Performance of mmWave UAV‐Assisted 5G Hybrid Heterogeneous Networks

Shehzad, Muhammad Karam; Akhtar, Muhammad Waseem; Hassan, Syed Ali

doi:10.1002/9781119751717.ch6

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…The UAV-assisted cellular network is the case where a single/multiple UAV-BSs is/are deployed in existing terrestrial BSs in order to enhance certain network performance indicators such as throughput, coverage, etc. [121,122]. However, their deployment could result in an increase in the overall energy consumption of the network if not properly managed.…”

Section: Optimization Of the Energy Consumption In Uav-assisted Cellu...mentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Optimization Of the Energy Consumption In Uav-assisted Cellu...mentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…This scenario, in particular, can happen in the network, where GTs are static. For instance, as presented in [3], [5]- [7], where ground SCBSs are static, and UAVs are used as communication hubs to route the uplink/downlink traffic of cellular users between the ground core-network and the SCBSs. Or, alternatively, when GTs are static cellular users (e.g., cellular users watching a football match in the stadium, attending a seminar, or cellular users sitting at home due to the lockdown of global coronavirus pandemic).…”

Section: B Csi Compression At Gtmentioning

confidence: 99%

“…Despite the fact that UAV can play a pivotal role in the modern era of wireless communication, there are multiple challenges associated with such a technology, and many of them have been addressed in the literature. For example, association (serving a group of network entities) of SCBSs with UAVs [5], placement of UAVs [6], [7], channel modeling [8], and so on. Nevertheless, to the best of our knowledge, channel state information (CSI) estimation, prediction, compression, and reporting have not been considered so far.…”

Section: Introductionmentioning

confidence: 99%

RNN-Based Twin Channel Predictors for CSI Acquisition in UAV-Assisted 5G+ Networks

Shehzad

Rose

Assaad

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Unmanned aerial vehicles (UAVs) evolution has gained an unabated interest for the use in several applications, such as agriculture, aerial surveillance, goods delivery, disaster recovery, intelligent transportation. The main features of this technology are high coverage, strong line-of-sight (LoS) links, promising throughput, cost-effective and flexible deployment. Currently, the Third Generation Partnership Project (3GPP) is working on the specification of release-17 (R-17) new radio (NR) for non-terrestrial networks (NTN). Therefore, owing to the drastic increase of UAV technology, in this paper, we propose channel state information (CSI) compression and its recovery with the aid of machine learning (ML)-based twin channel predictors. Due to the characteristic of gaining higher LoS communication paths in UAV network, the proposed strategy can bring potential benefits such as over-the-air (OTA)-overhead reduction, minimizing mean-squared-error (MSE) of a channel and maximizing precoding gain. Simulation-based results corroborate the validity of the proposed strategy, which can reap benefits in multiple factors.

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UAV Trajectory Optimization and Choice for UAV Placement for Data Collection in Beyond 5G Networks

Shehzad

Hassan

Luque-Nieto

et al. 2022

Intelligent Unmanned Air Vehicles Communications for Public Safety Networks

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Performance of mmWave UAV‐Assisted 5G Hybrid Heterogeneous Networks

Cited by 4 publications

References 37 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

RNN-Based Twin Channel Predictors for CSI Acquisition in UAV-Assisted 5G+ Networks

UAV Trajectory Optimization and Choice for UAV Placement for Data Collection in Beyond 5G Networks

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