An Intelligent Radio Resource Management Using AI for 5G ...
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Nidhi (PhD Student, AU), Dr. Albena Dimitrova Mihovska (Associate Professor, AU) An Intelligent Radio Resource Management Using AI for 5G and Beyond TeanUp5G aims to satisfies the user requirements with the emerging technologies and solutions.TeamUp5G project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project number 813391. Project Background New RAN TEchniques for 5G UltrA-dense Mobile networks - TeamUp5G TeamUp5G is a European Training Network (ETN) in the frame of the Marie Skłodowska- Curie Innovative Training Networks (MSCA ITN) of the European Commission’s Horizon 2020 framework. It is a multi-partner research-training network with both academic and non-academic beneficiaries. It provides a research platform integrating learning with real-life technical issues. Architectures and Supporting Algorithms for Spectrum and CA for Small Cells and Ultradense Deployments Reference Scenarios, KPIs, Use Cases and System Requirements Design and Evaluate Architectures and Supporting Energy-Efficient Algorithms Support Energy Efficient Communications Seamless Handover between Radio (RF) and Non- Radio (e.g. VLC) Technologies Offload the Unlicensed Spectrum. Development of Resource Sharing and CA Techniques for UltraDense Networks Development of Energy Efficient Protocols Vehicular Communications and Energy Efficiency AI Potential Reduced Capital Expenditure Improves MIMO Energy Management at Radio Sites Provide Paradigm Shift from Managing Networks to Managing Services Scalable and Flexible Application-Centric Platform Digital Transformation of Businesses Traffic Management and Need-Based Routing Protocols Machine Learning Algorithms to Manage Massive Data Flow Terahertz Frequency Communication Intelligent Resource Allocation Network Performance Management for Telecommunication Operators Research Objective The research inspiration is to target the IoT and the cellular broadband applications with an intelligent Radio Resource Management procedure with the implementation of Artificial Intelligence (AI) Methodology Dynamic and Modular Resource Management Architecture based on Reference Model Incorporate Sensors and Prediction Capabilities to Architectural Components Incorporate the AI Capabilities to Allocate Resources Determination of the Tradeoff Using Multi-Techniques Evaluation of Network Performance Enhanced Energy Efficiency and Data Rate Concept/Hypothesis Digestion and Absorption Fission and Assimilation Egestion Ingestion Network Requirements • Quality Education • Economic Stability • Environmental Sustainability • Innovation and Infrastructure • Responsible Consumption and Production • Employment Stability and Opportunities Long-Term Evolution Goals • RAN Agnostic/Automatically Orchestrated Transceivers • Non-device Centric Communications • Extreme URLLC • Consent and Privacy Preserving Data Sharing • Support for Ambient/Novel Sensing • Small Data AI (Distributed Learning) • Terahertz Technologies • 4D-Imaging and Image Projection • Haptic Remote Telepresence • Full Spectrum Photonic Signal Processing • Proactive Decision Making • Pervasive User Identification and Authentication • Net Neutrality • Zero-energy Communications • AI Inspired Air Interfaces • Grant Free Access (IoT) Technology Enablers • Education Innovations • Health and Wellbeing Services • Urbanization vs. Remote • Infrastructure • Data Security and Privacy Societal Challenges • Health • Manufacturing • Finance Technologies • Society 5.0 • Transport Productivity in Vertical Industries Expected Outcome An intelligent architecture for allocating radio resources based on user-behavior, network performance and use case scenario prediction models Enhanced Energy Efficiency and Data Rate Ultra Reliable Low Latency Communication Generic model that should satisfy communication systems beyond 5G Scan for detailed documentation Reference Model: SCIDAS [1] 1. A. Kumar and P. L. Mehta, "Architectural Framework for Good Data: A Realm for General Data Protection Regulation," 2018 21st International Symposium on Wireless Personal Multimedia Communications (WPMC), Chiang Rai, Thailand, 2018, pp. 375-380 doi: 10.1109/WPMC.2018.8712907
Transcript of An Intelligent Radio Resource Management Using AI for 5G ...
Nidhi (PhD Student, AU), Dr. Albena Dimitrova Mihovska (Associate Professor, AU)
An Intelligent Radio Resource Management Using AI for 5G and Beyond
TeanUp5G aims to satisfies the user requirements with the emerging technologies and solutions.TeamUp5G project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project number 813391.
Project Background
New RAN TEchniques for 5G UltrA-dense Mobile networks - TeamUp5G
TeamUp5G is a European Training Network (ETN) in the frame of the Marie Skłodowska-Curie Innovative Training Networks (MSCA ITN) of the European Commission’s Horizon2020 framework. It is a multi-partner research-training network with both academic andnon-academic beneficiaries. It provides a research platform integrating learning withreal-life technical issues.
Architectures and Supporting Algorithms for Spectrum and CA for
Small Cells and Ultradense Deployments
Reference Scenarios, KPIs, Use Cases and System Requirements
Design and Evaluate Architectures and Supporting Energy-Efficient Algorithms
Support Energy Efficient Communications
Seamless Handover between Radio (RF) and Non-Radio (e.g. VLC) Technologies
Offload the Unlicensed Spectrum.
Development of Resource Sharing and CA
Techniques for UltraDense Networks
Development of Energy Efficient Protocols
Vehicular Communications and
Energy Efficiency
AI Potential
Reduced Capital Expenditure
Improves MIMO Energy Management at Radio Sites
Provide Paradigm Shift from Managing Networks to Managing Services
Scalable and Flexible Application-Centric Platform
Digital Transformation of Businesses
Traffic Management and Need-Based Routing Protocols
Machine Learning Algorithms to Manage Massive Data Flow
Terahertz Frequency Communication
Intelligent Resource Allocation
Network Performance Management for Telecommunication Operators
Research Objective
The research inspiration is to target the IoT and the cellular broadband applications with an intelligent Radio Resource Management procedure with the implementation of Artificial Intelligence
(AI)
Methodology
Dynamic and Modular Resource Management Architecture based on Reference Model
Incorporate Sensors and Prediction Capabilities to Architectural Components
Incorporate the AI Capabilities to Allocate Resources
Determination of the Tradeoff Using Multi-Techniques
Evaluation of Network Performance
Enhanced Energy Efficiency and Data Rate
Concept/Hypothesis
Digestion and Absorption
Fission and AssimilationEgestion
Ingestion
Network Requirements
• Quality Education• Economic Stability• Environmental Sustainability• Innovation and Infrastructure• Responsible Consumption and Production• Employment Stability and Opportunities
Long-Term Evolution Goals
• RAN Agnostic/Automatically Orchestrated Transceivers• Non-device Centric Communications• Extreme URLLC• Consent and Privacy Preserving Data Sharing• Support for Ambient/Novel Sensing• Small Data AI (Distributed Learning)• Terahertz Technologies • 4D-Imaging and Image Projection• Haptic Remote Telepresence• Full Spectrum Photonic Signal Processing• Proactive Decision Making • Pervasive User Identification and Authentication• Net Neutrality• Zero-energy Communications• AI Inspired Air Interfaces• Grant Free Access (IoT)
Technology Enablers
• Education Innovations• Health and Wellbeing Services• Urbanization vs. Remote• Infrastructure• Data Security and Privacy
Societal Challenges
• Health• Manufacturing• Finance Technologies• Society 5.0• Transport
Productivity in Vertical Industries
Expected Outcome
An intelligent architecture for allocating radio resources based on user-behavior, network performance and use case scenario prediction models
Enhanced Energy Efficiency and Data Rate
Ultra Reliable Low Latency Communication
Generic model that should satisfy communication systems beyond 5G
Scan for detailed documentation
Reference Model: SCIDAS [1]
1. A. Kumar and P. L. Mehta, "Architectural Framework for Good Data: A Realm for General Data Protection Regulation," 2018 21st International Symposium on Wireless Personal Multimedia Communications (WPMC), Chiang Rai, Thailand, 2018, pp. 375-380 doi: 10.1109/WPMC.2018.8712907
