Real-time detection and optimization algorithms for flexible resource allocation in 5G networks and beyond
Abstract
The roll-out of fifth-generation (5G) mobile networks and the forthcoming sixth-generation (6G) will bring about fundamental changes in the way we communicate, access services and entertainment. With respect to the latter, the multi-fold increase in the service data rates of enhanced mobile broadband (eMBB) services will provide users with ultra high resolution in video-streaming, multi-media and virtual reality, offering immersive experiences. To this end, it is important for Edge content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. For flexible and highly adaptive solutions, the capability for quick resource (re-) allocation should be driven by early and low-complexity content popularity detection schemes. In the present thesis, we study aspects of low-complexity detection of changes in video content popularity in real-time, addressed as a statistical change point (CP) detection problem, breaking completely new ground compared to e ...
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