A Related Workmentioning

confidence: 99%

On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks

Qin

Kishk

Alouini

2021

IEEE Trans. on Green Commun. Netw.

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Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.

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Performance Evaluation of UAV-Enabled Cellular Networks With Battery-Limited Drones

Cited by 60 publications

References 10 publications

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

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

On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks

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