Education
Research Interests
Claudio is a Fulbright and José Castillejo visitor at Northeastern University (June-Sept 2022). He is currently a postdoctoral researcher at IMDEA Networks Institute, Madrid, Spain. He joined the the Wireless Networking Group (WNG) in December 2016 right after having obtained his Ph.D. degree at the University of Luxembourg. He obtained both B.Sc. and M.Sc. from Politecnico di Torino, Italy and was a visiting Ph.D. student at Clarkson University, NY, USA in 2016. Claudio has been awarded with the Spanish Juan de la Cierva grants (Formación and Incorporación) and the Best Paper Awards in IEEE Cloudnet 2016, in ACM WiNTECH 2018 and IEEE GLOBECOM 2019. He is member of IEEE and ACM, serves in the Technical Program Committee (TPC) of several international conferences and regularly participates in the organization of events (i.e., he serves as TPC Co-Chair at IEEE ICC 2023 and has hold the same role in IEEE CAMAD 2019 and 2021). Claudio is member of the Editorial Board team of IEEE Networking Letters and is the Chair of the IEEE ComSoc EMEA Awards Committee. His primary research interests include AI-driven mobile network optimization, multi-access edge/fog computing, and mobile crowdsensing.
Publications
While the availability of large datasets has been instrumental to advance fields like computer vision and natural language processing, this has not been the case in mobile networking. Indeed, mobile traffic data is often unavailable due to privacy or regulatory concerns. This problem becomes especially relevant in Open Radio Access Network (RAN), where artificial intelligence can potentially drive optimization and control of the RAN, but still lags behind due to the lack of training datasets. While substantial work has focused on developing testbeds that can accurately reflect production environments, the same level of effort has not been put into twinning the traffic that traverse such networks.To fill this gap, in this paper, we design a methodology to twin real-world cellular traffic traces in experimental Open RAN testbeds. We demonstrate our approach on the Colosseum Open RAN digital twin, and publicly release a large dataset (more than 500 hours and 450 GB) with PHY-, MAC-, and App-layer Key Performance Measurements (KPMs), and protocol stack logs. Our analysis shows that our dataset can be used to develop and evaluate a number of Open RAN use cases, including those with strict latency requirements.
LinkThe Open Radio Access Network (RAN) paradigm is transforming cellular networks into a system of disaggregated, virtualized, and software-based components. These self-optimize the network through programmable, closed-loop control, leveraging Artificial Intelligence (AI) and Machine Learning (ML) routines. In this context, Deep Reinforcement Learning (DRL) has shown great potential in addressing complex resource allocation problems. However, DRL-based solutions are inherently hard to explain, which hinders their deployment and use in practice. In this paper, we propose EXPLORA, a framework that provides explainability of DRL-based control solutions for the Open RAN ecosystem. EXPLORA synthesizes network-oriented explanations based on an attributed graph that produces a link between the actions taken by a DRL agent (i.e., the nodes of the graph) and the input state space (i.e., the attributes of each node). This novel approach allows EXPLORA to explain models by providing information on the wireless context in which the DRL agent operates. EXPLORA is also designed to be lightweight for real-time operation. We prototype EXPLORA and test it experimentally on an O-RAN-compliant near-real-time RIC deployed on the Colosseum wireless network emulator. We evaluate EXPLORA for agents trained for different purposes and showcase how it generates clear network-oriented explanations. We also show how explanations can be used to perform informative and targeted intent-based action steering and achieve median transmission bitrate improvements of 4% and tail improvements of 10%.
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