Outage Analysis for Millimeter-Wave Fronthaul Link of UAV-Aided Wireless Networks

Fontanesi, Gianluca; Zhu, Anding; Ahmadi, Hamed

doi:10.1109/access.2020.3001342

Cited by 27 publications

(18 citation statements)

References 28 publications

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“…However, this presents a major challenge for the backhaul network, given the complexity of bringing the backhaul to every new cell in urban areas and also given the enormous rates resulting from mmWave bandwidths of the order of GHz. In this way, the authors of [164], [327] propose to exploit the interference isolation provided by the narrow directional mmWave beams so that a wired link can provide the backhaul for some new cells via mmWave beams. In particular, [164] evaluated self-backhauling concept wherein BSs are PPP-distributed into three classes: µWave BSs, mmWave BSs with wired backhaul, and mmWave BSs with wireless backhaul to those BSs with wired backhaul.…”

Section: A Stochastic Geometry For Communications In Higher Frequencmentioning

confidence: 99%

“…For mathematical convenience, an approximate blockage model is adopted such that all adjacent BSs to a given UE were treated as LOS and BSs beyond a given distance were neglected. In [327], the authors considered a C-RAN abstraction and examined the feasibility of bringing wireless fronthaul to distributed aerial units by means of mmWave beams. The outage probability is next derived using technique #5 that allows an upper bound based on Alzer's lemma.…”

Section: A Stochastic Geometry For Communications In Higher Frequencmentioning

confidence: 99%

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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: A Stochastic Geometry For Communications In Higher Frequencmentioning

confidence: 99%

Section: A Stochastic Geometry For Communications In Higher Frequencmentioning

confidence: 99%

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

et al. 2021

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“…Parameter α is the ratio of land area covered by buildings to total land area, β is the mean number of buildings per unit area, and γ denotes the distribution of building height. Note that the blockage model defined in (15) can be used for any transmitter/receiver heights for UAV-to-ground and ground-to-ground transmissions and for a wide spectrum range [48]. Furthermore, the non-LoS probability can be determined as pr NLoS…”

Section: ) Propagation Channelmentioning

confidence: 99%

Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

Arani

Zhu

2021

IEEE Access

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The integration of space and air components considering satellites and unmanned aerial vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has been envisioned as a promising solution to enhancing the terrestrial networks in terms of fairness, performance, and network resilience. However, employing UAVs introduces some key challenges, among which backhaul connectivity, resource management, and efficient three-dimensional (3D) trajectory designs of UAVs are very crucial. In this paper, low-Earth orbit (LEO) satellites are employed to alleviate the backhaul connectivity issues with UAV networks, where we address the problem of jointly determining backhaul-aware 3D trajectories of UAVs, resource management, and associations between users, satellites and base stations (BSs) in an STIN while satisfying ground users' quality-of-experience requirements and provisioning fairness concerning users' data rates. The proposed approach maximizes a novel objective function with joint consideration for BS's load and fairness, which can be categorized as a non-deterministic polynomial time hard (NPhard) problem. To tackle this issue, we leverage a reinforcement learning framework, in which our problem is modeled as a multi-armed bandit problem. Accordingly, BSs learn the environment and its dynamics and then make decisions under an upper confidence bound based method. Simulation results show that our proposed approach outperforms the benchmark methods in terms of fairness, throughput, and load.

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“…They showed that, although increasing the altitude increases the LoS probability, the altitude increase also increased signal path loss. In [10] the outage probability at mmWave and sub-6 GHz frequency is investigated for different blockage environments and UAV altitudes. The authors first derived analytical approximate expressions for the outage probability.…”

Section: Introductionmentioning

confidence: 99%

An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

Rahimi

Sobouti

Ghanbari

et al. 2022

IEEE Internet Things J.

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Using Unmanned Aerial Vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this paper, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

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Outage Analysis for Millimeter-Wave Fronthaul Link of UAV-Aided Wireless Networks

Cited by 27 publications

References 28 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

Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

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