Controlled Radio Fault Injection and Telemetry Evaluation for Open 5G RANs

Abstract

Radio access network (RAN) telemetry reveals radio conditions, protocol behavior, and their consequences for applications. Evaluating its usefulness requires controlled impairments and observations of the same execution. We present C and Python channel brokers and a fault injection workflow for software 5G testbeds connecting an OCUDU base station and a software UE through ZeroMQ. The brokers apply scheduled, directional transformations to I/Q samples and restore the link, while the workflow jointly records applied faults, native and programmable (codelet) telemetry, and application delay. We demonstrate this capability through 45 comparative trials of brief uplink interruptions and stepped additive noise. These use cases distinguish recognizing a radio change from measuring its service consequence: native missing-feedback events retrospectively identify interruptions, whereas noise changes decoding-error measurements without producing those events. Median on-time round-trip times remain near 30 ms, but the prespecified comparisons show mean increases in on-time 95th-percentile delay of 354 and 604 ms for one and five interruptions, and 113 ms for noise. Adding receive-side radio link control (RLC) codelet summaries did not improve interruption classification on held-out trials. The framework provides a reusable way to evaluate measurement choices under known radio faults, before using them in monitoring or learning systems.