QoS Provisioning and Resource Block Management in AI-Enabled Networks

With the rise of requirements for high-speed and low-latency connectivity, innovative approaches such as network slicing, Quality of Service (QoS) Provisioning, and reinforcement learning-based resource allocation, including the use of resource blocks (RBs) including radio resources, have to keep pace with these evolving requirements. By utilising network intelligence through machine learning and deep reinforcement learning, there is a potential to enhance QoS provisioning, expand the current network capacity, reduce congestion and latency, improve energy efficiency, and thus support new business models and revenue sources. The progress so far suggests that while considering RBs, network-slicing datasets, intertwined with QoS provisioning, have not been explored comprehensively, and packet drop probability or rate has not been taken into account in resource allocation. This paper proposes a network slicing method that uses seven machine learning algorithms and demonstrates its efficiency and accuracy compared to benchmarks in the literature with respect to QoS. Moreover, a priority algorithm is developed to ensure that packets with a high chance of being dropped (affecting QoS) are queued first. A resource allocation algorithm considering QoS provisioning and RBs is utilised to improve network performance based on a mathematical derivation of packet drop rate. Furthermore, a virtualisation of the processing between Cloud and Edge depends on the network slice. By intelligently distributing tasks between Cloud and Edge resources using deep reinforcement learning and genetic algorithms, an offloading script ensures uninterrupted service availability even when all network resources (i.e., RBs) are in use, thus maintaining the desired QoS.