Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment - Shu-Ping Yeh; Sonia Bhattacharya; Rashika Sharma; Hassnaa Moustafa (IEEE Open Journal of the Communications Society, 2024)
5G and beyond networks are considered a catalyst for emerging IoT applications and services by providing ultra-reliable connectivity and massive connections to billions of IoT sensors and devices. However, the scalable deployment of such services requires reduced cost, an open ecosystem for IoT application developers and service providers, and a multi-tenant deployment model enabling the 5G and beyond network infrastructure to host multiple IoT services while preserving the service level agreement (SLA) requirements. AI brings intelligence to the network infrastructure to automate several network functions and predict the service’s workload to ensure network function scaling and adaptation. 5G brings AI to the radio access network (RAN) to reduce the operation cost, decrease power consumption and boost service quality. With this evolution towards AI-based features in the network, the Open RAN (ORAN) specification expanded the network functions virtualization to the RAN intelligence by introducing RAN Intelligent Controller (RIC) to enable AI applications for the network functions. This paper focuses on the RAN intelligence ecosystem and presents an intelligent network application (xApp) for network slicing for the RAN using AI and Deep Learning techniques. We evaluated the xApp with a near Real-Time RAN Intelligent Controller (near-RT RIC) and showed the network slicing functionality in an automated and intelligent fashion. We show how intelligent network slicing enables emerging IoT services to co-exist while meeting the required SLAs.