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

点击翻译按钮获取中文摘要

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.