RICStar: AI-Enabled Traffic Scheduling and Resource Provisioning for RAN-Based Wireless IoT

Efficient resource management in the centralized unit user plane (CU-UP) of 5G and beyond networks is essential to support the diverse and dynamic demands of next-generation wireless Internet of Things (IoT) applications. In this article, we present a novel autoscaling technique that leverages the capabilities of the xApp and rApp features of the near-real-time radio access network (RAN) intelligent controller (near-RT RIC) and non-real-time RAN intelligent controller (non-RT RIC) to achieve sub-millisecond latency and broadband capabilities necessary for ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) slicing use cases in the RAN. The proposed RICStar leverages three quality-of-service (QoS) prediction models: latency prediction, throughput estimation, and provisioning latency models to execute optimal capacity adjustment decisions based on the output of a time-series traffic forecasting model. Our evaluation uses a publicly available dataset representative of eMBB and URLLC traffic, where the autoscaler adjusts the capacity units available for each slice. Our results indicate that RICStar achieves better QoS and cost performance compared to canonical autoscaling techniques. This improvement is achieved by closely tracking traffic demand trends and proactively autoscaling CU-UP resources. These findings underscore the benefits of predictive autoscaling in mitigating network degradation caused by under-provisioning while reducing costs for communication service providers (CSPs). As a result, CSPs can readily support emerging applications such as telemedicine, connected vehicles, and augmented reality/virtual reality (AR/VR) with greater capacity and reliability.