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Grant: Discovery Grants Program - Individual

Toronto Metropolitan University

$46,000.00

Machine Learning-Aided Solutions for Efficient Planning, Design, Operation and Adaptation of Beyond 5G Wireless Networks

EF 2024-25

Résumé
With the rapid growth in the number of smart devices, service types/demands and network complexity with the increased inflow of raw data, leveraging intelligence into future wireless networks to make them more intelligent and automotive has become a crucial need. The upcoming 5G and Beyond (B5G) wireless networks are expected to create a foundation for intelligent and dynamic networks with some isolated Artificial Intelligence (AI) operations, however, full-fledged intelligence and automation with the capabilities of self-configuration, self-optimization and self-healing operations will require significant amount of further research, and seems feasible only in B5G wireless networks, with the incorporation of emerging AI/Machine Learning (ML) techniques. The long-term objective of the proposed research program is to develop AI/ML-assisted resource-efficient, reliable, secure and cost-efficient low-complexity communication technologies/protocols towards enabling full-fledged intelligent and autonomous B5G networks. To accomplish this long-term objective, the short-term objectives are: (1) Develop AI)/ML-assisted solutions for efficient planning, design, operation and adaptation of wireless networks including IoT and aerial networks, while improving various performance metrics of future networks such as energy efficiency, operational efficiency, end-to-end latency, connection density, coverage, security and privacy; (2) Develop ML-assisted solutions for Radio Intelligent Controllers (RICs) of Open Radio Access Network (O-RAN) in order to enhance the radio resource management efficiency and reduce the overall computational complexity, and (3) Develop secure-aware resource management schemes for the 5G edge-cloud infrastructure. The outcomes of the proposed research will significantly benefit the scientific communities in addressing various underlying technical issues in B5G wireless networks, and society in addressing several societal and economic challenges by supporting the ever-increasing massive number of heterogeneous devices and sophisticated applications. Besides, deploying the investigated solutions for RICs in future O-RAN architecture will help Canadian Telecom operators to gain significant benefits in terms of deployment flexibility, agility, resource utilization efficiency and reduction of operating cost. On the other hand, the security of the 5G cloudified infrastructure is vital to ensure the success of the digital economy in Canada. In this sense, the proposed AI-empowered security-aware resource managers will contribute in realizing a safe 5G operation since it will foster the inclusion of security by design strategies when it comes to the design and development of new resource allocation mechanisms for 5G networks, which traditionally emphasize performance metrics such as blocking and dropping probabilities for the offloaded tasks while neglecting the importance of risk awareness to decrease the likelihood of attacks.
Programme
Discovery Grants Program - Individual
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2022-03798
Name nom
Woungang, Isaac
Department département
Computer Science
Organization id
43
Institution établissement
Toronto Metropolitan University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
46000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1510
Committee name en
Electrical and Computer Engineering
Committee name fr
Génie électrique et informatique
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2508
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Communications networks
Research subject fr
Réseaux de télécommunications
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Machine Learning-Aided Solutions for Efficient Planning, Design, Operation and Adaptation of Beyond 5G Wireless Networks
Keywords
machine learning for wireless communications; beyond 5g wireless networks (b5g); wireless internet of things; edge intelligence in internet of things; monitoring and network optimization in b5g; radio resource mavnagement; secure-aware resource managers; tactile internet; mobile cloud/edge computing; industrial internet of things
Application summary
With the rapid growth in the number of smart devices, service types/demands and network complexity with the increased inflow of raw data, leveraging intelligence into future wireless networks to make them more intelligent and automotive has become a crucial need. The upcoming 5G and Beyond (B5G) wireless networks are expected to create a foundation for intelligent and dynamic networks with some isolated Artificial Intelligence (AI) operations, however, full-fledged intelligence and automation with the capabilities of self-configuration, self-optimization and self-healing operations will require significant amount of further research, and seems feasible only in B5G wireless networks, with the incorporation of emerging AI/Machine Learning (ML) techniques. The long-term objective of the proposed research program is to develop AI/ML-assisted resource-efficient, reliable, secure and cost-efficient low-complexity communication technologies/protocols towards enabling full-fledged intelligent and autonomous B5G networks. To accomplish this long-term objective, the short-term objectives are: (1) Develop AI)/ML-assisted solutions for efficient planning, design, operation and adaptation of wireless networks including IoT and aerial networks, while improving various performance metrics of future networks such as energy efficiency, operational efficiency, end-to-end latency, connection density, coverage, security and privacy; (2) Develop ML-assisted solutions for Radio Intelligent Controllers (RICs) of Open Radio Access Network (O-RAN) in order to enhance the radio resource management efficiency and reduce the overall computational complexity, and (3) Develop secure-aware resource management schemes for the 5G edge-cloud infrastructure. The outcomes of the proposed research will significantly benefit the scientific communities in addressing various underlying technical issues in B5G wireless networks, and society in addressing several societal and economic challenges by supporting the ever-increasing massive number of heterogeneous devices and sophisticated applications. Besides, deploying the investigated solutions for RICs in future O-RAN architecture will help Canadian Telecom operators to gain significant benefits in terms of deployment flexibility, agility, resource utilization efficiency and reduction of operating cost. On the other hand, the security of the 5G cloudified infrastructure is vital to ensure the success of the digital economy in Canada. In this sense, the proposed AI-empowered security-aware resource managers will contribute in realizing a safe 5G operation since it will foster the inclusion of security by design strategies when it comes to the design and development of new resource allocation mechanisms for 5G networks, which traditionally emphasize performance metrics such as blocking and dropping probabilities for the offloaded tasks while neglecting the importance of risk awareness to decrease the likelihood of attacks.