0000000001291584

AUTHOR

Lei Lei

showing 6 related works from this author

Energy-Efficient Resource Optimization with Wireless Power Transfer for Secure NOMA Systems

2018

In this paper, we investigate resource allocation algorithm design for secure non-orthogonal multiple access (NOMA) systems empowered by wireless power transfer. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among multiple users by optimizing time, power and subchannel allocation. Moreover, we also take into consideration for the practical case that the statistics of the channel state information of the eavesdropper is not available. In order to address the optimization problem and its high computational complexity, we propose an iterative algorithm with guaranteed convergence to deliver a suboptimal solution for gene…

Mathematical optimizationOptimization problemIterative methodComputer sciencewireless power transfer02 engineering and technologysecuritylangaton tiedonsiirto0203 mechanical engineeringoptimointi0202 electrical engineering electronic engineering information engineeringWirelessResource managementresource managementreceiversta213business.industryturvallisuusNOMA020206 networking & telecommunications020302 automobile design & engineeringwireless communicationChannel state informationlangaton viestintäResource allocationbusinessoptimizationEfficient energy use
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Energy-Efficient and Secure Resource Allocation for Multiple-Antenna NOMA with Wireless Power Transfer

2018

Non-orthogonal multiple access (NOMA) is considered as one of the promising techniques for providing high data rates in the fifth generation mobile communication. By applying successive interference cancellation schemes and superposition coding at the NOMA receiver, multiple users can be multiplexed on the same subchannel. In this paper, we investigate resource allocation algorithm design for an OFDM-based NOMA system empowered by wireless power transfer. In the considered system, users who need to transmit data can only be powered by the wireless power transfer. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among mul…

Computer Networks and CommunicationsOrthogonal frequency-division multiplexingComputer sciencewireless power transfer02 engineering and technologysecurityNoma0203 mechanical engineering0202 electrical engineering electronic engineering information engineeringmedicineWirelessResource managementta113: Computer science [C05] [Engineering computing & technology]ta213Renewable Energy Sustainability and the Environmentbusiness.industry020206 networking & telecommunications020302 automobile design & engineeringnon-orthogonal multiple access (NOMA)medicine.disease: Sciences informatiques [C05] [Ingénierie informatique & technologie]power allocationSingle antenna interference cancellationChannel state informationResource allocationsubchannel allocationbusinessEfficient energy useComputer network
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Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study (Intensive…

2021

The original version of this article unfortunately contained a mistake. The members of the ESICM Trials Group Collaborators were not shown in the article but only in the ESM. The full list of collaborators is shown below. The original article has been corrected.

pressure ulcerintensive care
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Adapting to Dynamic LEO-B5G Systems : Meta-Critic Learning Based Efficient Resource Scheduling

2022

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments.To address them, we first…

Signal Processing (eess.SP)FOS: Computer and information sciencesdynamic environmentComputer Science - Machine Learningreinforcement learningmeta-critic learningComputer Science - Artificial Intelligence5G-tekniikkaresursointiMachine Learning (cs.LG): Electrical & electronics engineering [C06] [Engineering computing & technology]LEO satelliteslangaton tiedonsiirtoresources allocationalgoritmitFOS: Electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal Processing: Ingénierie électrique & électronique [C06] [Ingénierie informatique & technologie]Applied MathematicstietoliikennesatelliititComputer Science ApplicationsArtificial Intelligence (cs.AI)koneoppiminenresource schedulinglangattomat verkot
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Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era

2018

The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resou…

wireless networksContent popularityEdge deviceComputer scienceBig data5G-tekniikkaRadio resource02 engineering and technologyWireless network architecturebig data5G mobile communication0202 electrical engineering electronic engineering information engineeringElectrical and Electronic Engineeringta113: Computer science [C05] [Engineering computing & technology]hidden Markov modelsbusiness.industry020208 electrical & electronic engineeringWireless dataanalytical models020206 networking & telecommunications: Sciences informatiques [C05] [Ingénierie informatique & technologie]Computer Science Applicationsdata modelskoneoppiminenmachine learningdevice-to-device communicationEnhanced Data Rates for GSM EvolutionCachebusinesslangattomat verkotComputer networkIEEE Wireless Communications
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Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

2020

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347

MaleOriginalmedicine.medical_treatmentartificialCritical Care and Intensive Care MedicineMedical and Health SciencesPressure ulcerlaw.inventionDecubitus epidemiology; ICU; Morbidity; Mortality; Outcome; Pressure injury; Pressure ulcer; Risk factors; Adult; Aged; Hospital Mortality; Humans; Male; Patient Discharge; Prevalence; Risk Factors; Intensive Care Units; Respiration Artificial0302 clinical medicineDecubitus epidemiologydecubitus epidemiology ; ICU ; pressure injury ; pressure ulcer ; outcome ; risk factors ; morbidity ; mortalitylawMedicine and Health SciencesadultsPrevalenceMedicineHospital MortalitySimplified Acute Physiology Scoredecubitus epidemiology; icu; pressure injury; pressure ulcer; outcome; risk factors; morbidityziekenhuissterfteImmunodeficiencyintensive careOutcomeEuropean Society of Intensive Care Medicine (ESICM) Trials Group CollaboratorsmannenvolwassenenCOSTIntensive care unitSTATEPatient DischargeIntensive Care UnitsULCERSUnderweightmedicine.symptomLife Sciences & BiomedicineDecubitus epidemiology; ICU; Morbidity; Mortality; Outcome; Pressure injury; Pressure ulcer; Risk factorsHumanAdultmedicine.medical_specialtyrisicofactorenDecubitus epidemiology ICU Pressure injury Pressure ulcer Outcome Risk factors Morbidity Mortalitypressure injuriesIntensive Care UnitprevalentieNO1117 Public Health and Health ServicesDecubICUs Study Team03 medical and health sciencesCritical Care MedicineAnesthesiologyGeneral & Internal MedicineHealth SciencesouderenHumansMortalityAgedMechanical ventilationScience & Technologybusiness.industrydecubitusRisk Factor030208 emergency & critical care medicine1103 Clinical SciencesOdds ratiomedicine.diseaseRespiration ArtificialEmergency & Critical Care MedicineConfidence interval030228 respiratory systemRisk factorsEmergency medicineICUkunstmatige ademhalingRISK-FACTORSMorbiditybusinessPressure injuryrespiration
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