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

• URL: http://arxiv.org/abs/2505.10537v2
• Date: Tue, 27 May 2025 22:00:27 GMT
• Title: LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps
• Authors: Filippo Olimpieri, Noemi Giustini, Andrea Lacava, Salvatore D'Oro, Tommaso Melodia, Francesca Cuomo,
• Abstract summary: O-RANs are transforming cellular networks by adopting RAN softwarization and disaggregation concepts.<n>Such management is enabled by RICs, which facilitate near-real-time and non-real-time network control through xApps and rApps.<n>We leverage the dApps concept to enable real-time RF spectrum classification with LibIQ, a novel library for RF signals.
• Score: 15.696586190289947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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