Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Theofanis P. Raptis, Weronika Maria Bachan, Roberto Verdone

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (16 sections, 12 figures, 5 tables)

This paper contains 16 sections, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dataset Overview
  4. Processing and Methodology
  5. Data consolidation
  6. Metrics
  7. Exploratory Results
  8. Latency distributions at 6 m (baseline)
  9. Packet-size and UE comparison (6 m baseline)
  10. Distance scaling and tail exceedance (baseline)
  11. Dynamic "people moving" test (smartphone, 6 m)
  12. Dynamic obstruction: phase-wise view
  13. Windowed cross-layer coupling and lightweight diagnostics
  14. Scheduler-side metrics (gNB fullstats) at 6 m
  15. Limitations and Future Potential
  16. ...and 1 more sections

Figures (12)

  • Figure 1: Latency CDF at 6 m, 30 B baseline: comparison between smartphone and modem-based UE.
  • Figure 2: Latency boxplot at 6 m baseline: 30 B vs 1000 B, smartphone vs modem-based UE.
  • Figure 3: Baseline 95th-percentile latency vs distance for 30 B packets (per UE where available).
  • Figure 4: Baseline 95th-percentile latency vs distance for 1000 B packets (per UE where available).
  • Figure 5: Baseline exceedance probabilities at 6 m (by UE and packet size) for 100 ms and 1 s thresholds.
  • ...and 7 more figures