SLA-AWare RAN Slicing Via Online Meta-Learning

Abstract

Real-time inter-slice resource allocation in the Radio Access Network (RAN) is a critical control function in 5G and emerging 6G networks, where the scheduler in the Distributed Unit (DU) dynamically allocates physical resources, namely Physical Resource Blocks (PRBs), to different network slices to meet their diverse Quality of Service (QoS) requirements. To address the need for faster and more flexible radio resource management, and inspired by recent efforts to extend the O-RAN architecture with a real-time controller, we investigate slice-level PRB allocation through the lens of online learning. We formulate inter-slice scheduling as a dynamic decision problem and develop a system model that captures per-slice Service Level Agreement (SLA) requirements and throughput variations over configurable time windows, without assuming future channel knowledge. Our scheduling solution is implemented as a real-time RAN control application, in line with the O-RAN proposition for dApps that are programmable and distributed software components for fine-grained control in O-RAN DUs (O-DUs) and Centralized Units (O-CUs). The proposed approach adapts inter-slice radio resource allocations based on telemetry, with low computational complexity. Experimental results show sublinear dynamic regret, up to 85% fewer SLA violations than static baselines, and submillisecond amortized control overhead. Overall, these findings highlight dynamic-benchmark online control as a practical mechanism for real-time, SLA-aware slicing in O-RAN.