Measurement-Driven O-RAN Diagnostics with Tail Latency and Scheduler Indicators
This work addresses practical monitoring and troubleshooting for O-RAN systems, though it is incremental as it builds on existing diagnostics with a new measurement-driven approach.
The study tackled cross-layer performance diagnostics in O-RAN by analyzing application-level latency and radio-layer behavior from real measurements, revealing UE-dependent differences in latency tails and systematic scaling with distance and payload.
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.