Near Real-Time Data-Driven Adaptive RAN Slicing of VR Traffic in ORAN

Open Radio Access Network (ORAN) provides a flexible framework for network slicing in 4G/5G systems, with xApps enabling efficient near-real-time resource management. The purpose of slicing is to allocate resources in a way that meets the heterogeneous demands posed by diverse services, while minimizing the amount of unused resources. We demonstrate experimentally how xApps can support slicing through near real-time control of Radio Access Network (RAN) resources and at the same time avoid resource waste due to overprovisioning. We develop an open-loop implementation for Radio Resource Management (RRM) to allocate resources efficiently across slices to serve multiple users of Virtual Reality (VR) traffic with latency guarantees. Prior research concluded that VR traffic is highly variable in its demand. We evaluate the methods’ ability to meet latency targets and minimize overprovisioning under variable traffic using specialized xApps with open-loop prediction. Our findings reveal distinct trade-offs between the methods: our proposed solution, Latency Estimation Correction (LEC), offers superior latency control but at the cost of resource efficiency; it emphasizes estimating end-to-end latency and guides the allocation accordingly. Conversely, a simpler Packet Size Prediction (PSP) approach demonstrates improved resource efficiency, though it sacrifices control over latency. The method leverages traffic forecasting to predict the required allocation. This comparison underscores the balance between latency management and resource utilization in the context of network slicing and ORAN. Aggregating users reduces resource overprovisioning by 30%, though it introduces a slight increase in end-to-end latency offset. Across target latencies ranging from 10 to 16.67 ms, our evaluations show a stable average offset of 0.6 ms.