On the Association of Small Cell Base Stations with UAVs Using Unsupervised Learning

Shehzad, Muhammad Karam; Hassan, Syed Ali; Mahmood, Aamir; Gidlund, Mikael

doi:10.1109/vtcspring.2019.8746456

Cited by 22 publications

(22 citation statements)

References 16 publications

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“…Also, the results are averaged over 1000 iterations. Moreover, in the considered scenarios, the results are scrutinized for all the approaches (i.e., conventional approach [14], unsupervised learning [20] and proposed hybrid approach). Figure 2 shows the bar plot of optimal child-UAVs heights for the proposed hybrid approach in all scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, a modified algorithm to consider the backhaul rate limit is presented to provide fairness to all the regions. Moreover, our work is different from [14][15][16] and [20] in the aspect that we consider the energy efficiency of UAVs while associating the SBs with them as the energy-efficient deployment of UAVs can help them stay aloft for a longer period [11].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this random distribution of UAVs results in higher interference and more power consumption; thus, it demands higher bandwidth. To overcome the higher bandwidth consumption problem, [20] provides an unsupervised learning-based UAVs placement, which clusters the SBs based on the Euclidean distance and then associates to UAVs. Consequently, [20] ignores the channel conditions, objective function's maximization, etc., which are of pivotal importance for the deployment of UAVs.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficient placement of UAVs in wireless backhaul networks

Shehzad¹,

Hassan²,

Luque-Nieto

et al. 2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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The enormous increase in cellular users requires novel advancements in the existing cellular infrastructure. Therefore, small cell networks (SCNs) are a promising solution to meet the ever-growing demands of cellular users as they are beneficial in terms of coverage and providing higher data rates. However, one of the challenging parts is the deployment of small cell base stations (SBs) and their connectivity with the backhaul network. In this paper, we use the scalable idea of replacing the terrestrial backhaul network with an aerial network to provide fronthaul connectivity to SBs. In particular, we address the optimum placement of unmanned aerial vehicles (UAVs) to associate the SBs such that the sum-rate of the overall network is maximized. We achieve such an objective by proposing a two-layer framework, i.e., unsupervised learning and iterative algorithm (defined as UAV equalizer), and we call this twolayer framework as a hybrid approach. Simulation results show that the proposed hybrid approach outperforms the traditional approaches in terms of maximizing the sum-rate, minimum bandwidth consumption, moreover, maximizing link utilization and energy efficiency. CCS CONCEPTS • Networks → Unmanned aerial vehicles; Small cell networks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy efficient placement of UAVs in wireless backhaul networks

Shehzad¹,

Hassan²,

Luque-Nieto

et al. 2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

Self Cite

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“…Recently, the unmanned aerial vehicle (UAV)-assisted communication has attracted significant attention of the researchers. UAV-assisted communication adds an extra-terrestrial network tier to a HetNet, which leads to several advantages in terms of network reliability and coverage [1]. A UAV base station (UAV-BS) can add a layer of UAV cells to realize new applications, especially in the scenario where the network demands are not static and there is a need to adapt to changing demands.…”

Section: Introductionmentioning

confidence: 99%

Spectrum on Demand: A Competitive Open Market Model for Spectrum Sharing for UAV-Assisted Communications

et al. 2020

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Unmanned aerial vehicles (UAVs)-assisted communication has gathered significant interest of the industry, especially with regards to the vision of providing ubiquitous connectivity for beyond 5G (B5G) networks. In this article, we motivate the need for utilizing licensed spectrum for UAV-assisted communication and discuss its advantages such as reliability and security. Moreover, we explore a new dimension to spectrum sharing by proposing a decentralized competitive open market approachbased model, where the different mobile network operators (MNOs) have the opportunity to lease the spectrum to UAV base stations (UAV-BSs), leading to new revenue generation opportunities. The proposed spectrum sharing mechanism is based on the logarithmic utility function and willingness to pay of each UAV-BS. We provide a tradeoff analysis between spectrum sharing and price offered by the MNOs, highlighting the impact of the willingness to pay on the spectrum sharing. The results also highlight the behaviour of price and spectrum shared w.r.t. time, thereby providing an insight into different performance regions until the algorithm converges to it's optimal value. In addition, we also present future directions that could lead to interesting analyses, especially with regards to incentive-based spectrum sharing and security.

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“…In [19], the authors provided a geometric lineof-sight (LoS) model for UAV and also depicted the optimal UAV altitude for maximum coverage probability. In [20], the authors computed the UAVs optimal position for backhauling the data between small cells and the core network. The authors in [21] studied and analyzed various quality-of-service (QoS) metrics for a coexisting network of sub-6 GHz and mmWave UAVs.…”

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confidence: 99%

Coverage Analysis of mmWave and THz-Enabled Aerial and Terrestrial Heterogeneous Networks

Raja

Pervaiz

Hassan

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)enabled unmanned aerial vehicles (UAVs) operating at 28 GHz and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper, whereas Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. It has been shown that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

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On the Association of Small Cell Base Stations with UAVs Using Unsupervised Learning

Cited by 22 publications

References 16 publications

Energy efficient placement of UAVs in wireless backhaul networks

Energy efficient placement of UAVs in wireless backhaul networks

Spectrum on Demand: A Competitive Open Market Model for Spectrum Sharing for UAV-Assisted Communications

Coverage Analysis of mmWave and THz-Enabled Aerial and Terrestrial Heterogeneous Networks

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