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

TL;DR

This paper introduces LibIQ, a library enabling real-time spectrum classification in O-RAN networks by processing I/Q samples with CNNs, achieving high accuracy and supporting spectrum monitoring and control.

Contribution

LibIQ provides an efficient, real-time RF spectrum classification tool integrated with O-RAN, including a new dataset and CNN-based classification for spectrum management.

Findings

Achieved 97.8% accuracy in signal classification across scenarios

Developed an extensive I/Q dataset for RF signals

Demonstrated real-time spectrum analysis in 5G environments

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.