Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling

Challita, Ursula; Saad, Walid

doi:10.1109/glocom.2017.8254715

Cited by 92 publications

(61 citation statements)

References 19 publications

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“…In particular, they can be trained to learn the antenna tilting angle based on the environment changes in order to guarantee a LoS communication link with the users and, thus, to enable an efficient communication over the mmWave spectrum. Moreover, ANNs may enable multiple BSs to learn how to form multi-hop, mmWave links over backhaul infrastructure, while properly allocating resources across those links in an autonomous manner [154], [155]. To cope with the changes in the traffic model and/or the users' mobility pattern, ANNs can be combined with online ML [156] by properly re-training the weights of the developed learning mechanisms.…”

Section: ) Co-existence Of Multiple Radio Access Technologiesmentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

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836

418

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In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based machine learning algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important for wireless network design. Such examples include echo state networks, liquid state machine, and long short term memory. And then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks and edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this article constitutes the first holistic tutorial on the development of ANN-based machine learning techniques tailored to the needs of future wireless networks. arXiv:1710.02913v2 [cs.IT] 30 Jun 2019• Input gate (i t ): controls whether the input is passed on to the memory cell or ignored. • Output gate (o t ): controls whether the current activation

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Section: ) Co-existence Of Multiple Radio Access Technologiesmentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

836

418

View full text Add to dashboard Cite

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“…The wireless links between node i ∈ A (i.e., ABSs) and node j ∈ G (i.e., the source, the destination, and the interference sources) can either be line-of-sight (LoS) or non-line-of-sight (NLoS). However, only LoS channels are considered for the links between the nodes i, j ∈ A [27]. In our model, we assume channel reciprocity for all links.…”

Section: B Abs A2a and A2g Channel Modelsmentioning

confidence: 99%

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Rahmati

Güvenç

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

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“…Next, we consider the wireless transmission over the A2G access links from the UAVs to the ground users, and that over the A2A backhaul links among UAVs. For both A2G and A2A links, we suppose that the wireless channels are dominated by the LoS link, and therefore, we use the free space path loss model, as commonly adopted in the UAV communication literature [8,22,23]. Therefore, the channel power gain from UAV m to user k is expressed as…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

2018

J. Commun. Inf. Netw.

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Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users in various applications (e.g., in emergency situations). This paper considers a UAV-enabled wireless network, in which multiple UAVs are deployed as aerial base stations (BSs) to serve users distributed on the ground. Different from prior works that ignore UAVs' backhaul connections, we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls. We also consider that the air-to-ground (A2G) access links from UAVs to users and the air-to-air (A2A) backhaul links among UAVs are operated over orthogonal frequency bands. Under this setup, we aim to maximize the common (or minimum) throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs, by optimizing the UAVs' deployment locations, jointly with the bandwidth and power allocation of both the access and backhaul links. However, the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally. To tackle this issue, we use the techniques of alternating optimization and successive convex programming (SCP) to obtain a locally optimal solution. Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.Index Terms-Unmanned aerial vehicle (UAV), wireless networks, multi-hop backhauls, deployment optimization, bandwidth and power allocation. ). J. Xu is the corresponding author.in Internet-of-things (IoT) networks) [10]. Besides wireless communications, UAVs can also be used as aerial platforms for wireless power transfer (WPT) [11,12], wireless powered communication networks (WPCN) [13], and mobile edge computing (MEC) [14]. In the industry, various companies have launched their UAV-assisted wireless communication projects, and some preliminary prototypes include Facebook's Aquila [15] and Nokia's flying-cell (F-Cell) [16].As compared to conventional terrestrial wireless communications, UAV-assisted wireless communications have the following advantages. First, UAV-enabled aerial communication platforms can be quickly deployed on demand, and thus are cost-effective and suitable for emergency scenarios, e.g., when the terrestrial wireless infrastructures are damaged due to natural disasters. Next, the air-to-ground (A2G) wireless channels between UAVs and ground nodes normally have much stronger line-of-sight (LoS) links than conventional groundto-ground (G2G) wireless channels; as a result, the aerial BSs are expected to provide better wireless coverage and higher communication throughput than ground BSs. Furthermore, due to the fully controllable mobility in three-dimensional (3D) airspace, UAVs can adaptively change their locations over time for reducing the distances with intended ground users, so as to further improve the communication performance.In general,...

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Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling

Cited by 92 publications

References 19 publications

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

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