# Demonstrating Real-Time RAN Observability in Open-Source 5G: Controlled Interference Scenario

Yassir Amami, Ziyad Mabrouk, Chadi Barakat, Thierry Turletti

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Yassir Amami, Ziyad Mabrouk, Chadi Barakat, Thierry Turletti. Demonstrating Real-Time RAN Observability in Open-Source 5G: Controlled Interference Scenario. ICIN 2026 - 29th Conference on Innovation in Clouds, Internet and Networks, Mar 2026, Athènes, Greece. hal-05527226

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Submitted on 25 Feb 2026

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# Demonstrating Real-Time RAN Observability in Open-Source 5G: Controlled Interference Scenario

Yassir Amami, Ziyad Mabrouk, Chadi Barakat, Thierry Turletti

Inria, Université Côte d'Azur, France

{yassir.amami, ziyad.mabrouk, chadi.barakat, thierry.turletti}@inria.fr

***Abstract***—Diagnosing performance degradations in 5G networks remains challenging due to the limited visibility of internal RAN behavior in open-source deployments, where radio impairments, such as interference, directly affect performance and are difficult to observe. This demo presents a real-time RAN observability framework for open-source 5G systems and demonstrates its effectiveness under controlled radio interference. The solution extends Monarch's existing cloud-native monitoring stack with a non-intrusive, Prometheus-compatible telemetry pipeline for the OAI gNB, exposing fine-grained, per-UE metrics from the physical, MAC, and RRC layers and visualizing them live in Grafana. The system is deployed automatically using an Ansible-based framework and validated on a controlled testbed with real radio hardware, where interference-induced effects can be observed and correlated in real time, enabling rapid and practical diagnosis of RAN performance degradation.

***Index Terms***—5G, RAN, Real-time monitoring, OpenAirInterface, Monarch, Prometheus.

## I. INTRODUCTION

Monitoring 5G networks is essential for identifying and diagnosing performance degradations, yet their observability remains uneven across network domains. While core network monitoring has matured, the RAN (Radio Access Network) remains largely opaque in open-source research and experimental deployments, despite being the origin of many performance impairments. The gNB performs critical functions such as scheduling, link adaptation, and resource allocation, but its internal behavior is rarely exposed through fine-grained, real-time and interpretable metrics, making radio-induced degradations difficult to diagnose using core-level indicators alone.

This demo addresses this gap by enabling real-time observability of gNB behavior in open-source 5G systems. We present a RAN monitoring pipeline that extends the Monarch framework [1], originally designed for slice-aware core monitoring, to the RAN while preserving its cloud-native architecture and automation. gNB metrics are extracted non-intrusively from OpenAirInterface (OAI)'s (OpenAirInterface) [2] local logs, and exported through a Prometheus-compatible pipeline, with live visualization in Grafana, providing immediate insight into low-layer gNB dynamics. Deployment of our pipeline is fully automated using an Ansible-based framework to ensure reproducibility on the experimental testbed.

To showcase the practical value of real-time RAN observability for experimental 5G analysis, this demo features a

live experiment with controlled radio interference on a gNB deployed with real radio hardware. As interference is introduced, the dashboard reveals immediate RAN-level reactions that remain largely invisible at the core level.

## II. RAN MONITORING PIPELINE

The Monarch framework [1] was originally developed to provide slice-aware monitoring in dynamic, cloud-native 5G deployments, unlike traditional monitoring tools. It integrates with container orchestration platforms such as Kubernetes while relying on Prometheus to scrape metrics exposed by network functions through Kubernetes Services (called MDEs). However, as a proof-of-concept, Monarch supports only slice-level throughput based on OPEN5GS, using UERANSIM for RAN simulation. In this work, we extend the Monarch monitoring framework with RAN-level observability using OAI [2] gNB and real wireless and User Equipment (UE), thus enabling real-time visualization of realistic per-UE metrics from the gNB's internal operations.

<figure><img src="image_2.png" /><figcaption>Fig. 1: OAI RAN Monitoring Architecture.</figcaption></figure>

Our monitoring pipeline focuses on metrics from the physical, MAC, and Radio Resource Control (RRC) layers. These metrics are available in the OAI gNB's runtime logs but are not exposed in a structured or queryable format. To address this, we developed a lightweight Prometheus-compatible exporter in Python that parses dedicated OAI statistic logs and converts selected entries into labeled time-series that can be scraped by

<footnote>This work was supported by the ANR under the France 2030 program, grant "NF-NAI: ANR-22-PEFT-0003".</footnote>
Prometheus. This approach avoids intrusive modifications to
the gNB while enabling live observation of RAN behavior.

As shown in Fig. 1, our exporter relies on three dedicated OAI runtime log files: nrMAC_stats.log, nrL1_stats.log, and nrRRC_stats.log. Each file is updated each second by the gNB, with new entries overwriting previous ones, keeping log sizes bounded and ensuring stable metric extraction. Since these logs are written independently of the gNB's global logging configuration, changes in verbosity or debug settings do not affect the extracted metrics.

The exported metrics include per-UE MAC throughput, signal quality indicators (Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-Noise Ratio (SNR)), Hybrid Automatic Repeat reQuest (HARQ) retransmissions, BLock Error Rate (BLER), and Modulation and Coding Scheme (MCS) values. To enable temporal and cross-UE comparisons, each metric is tagged with the Radio Network Temporary Identifier (RNTI) assigned by the gNB to each connected UE. These metrics are collected and visualized in real time using Grafana, allowing immediate insight into gNB behavior.

The entire setup is deployed using our open-source ANSIBLE-based automation framework<fnref n="1" />, ensuring reproducibility and consistent configuration across the different runs. This demo highlights one of the use-cases of the broader 5G experimentation and monitoring pipeline introduced in our original work [3], which was designed to facilitate rapid and repeatable instantiation of different cloud-native 5G cores and RAN implementations, while integrating a unified monitoring stack that exposes both core and RAN-level metrics.

### III. EXPERIMENTAL SCENARIO

This demo illustrates the capabilities of our RAN monitor-
ing pipeline through a controlled interference scenario. The
objective is not to benchmark network performance but to
demonstrate how real-time per-UE metrics extracted directly
from the gNB can reveal radio impairments that remain hidden
at the core network level. Fig. 2 illustrates our experimental
setup, deployed using our automation framework, and span-
ning three physical servers in the SLICES-RI [4] infrastructure
(sopnode-f1, sopnode-f2, and sopnode-f3).

We use an OAI gNB deployed with AW2S JAGUAR 2T2R Radio Unit (RU) operating with a narrow configuration<fnref n="2" /> of 1x1 Single Input and Single Output (SISO) and 51 Physical Resource Blocks (PRBs), equivalent to ≈ 20 MHz frequency range (from 3401.22 MHz to 3419.52 MHz). Then, we connect a single UE (referred to as qhat01), composed of a RASPBERRY PI 4 with a QUECTEL RM500Q-GL 5G module, and run a continuous uplink and downlink TCP traffic flow generated with IPERF to maintain steady radio activity throughout the demo. Our Gaussian noise generator is composed of a USRP N320 software radio device running the GNURadio uhd_siggen utility. Spectrum visualization is handled using

<figure><img src="image_3.png" /><figcaption>Fig. 2: Experimental setup for controlled interference scenario.</figcaption></figure>

a USRP B210 running the GNURadio `uhd tool. After an initial stable period with no exogenous noise, a 2 MHz-wide Gaussian noise signal is generated at a moderate gain of 40 dB. The interfering noise occupies the lower part of the operating band and overlaps with: $\frac{2 MHz}{12 \times 30 kHz} \approx 5.6$ PRBs, where 12 is the number of sub-carriers per PRB, and 30 kHz is the sub-carrier spacing.

<figure><img src="image_4.png" /><figcaption>Fig. 3: Spectrum visualization during interference.</figcaption></figure>

Fig. 3 shows the spectrum view during interference, where the affected frequency range appears as a localized, 2 MHz-wide increase in power from 3401.22 MHz to 3403.22 MHz as expected. At the same time, RAN metrics extracted from the gNB are visualized live using a Grafana dashboard. The live dashboard shown in Fig. 4 highlights the contrast between core-level and gNB-level observability metrics. The new RAN Stats section we added to the existing Monarch dashboard displays per-UE metrics alongside raw gNB measurements.

When the interference is activated, several RAN-level metrics exhibit immediate and clearly visible changes. The Physical Random Access Channel (PRACH) $I_0$, which reflects the total received interference plus background noise during random access, increases sharply from approximately 15 dB to 32 dB, indicating a significant rise in uplink noise power. At the same time, the SNR per RNTI becomes noticeably more unstable, with increased variance and peaks reaching up to 33 dB. In contrast, UE-reported indicators such as RSRP, RSRQ, and Signal-to-Interference-plus-Noise Ratio (SINR) remain largely unchanged. A likely explanation is that the UE cannot distinguish the desired signal from Gaussian noise and

<footnote n="1">¹Code available at: https://github.com/yassir63/5g_ansible</footnote>

<footnote n="2">²gNB configuration available at: https://github.com/yassir63/oai5g-rru/blob/gen-cn2/ran-config/conf/gnb.sa.band78.51prb.aw2s.ddsuu.20MHz.conf</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 4: Live Grafana dashboard in presence of interference.</figcaption></figure>

therefore reports the aggregate received power instead. The PRB $I_0$ noise deviation metric provides fine-grained localization of the interference, clearly identifying the affected PRBs through high deviation of positive values (shown in green), while unaffected PRBs remain within -20 dB to -9 dB of the average noise deviation from the central interference value of -44 dB for PRB 25 (shown in red), to which all other PRBs are compared.

On the uplink side of the dashboard (right side of Fig. 4), the impact of interference is immediately visible through adaptive link-layer behavior. The *MCS per RNTI* decreases from approximately 16 to 14, corresponding to a reduction in modulation order from $Q_m = 4$ to $Q_m = 2$ in accordance with 3GPP TS 38.214. This adaptation is accompanied by a drop in *MAC throughput* from around 25 Mbps to 20 Mbps and a marked increase in *HARQ errors*, reflecting more frequent decoding failures.

On the downlink side of the dashboard (left side of Fig. 4), most metrics, including *HARQ errors* and *MCS*, remain largely unaffected, indicating stronger resilience to interference. The only visible impact is a slight increase in *BLER*, from 0% to approximately 0.02%, which corresponds to a modest throughput reduction of about 1 Mbps. The observed uplink-downlink asymmetry illustrates the higher vulnerability of the uplink in our narrow configuration, where the UE's limited transmit power makes it more sensitive to interference than the gNB's downlink transmission.

This scenario demonstrates how per-UE gNB metrics provide immediate insight into radio impairments, showing the direct cause-and-effect relationship between localized interference and the RAN's adaptation protocols.

## IV. CONCLUSION

With this demo, we showcase a real-time RAN monitoring pipeline for open-source 5G systems that provides fine-grained visibility into gNB behavior and enables immediate diagnosis of radio performance issues. By extending Monarch with per-UE gNB metrics, the demo illustrates how localized interference can be detected and interpreted in real time, complementing traditional core-level observability. Validation on a realistic testbed with real radio hardware confirms the framework's ability to deliver accurate and actionable insights into RAN behavior under controlled conditions, demonstrating its value as a research-oriented tool.

## REFERENCES

[1] Niloy Saha, Nashid Shahriar, et al. Monarch: Monitoring Architecture for 5G and Beyond Network Slices. *IEEE TNSM journal*, 22(1):777-790, October 2024.

[2] Florian Kaltenberger, Raymond Knopp, et al. OpenAirInterface: Democratizing Wireless Innovation. In *Proceedings of the ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH)*, pages 33-40, 2020.

[3] Yassir Amami, Ziyad Mabrouk, et al. Toward real-time ran observability in open-source 5g systems. In *The 29th Conference on Innovation in Clouds, Internet and Networks (ICIN 2026)*, 2026.

[4] SLICES-RI | Scientific LargeScale Infrastructure for Computing/Communication Experimental Studies. https://www.slices-ri.eu/[Accessed 22-01-2026].