Cooperative Small Cell HetNets With Dynamic Sleeping and Energy Harvesting

Alqasir, Abdullah; Kamal, Ahmed E.

doi:10.1109/tgcn.2020.2985496

Cited by 36 publications

(17 citation statements)

References 20 publications

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“…An initial assumption about the demand characterization can be made, but similar to the second case the UAVs will also collect local information that better characterize the demand. Machine learning also contributes to the demand profiling and the prediction of mobility patterns, e.g., [17], [18].…”

Section: Demand Forecasting and Characterizationmentioning

confidence: 99%

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Towards enabling unmanned aerial vehicles as a service for heterogeneous applications

et al. 2021

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Section: Demand Forecasting and Characterizationmentioning

confidence: 99%

“…Deep learning algorithms can also be used to capture and predict users mobility and their demands. To improve accuracy and expedite convergence, deep learning can be combined with probabilistic latent semantic analysis in order to characterize different classes of users traffic and their mobility [17].…”

Section: Demand Forecasting and Characterizationmentioning

confidence: 99%

Towards enabling unmanned aerial vehicles as a service for heterogeneous applications

et al. 2021

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“…A real-time pricing strategy was also proposed for SG based on the green IoT [27], which concentrated on the physical layer modeling, assuming the cyber layer to be optimally designed. On the other hand, although HSCNs have been popularized in 5G cellular networks to support large scale CPSs, characteristics of users/devices in the physical layer were mostly neglected [15], [28]. For instance, energy efficiency management of HSCNs was tackled with energy harvesting, based on sleeping strategies of SBSs [8], [28], which modelled the traffic flow as constant statistical traffic pattern affected by the cyber layer.…”

Section: Related Workmentioning

confidence: 99%

“…The dynamic MEC-SC powering scheme is proposed based on the sleeping strategy [28]. According to the heterogeneous operation modes of SGUEs, SBSs could fall into sleeping mode, when sufficiently few SGUEs are active in a time period, with the facilitation of D2D.…”

Section: A On-demand Mec-sc Powering Strategymentioning

confidence: 99%

Spatiotemporal D2D Small Cell Allocation and On-Demand Deployment for Microgrids

et al. 2021

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Microgrids in smart grid development create a new era towards resilient energy supply. In turn, They also render complicated cyber-physical system development towards high scalability and comprehensiveness. Heterogeneous Small Cell based Networks (HSCNs) endow a suitable cyber infrastructure for microgrid based cyber-physical systems. To tackle this emerging research area, this paper proposes a Spatiotemporal Device-to-device communication based HSCN Deployment scheme for Microgrid based Smart grid Development (3xSD), to achieve high energy efficiency with high qualityof-service, chronically and dynamically. Concretely, a joint optimization for long-term energy efficiency and achievable data rate maximization with interference mitigation is formulated for spatial small cell positioning, which models both cyber and physical characteristics of Smart Grid User Equipment (SGUE). A Temporal On-demand Small cell Powering and work-Offloading scheme (TOSPO) is proposed on top of the determined small cell positioning. TOSPO considers the real-time heterogeneous features and demands of SGUEs to maximize energy and cost efficiency, for both communication and computation perspectives. To evaluate the performance of 3xSD, spatial analysis, temporal analysis, and selected case study are simulated. Numerical results showcase that 3xSD is capable of providing both long-term and on-demand optimal energy efficiency and quality-of-service solution for microgrids, outperforming existing benchmarks in the literature.INDEX TERMS Small cells, smart grid, on-demand, energy efficiency, cyber-physical systems.

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“…The explosion of emerging wireless applications, significantly increasing spectrum utilization demand, and green-andsustainable communication induce energy efficiency and spectral efficiency to become critical design metrics for modern wireless communication networks (e.g., Fifth Generation (5G)) [1][2][3][4][5]. Indeed, one of the 5G system's main use cases is Internet of Things (IoT).…”

Section: Introductionmentioning

confidence: 99%

Security Enhancement for Energy Harvesting Cognitive Networks with Relay Selection

Ho-Van

Do-Dac²

2020

Wireless Communications and Mobile Computing

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Relay selection is proposed in this paper as an efficient solution to secure information transmission of secondary users against eavesdroppers in energy harvesting cognitive networks. The proposed relay selection method selects a secondary relay among available secondary relays, which are capable of harvesting radio frequency energy in signals of the secondary transmitter and correctly restore secondary message, to curtail signal-to-noise ratio at the wire-tapper. In order to evaluate the security performance of the suggested relay selection method, an exact intercept outage probability formula accounting for peak transmit power confinement, Rayleigh fading, and interference power confinement is firstly derived. Monte-Carlo simulations are then generated to corroborate the proposed formula. Numerous results expose that positions of relays, the number of relays, and parameters of the energy harvesting method significantly influence the security performance while the power confinements on secondary transmitters cause the performance saturation.

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Cooperative Small Cell HetNets With Dynamic Sleeping and Energy Harvesting

Cited by 36 publications

References 20 publications

Towards enabling unmanned aerial vehicles as a service for heterogeneous applications

Towards enabling unmanned aerial vehicles as a service for heterogeneous applications

Spatiotemporal D2D Small Cell Allocation and On-Demand Deployment for Microgrids

Security Enhancement for Energy Harvesting Cognitive Networks with Relay Selection

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