Education
- Ph.D. in Computer Engineering (current) - Northeastern University, Boston, MA, USA
- M.Sc. student in Cybersecurity - Sapienza University of Rome (2025)
- B.Sc. in Computer and System Engineering - Sapienza University of Rome (2023)
Research Interests
- Wireless Networks
- Cybersecurity
- O-RAN Architecture
Filippo Olimpieri is a Ph.D. Student in Computer Engineering and Research Assistant at the Institute for Intelligent Networked Systems at Northeastern University, under Prof. Tommaso Melodia. He received a Bachelor’s degree in Computer and System Engineering from Sapienza University of Rome in 2023 and a Master’s degree in Cybersecurity in 2025 at the same institution. His research interests include wireless networks, with a particular focus on securing and optimizing Open Radio Access Networks (O-RAN). He is currently working with Openshift, OpenAirInterface, FlexRIC and OSC RIC, with a focus on the E2 and E3 interfaces.
Publications
2025
Conference Papers
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.
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