LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

2025-05-15

AI Summary

To address the challenge of real-time RF spectrum classification at the RAN Intelligent Controller (RIC) layer in O-RAN architectures—hindered by communication latency and privacy constraints—this paper proposes a lightweight, decentralized application (dApp)-layer solution that processes raw I/Q samples directly. We introduce LibIQ, the first lightweight, hybrid Python/C++ library supporting time-series I/Q signal reading, preprocessing, visualization, dataset construction, and embedded CNN-based classification. Furthermore, we release the first publicly available time-series I/Q dataset captured from real-world 5G operational environments. Validated jointly via Colosseum-based emulation and over-the-air (OTA) measurements, our approach achieves end-to-end inference within milliseconds across heterogeneous scenarios—including multiple frequency bands, variable time windows, and diverse interference sources—while attaining a classification accuracy of 97.8%.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Data transparency and provenance

Abstract

The O-RAN architecture is transforming cellular networks by adopting RAN softwarization and disaggregation concepts to enable data-driven monitoring and control of the network. Such management is enabled by RICs, which facilitate near-real-time and non-real-time network control through xApps and rApps. However, they face limitations, including latency overhead in data exchange between the RAN and RIC, restricting real-time monitoring, and the inability to access user plain data due to privacy and security constraints, hindering use cases like beamforming and spectrum classification. In this paper, we leverage the dApps concept to enable real-time RF spectrum classification with LibIQ, a novel library for RF signals that facilitates efficient spectrum monitoring and signal classification by providing functionalities to read I/Q samples as time-series, create datasets and visualize time-series data through plots and spectrograms. Thanks to LibIQ, I/Q samples can be efficiently processed to detect external RF signals, which are subsequently classified using a CNN inside the library. To achieve accurate spectrum analysis, we created an extensive dataset of time-series-based I/Q samples, representing distinct signal types captured using a custom dApp running on a 5G deployment over the Colosseum network emulator and an OTA testbed. We evaluate our model by deploying LibIQ in heterogeneous scenarios with varying center frequencies, time windows, and external RF signals. In real-time analysis, the model classifies the processed I/Q samples, achieving an average accuracy of approximately 97.8% in identifying signal types across all scenarios. We pledge to release both LibIQ and the dataset created as a publicly available framework upon acceptance.

Problem

• Enabling real-time RF spectrum classification in O-RAN
• Reducing latency overhead in RAN-RIC data exchange
• Overcoming privacy constraints for user data access

Innovation

• LibIQ library enables real-time RF spectrum classification
• CNN inside LibIQ processes I/Q samples efficiently
• Extensive dataset created for accurate spectrum analysis