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

TL;DR

This paper presents a measurement-driven methodology for diagnosing O-RAN performance issues by analyzing tail latency and radio-layer indicators across different link distances and device configurations, enabling practical troubleshooting.

Contribution

It introduces a cross-layer diagnostic approach combining tail latency metrics with radio indicators, with lightweight degradation flags for real-time monitoring.

Findings

Tail latency varies significantly with device and distance.

Radio-layer dynamics can be detected even when end-to-end latency is stable.

Systematic scaling of tail latency with distance and payload.

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.