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

TL;DR

This paper introduces a programmable scheduler for Open RAN that dynamically adjusts parameters to improve throughput guarantees and asset tracking accuracy in 5G IIoT scenarios, demonstrating significant performance gains.

Contribution

It presents a novel, programmable scheduler design and an O-RAN xApp that dynamically reconfigures scheduler weights based on throughput reports, enhancing RAN performance.

Findings

33% increase in meeting throughput requirements

Up to 37.04% improvement in asset detection accuracy

Effective control of 5G RAN via near-RT RIC

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%.