About

I am a Research Assistant Professor at the Institute for Intelligent Networked Systems (INSI), Northeastern University, Boston.

Research topics

• Computer Science
• Artificial Intelligence
• Telecommunications
• Computer network
• Operating system
• Distributed computing
• World Wide Web
• Computer Security
• Computer architecture
• Software engineering
• Embedded system
• Database
• Computer hardware

Selected publications

• Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN

  arXiv (Cornell University) · 2026-01-05

  preprintOpen access

  The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced and a tutorial for energy efficiency testing in ns-O-RAN.

  PublisherDOI

• dApps: Enabling real-time AI-based Open RAN control

  Computer Networks · 2025-06-03 · 7 citations

  preprintOpen access
  PublisherOA PDFDOI

• On AI Verification in Open RAN

  ArXiv.org · 2025-10-21

  preprintOpen access

  Open RAN introduces a flexible, cloud-based architecture for the Radio Access Network (RAN), enabling Artificial Intelligence (AI)/Machine Learning (ML)-driven automation across heterogeneous, multi-vendor deployments. While EXplainable Artificial Intelligence (XAI) helps mitigate the opacity of AI models, explainability alone does not guarantee reliable network operations. In this article, we propose a lightweight verification approach based on interpretable models to validate the behavior of Deep Reinforcement Learning (DRL) agents for RAN slicing and scheduling in Open RAN. Specifically, we use Decision Tree (DT)-based verifiers to perform near-real-time consistency checks at runtime, which would be otherwise unfeasible with computationally expensive state-of-the-art verifiers. We analyze the landscape of XAI and AI verification, propose a scalable architectural integration, and demonstrate feasibility with a DT-based slice-verifier. We also outline future challenges to ensure trustworthy AI adoption in Open RAN.

  PublisherOA PDFDOI

• AutoRAN: Automated and Zero-Touch Open RAN Systems

  ArXiv.org · 2025-04-15 · 1 citations

  preprintOpen accessSenior author

  [...] This paper presents AutoRAN, an automated, intent-driven framework for zero-touch provisioning of open, programmable cellular networks. Leveraging cloud-native principles, AutoRAN employs virtualization, declarative infrastructure-as-code templates, and disaggregated micro-services to abstract physical resources and protocol stacks. Its orchestration engine integrates Language Models (LLMs) to translate high-level intents into machine-readable configurations, enabling closed-loop control via telemetry-driven observability. Implemented on a multi-architecture OpenShift cluster with heterogeneous compute (x86/ARM CPUs, NVIDIA GPUs) and multi-vendor Radio Access Network (RAN) hardware (Foxconn, NI), AutoRAN automates deployment of O-RAN-compliant stacks-including OpenAirInterface, NVIDIA ARC RAN, Open5GS core, and O-RAN Software Community (OSC) RIC components-using CI/CD pipelines. Experimental results demonstrate that AutoRAN is capable of deploying an end-to-end Private 5G network in less than 60 seconds with 1.6 Gbps throughput, validating its ability to streamline configuration, accelerate testing, and reduce manual intervention with similar performance than non cloud-based implementations. With its novel LLM-assisted intent translation mechanism, and performance-optimized automation workflow for multi-vendor environments, AutoRAN has the potential of advancing the robustness of next-generation cellular supply chains through reproducible, intent-based provisioning across public and private deployments.

  PublisherOA PDFDOI

• InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps

  ArXiv.org · 2025-07-31

  preprintOpen access

  Ultra-dense fifth generation (5G) and beyond networks leverage spectrum sharing and frequency reuse to enhance throughput, but face unpredictable in-band uplink (UL) interference challenges that significantly degrade Signal to Interference plus Noise Ratio (SINR) at affected Next Generation Node Bases (gNBs). This is particularly problematic at cell edges, where overlapping regions force User Equipments (UEs) to increase transmit power, and in directional millimeter wave systems, where beamforming sidelobes can create unexpected interference. The resulting signal degradation disrupts protocol operations, including scheduling and resource allocation, by distorting quality indicators like Reference Signal Received Power (RSRP) and Received Signal Strength Indicator (RSSI), and can compromise critical functions such as channel state reporting and Hybrid Automatic Repeat Request (HARQ) acknowledgments. To address this problem, this article introduces InterfO-RAN, a real-time programmable solution that leverages a Convolutional Neural Network (CNN) to process In-phase and Quadrature (I/Q) samples in the gNB physical layer, detecting in-band interference with accuracy exceeding 91% in under 650 us. InterfO-RAN represents the first O-RAN dApp accelerated on Graphics Processing Unit (GPU), coexisting with the 5G NR physical layer processing of NVIDIA Aerial. Deployed in an end-to-end private 5G network with commercial Radio Units (RUs) and smartphones, our solution was trained and tested on more than 7 million NR UL slots collected from real-world environments, demonstrating robust interference detection capabilities essential for maintaining network performance in dense deployments.

  PublisherOA PDFDOI

• Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN

  ArXiv.org · 2025-09-05

  preprintOpen access

  As networks evolve towards 6G, Mobile Network Operators (MNOs) must accommodate diverse requirements and at the same time manage rising energy consumption. Integrated Access and Backhaul (IAB) networks facilitate dense cellular deployments with reduced infrastructure complexity. However, the multi-hop wireless backhauling in IAB networks necessitates proper routing and resource allocation decisions to meet the performance requirements. At the same time, cell densification makes energy optimization crucial. This paper addresses the joint optimization of routing and resource allocation in IAB networks through two distinct objectives: energy minimization and throughput maximization. We develop a novel capacity model that links power levels to achievable data rates. We propose two practical large-scale approaches to solve the optimization problems and leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to integrate the solutions. The approaches are evaluated on diverse scenarios built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed approaches effectively reduces number of activated nodes to save energy and achieves approximately 100 Mbps of minimum data rate per User Equipment (UE) during peak hours of the day using spectrum within the Frequency Range (FR) 3, or upper midband. The results validate the practical applicability of our framework for next-generation IAB network deployment and optimization.

  PublisherOA PDFDOI

• InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps

  2025-10-23

  articleOpen access

  Ultra-dense fifth generation (5G) and beyond networks leverage spectrum sharing and frequency reuse to enhance throughput, but face unpredictable in-band uplink (UL) interference challenges that significantly degrade Signal to Interference plus Noise Ratio (SINR) at affected Next Generation Node Bases (gNBs). This is particularly problematic at cell edges, where overlapping regions force User Equipments (UEs) to increase transmit power, and in directional millimeter wave systems, where beamforming sidelobes can create unexpected interference. The resulting signal degradation disrupts protocol operations, including scheduling and resource allocation, by distorting quality indicators like Reference Signal Received Power (RSRP) and Received Signal Strength Indicator (RSSI), and can compromise critical functions such as channel state reporting and Hybrid Automatic Repeat Request (HARQ) acknowledgments.

  PublisherOA PDFDOI

• The Future of Wireless Broadband in the Peak Smartphone Era: 6G, Wi-Fi 7, and Wi-Fi 8

  IEEE Wireless Communications · 2025-04-21 · 6 citations

  article

  The field of wireless communications has traditionally been defined by what seems like unending exponential traffic growth. History suggests this trend is unlikely to continue in perpetuity, at least with the current set of applications, with recent evidence pointing to moderating traffic growth. In this article, we evaluate the implications of the peak smartphone era for those designing wireless networks within the context of the next-generation of wireless broadband technologies. First, three potential future demand scenarios are identified, ranging from a return to exponential traffic growth (optimistic) to continued moderation in growth (realistic) and even a scenario of declining traffic (pessimistic). Second, we compare the emerging properties of the 6th generation of cellular technology ("6G") envisioned by IMT2030 and two new Wi-Fi standards, including IEEE 802.11be ("Wi-Fi 7") and IEEE 802.11bn ("Wi-Fi 8"). Finally, an alternative vision for the future of wireless broadband is proposed, focusing on enhanced coverage, reduced deployment costs, and improved energy efficiency. Four key recommendations include use of neutral hosts for superior indoor coverage, ensuring spectrum sharing and intelligent handover/roaming integration between cellular, Wi-Fi, and Non-Terrestrial Networks (NTNs), providing strong support for infrastructure sharing and national roaming in rural and remote areas, and efficient (re)organizing of existing spectrum allocations.

  PublisherDOI

• X5G: An Open, Programmable, Multi-Vendor, End-to-End, Private 5G O-RAN Testbed With NVIDIA ARC and OpenAirInterface

  IEEE Transactions on Mobile Computing · 2025-06-18 · 5 citations

  article

  As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) nearreal-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

  PublisherDOI

• Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non-Terrestrial Networks in 6G

  IEEE Communications Magazine · 2025-12-19 · 3 citations

  article

  Satellite networks are rapidly evolving, yet most Non-Terrestrial Networks (NTNs) remain isolated from terrestrial orchestration frameworks. Their control architectures are typically monolithic and static, limiting their adaptability to dynamic traffic, topology changes, and mission requirements. These constraints lead to inefficient spectrum use and underutilized network capacity. Although Artificial Intelligence (AI) promises automation, its deployment in orbit is limited by computing, energy, and connectivity limitations. This article introduces Space-O-RAN, a distributed control architecture that extends Open RAN principles into satellite constellations through hierarchical, closed-loop control. Lightweight dApps operate onboard satellites, enabling real-time functions like scheduling and beam steering without relying on continuous ground access. Cluster-level coordination is managed via Space RAN Intelligent Controllers (Space-RICs), which leverage low-latency Inter-Satellite Links (ISLs) for autonomous decisions in orbit. Strategic tasks, including AI training and policy updates, are transferred to terrestrial platforms Service Management and Orchestrations (SMO) using digital twins and feeder links. A key enabler is the dynamic mapping of the O-RAN interfaces to satellite links, supporting adaptive signaling under varying conditions. Simulations using the Starlink topology validate the latency bounds that inform this architectural split, demonstrating both feasibility and scalability for autonomous satellite RAN operations.

  PublisherDOI

Frequent coauthors

• Michele Zorzi

  University of Padua

  132 shared

• Tommaso Melodia

  93 shared

• Leonardo Bonati

  Northeastern University

  56 shared

• Salvatore D’Oro

  45 shared

• Matteo Pagin

  44 shared

• Francesco Agostini

  38 shared

• Arcangela Rago

  36 shared

• Giuseppe Piro

  36 shared

Education

• Ph.D. Information Engineering, Department of Information Engineering

  Università degli Studi di Padova

  2020

• Visiting Academic, NYU Wireless

  NYU Polytechnic School of Engineering

  2017

• M.Sc. Telecommunication Engineering, Department of Information Engineering

  Università degli Studi di Padova

  2016

• B.Sc. Information Engineering, Department of Information Engineering

  Università degli Studi di Padova

  2014

Awards & honors

• 2025: Elected chair of the AI-RAN Alliance AI-and-RAN Workin…
• 2024: Best Paper Award at CNSM 2024, Best Short Paper Award…
• 05/2025: Press on O-RAN digital twins on LightReading. News…
• 08/2023: New projects - Co-PI on NTIA TENORAN and NSF SWIFT-…