AI summaryⓘ
The authors studied how delays happen in a wireless network called O-RAN by looking at both the app-side waiting times and the radio signal behavior together. They tested at different distances and with two types of devices, including a phone and a modem, using real measurements instead of simulations. Their analysis focused on occasional long delays rather than averages, linking these delays to radio conditions like errors and signal quality. They found that delays depend on the device, get longer with distance and data size, and sometimes radio changes show up even when overall delay seems normal. The authors suggest new simple monitoring tools combining delay and radio info to better detect and fix network problems.
O-RANlatencyradio linkmodulationschedulerblock error ratetail latencylink adaptationcross-layer analysisnetwork diagnostics
Authors
Theofanis P. Raptis, Weronika Maria Bachan, Roberto Verdone
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.