Towards the optimal orchestration of steerable mmWave backhaul reconfiguration

Santos, Reginaldo Bezerra dos; Skorin-Kapov, Nina; Ghazzai, Hakim; Kassler, Andreas; Tran, Gia Khanh

doi:10.1016/j.comnet.2021.108750

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Here, NFV is a platform for deploying applications with computing resources, which is known as MEC (Multi-Access Edge Computing). In [20], the authors proposed an algorithm for controlling backhaul between network devices using OVS (Open Virtual Switch) and Open Flow-based SDN under varying user requirements and showed its effectiveness through numerical analysis. In [21], a testbed was constructed to demonstrate the effectiveness of the proposed algorithm in tracking applications and networks as users move.…”

Section: Nfv/sdn As An Enabler For Uav Network-related Workmentioning

confidence: 99%

NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations

Tran

Ozasa

Nakazato

2022

Network

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In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate.

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Section: Nfv/sdn As An Enabler For Uav Network-related Workmentioning

confidence: 99%

NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations

Tran

Ozasa

Nakazato

2022

Network

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“…In many applications, such as network backhauling or ad hoc networks [ 13 , 14 ], orchestrating the mmWave network remains a challenging task. In fact, the beam alignment of the steerable antennas is required to establish links between any two nodes.…”

Section: Introductionmentioning

confidence: 99%

A WaveGAN Approach for mmWave-Based FANET Topology Optimization

Odat,

Ghazzai,

Alsharoa

2023

Sensors

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The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, for FANET topology optimization aiming to maximize the network throughput by selecting the communication paths with the best channel conditions. The proposed approach consists of a WaveGAN model followed by a beam search. The former learns how to generate optimized network topologies from a supervised dataset, while the latter adjusts the generated topologies to meet the structure requirements of the mmWave-based FANET. The simulation results show that the proposed approach is able to quickly find FANET topologies with a very small optimality gap for different network sizes.

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