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The O-RAN architecture is transforming cellular networks by adopting RANsoftwarization and disaggregation concepts to enable data-driven monitoring andcontrol of the network. Such management is enabled by RICs, which facilitatenear-real-time and non-real-time network control through xApps and rApps.However, they face limitations, including latency overhead in data exchangebetween the RAN and RIC, restricting real-time monitoring, and the inability toaccess user plain data due to privacy and security constraints, hindering usecases like beamforming and spectrum classification. In this paper, we leveragethe dApps concept to enable real-time RF spectrum classification with LibIQ, anovel library for RF signals that facilitates efficient spectrum monitoring andsignal classification by providing functionalities to read I/Q samples astime-series, create datasets and visualize time-series data through plots andspectrograms. Thanks to LibIQ, I/Q samples can be efficiently processed todetect external RF signals, which are subsequently classified using a CNNinside the library. To achieve accurate spectrum analysis, we created anextensive dataset of time-series-based I/Q samples, representing distinctsignal types captured using a custom dApp running on a 5G deployment over theColosseum network emulator and an OTA testbed. We evaluate our model bydeploying LibIQ in heterogeneous scenarios with varying center frequencies,time windows, and external RF signals. In real-time analysis, the modelclassifies the processed I/Q samples, achieving an average accuracy ofapproximately 97.8\% in identifying signal types across all scenarios. Wepledge to release both LibIQ and the dataset created as a publicly availableframework upon acceptance.

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