TailO-RAN: O-RAN Control on Scheduler Parameters to Tailor RAN Performance

2025-08-16

AI Summary

To address the rigidity, inflexibility, and lack of QoS awareness in traditional RAN architectures, this paper proposes an O-RAN-native programmable scheduling framework. The method introduces an xApp-driven dynamic weight adjustment mechanism and pioneers a QoS-aware scheduling strategy based on the joint complementary cumulative distribution function (CCDF) of latency and reliability, enabling near-real-time closed-loop coordination between the RIC and application layer (computer vision). Implemented atop the OpenAirInterface 5G protocol stack and integrated within the Colosseum digital twin platform, the system supports reliable video streaming and real-time object detection for industrial IoT asset tracking. Experimental results demonstrate a 33% improvement in throughput compliance rate and up to a 37.04% increase in asset detection F1-score, significantly enhancing intelligent resource scheduling and cross-layer orchestration efficacy.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web search models and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The traditional black-box and monolithic approach to Radio Access Networks (RANs) has heavily limited flexibility and innovation. The Open RAN paradigm, and the architecture proposed by the O-RAN ALLIANCE, aim to address these limitations via openness, virtualization and network intelligence. In this work, first we propose a novel, programmable scheduler design for Open RAN Distributed Units (DUs) that can guarantee minimum throughput levels to User Equipments (UEs) via configurable weights. Then, we propose an O-RAN xApp that reconfigures the scheduler's weights dynamically based on the joint Complementary Cumulative Distribution Function (CCDF) of reported throughput values. We demonstrate the effectiveness of our approach by considering the problem of asset tracking in 5G-powered Industrial Internet of Things (IIoT) where uplink video transmissions from a set of cameras are used to detect and track assets via computer vision algorithms. We implement our programmable scheduler on the OpenAirInterface (OAI) 5G protocol stack, and test the effectiveness of our xApp control by deploying it on the O-RAN Software Community (OSC) near-RT RAN Intelligent Controller (RIC) and controlling a 5G RAN instantiated on the Colosseum Open RAN digital twin. Our experimental results demonstrate that our approach enhances the success percentage of meeting throughput requirements by 33% compared to a reference scheduler. Moreover, in the asset tracking use case, we show that the xApp improves the detection accuracy, i.e., the F1 score, by up to 37.04%.

Problem

• Enhancing RAN flexibility via programmable scheduler design
• Dynamically adjusting scheduler weights for throughput guarantees
• Improving IIoT asset tracking accuracy with xApp control

Innovation

• Programmable scheduler design for Open RAN DUs
• O-RAN xApp dynamically reconfigures scheduler weights
• Deployed on OSC near-RT RIC for 5G RAN control