AI-fuelled Dimensioning and Optimal Resource Allocation of 5G/6G Wireless Communication Networks

The advent of 5G/6G broadband wireless networks brings several challenges with respect to optimal resource planning and allocation. In a heavily interconnected network of wireless devices, and users along with their equipment, all compete for scarce resources which further emphasizes the importance of fair and efficient allocation of those resources for the proper functioning of the networks. This paper tackles a crucial and timely topic, i.e., understand the various factors involved for optimizing network performance and ensuring fair access for different users, applications and devices. Integrating Machine Learning (ML) and Artificial Intelligence (AI) for predictive dimensioning and pattern mining over the network traffic can enable dynamic and intelligent resource allocation, increase network capacity, enhance the underlying capabilities between users and core network, and better correlate the Quality of Service (QoS). The scientific contribution of this paper entails novel AI models harvesting data from real-world 5G/6G testbeds offered through the AI as a Service (AIaaS) paradigm to enable model reuse and seamless exploitation for different 5G/6G application requirements and learning tasks.