Machine Learning Support for Future Intelligent Networks

Supervisor: Dr Ahmed Sayed

Research group(s): Networks

Despite the huge success of the Internet, in many scenarios, the connectivity often falls short of expectations leading to devastating impacts on our services and applications. Currently, the legacy networks, which are the nerve system connecting all our services, lack sufficient intelligence to support the fast-paced evolution of many applications. This is a major obstacle that will be exacerbated when advanced applications like virtual reality, metaverse, autonomous vehicles, smart cities, digital economy, and real-time healthcare wish to be successfully deployed. Addressing this concern requires a drastic departure from legacy hard-coded and human-error-borne networking systems to developing more intelligent, agile and responsive networking systems. Thanks to the latest breakthroughs in software-defined networking, hardware programmability and ML for systems, in this project, we will tap into these developments to seek solutions to the current problems in a way that is deployable incrementally. This project will explore novel ML-based networking solutions and develop an ML-based support framework as the basis for Future Intelligent Networks (FIN).