Measurement-Driven O-RAN Diagnostics with Tail Latency and Scheduler Indicators

2026-03-11

AI Summary

This work proposes a cross-layer, interpretable performance diagnosis method to address the challenge of detecting subtle radio-layer dynamic anomalies in O-RAN systems when end-to-end latency appears stable. Leveraging real-world measurements across multiple distances and user equipment (UE) types, the approach jointly analyzes application-layer tail latency—such as the 95th percentile—with radio-layer metrics including scheduling behavior, modulation and coding scheme (MCS), block error rate (BLER), and signal quality to construct lightweight “degradation flags.” The method enables non-intrusive yet effective detection of radio-layer performance degradation, revealing the sensitivity of tail latency to UE type, distance, and network load. This facilitates practical and efficient fault localization and monitoring in O-RAN deployments.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

We investigate cross-layer performance diagnostics for an O-RAN instance by jointly analyzing application-level latency and radio-layer behavior from a real measurement campaign. Measurements were conducted at multiple link distances (2, 6 and 11 meters) using two representative UE configurations (a commercial smartphone and a modem-based device), under both static conditions and a controlled dynamic obstruction scenario. Rather than relying on averages, the study adopts tail-focused latency characterization (e.g., 95th percentile and exceedance probabilities) and connects it to scheduler- and link-adaptation indicators (e.g., block error behavior, modulation/coding selection and signal quality). The results reveal (i) UE-dependent differences that primarily manifest in the latency tail, (ii) systematic scaling of tail latency with distance and payload and (iii) cases where radio-layer dynamics are detectable even when end-to-end latency appears stable, motivating the need for cross-layer evidence. Distinct from much of the existing literature (often centered on throughput, simulated setups, or single-layer KPIs) this work contributes a measurement-driven methodology for interpretable O-RAN diagnostics and proposes lightweight, window-based "degradation flags" that combine tail latency and radio indicators to support practical monitoring and troubleshooting.

Problem

• O-RAN diagnostics
• tail latency
• cross-layer performance
• scheduler indicators
• radio-layer dynamics

Innovation

• tail latency
• O-RAN diagnostics
• cross-layer analysis
• scheduler indicators
• measurement-driven