About

Stefano Basagni holds a Ph.D. in electrical engineering from the University of Texas at Dallas and a Ph.D. in computer science from the University of Milano, Italy. He is currently a professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston, MA, and serves as the Associate Dean of the Global Engineering Campus in the Office of the Dean. Dr. Basagni is also a core faculty member of Northeastern's Institute for the Wireless Internet of Things. His research interests concern the research and implementation aspects of mobile networks and wireless communications systems, including wireless sensor networking for IoT (aerial, underwater, and terrestrial), network protocol design and performance evaluation, and the theoretical and practical aspects of distributed algorithms. He has published over twelve dozen highly cited, refereed technical papers and book chapters, with an h-index of 54, and is a co-editor of three books. Dr. Basagni has served as a guest editor for multiple international ACM/IEEE, Wiley, and Elsevier journals, and has held numerous roles in organizing and chairing international conferences related to wireless networks and mobility. He is recognized as a distinguished scientist of the ACM, a senior member of the IEEE, and a member of ASEE and CUR.

Research topics

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

Selected publications

• Special Issue: Extended Papers from the 16th International Conference on Underwater Networks & Systems “WUWNet 2022,” and Advances of the State-of-the-Art in Underwater Communications and Networking

  Computer Networks · 2025-06-10

  articleSenior authorCorresponding
  PublisherDOI

• AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

  ArXiv.org · 2025-10-28

  preprintOpen access

  Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

  PublisherOA PDFDOI

• An Open, Programmable, Multi-Vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface

  2024-05-20 · 29 citations

  article

  The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) layer performance, end-to-end integration and testing, and careful planning of the Radio Frequency (RF) environment. In this paper, we describe how the X5G testbed at Northeastern University has addressed these challenges through the first 8-node network deployment of the NVIDIA Aerial RAN CoLab (ARC), with the Aerial Software Development Kit (SDK) for the PHY layer, accelerated on Graphics Processing Unit (GPU), and through its integration with higher layers from the OpenAirInterface (OAI) open-source project through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). We discuss software integration, the 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, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink.

  PublisherDOI

• AI-Assisted Agile Propagation Modeling for Real-Time Digital Twin Wireless Networks

  2024-10-21 · 4 citations

  article

  Accurate channel modeling in real-time faces remarkable challenge due to the complexities of traditional methods such as ray tracing and field measurements. AI-based techniques have emerged to address these limitations, offering rapid, precise predictions of channel properties through ground truth data. This paper introduces an innovative approach to real-time, highfidelity propagation modeling through advanced deep learning. Our model integrates 3D geographical data and rough propagation estimates to generate precise path gain predictions. By positioning the transmitter centrally, we simplify the model and enhance its computational efficiency, making it amenable to larger scenarios. Our approach achieves a normalized Root Mean Squared Error of less than 0.035 dB over a 37,210 square meter area, processing in just 46 ms on a GPU and 183 ms on a CPU. This performance significantly surpasses traditional high-fidelity ray tracing methods, which require approximately three orders of magnitude more time. Additionally, the model's adaptability to real-world data highlights its potential to revolutionize wireless network design and optimization, through enabling real-time creation of adaptive digital twins of real-world wireless scenarios in dynamic environments.

  PublisherDOI

• On the Impact of Overcoming Wake-up Radio Limitations on the Performance of Energy Aware Routing in Wireless Sensor Networks

  2024-07-29

  articleSenior author

  The integration of Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) has significantly broadened the scope of interconnected devices, offering novel solutions and enhancing capabilities in monitoring and control across various sectors. Despite remarkable advancements, reliance on battery-powered wireless devices introduces significant challenges, primarily due to energy constraints that limit the operational lifespan of these networks. This paper addresses these challenges by exploring the efficacy of Wake-up Radio (WuR) technology as a means to enhance energy efficiency. WuR technology allows nodes to remain dormant until communication is necessary, thereby extending the network lifetime without compromising performance. However, limitations such as reduced communication range and data transmission rates pose obstacles to the full realization of WuR potential. Through simulation-based experiments, this study evaluates the performance of a novel protocol, Simple Energy Aware Routing (SEAR), under various WuR configurations, using the GreenCastalia simulator. Our findings demonstrate how optimizing WuR parameters can significantly impact key network performance metrics, suggesting pathways for future WuR technology development to achieve optimal WSN performance within the IoT paradigm. The insights provided aim to inform ongoing research efforts, contributing to the evolution of WSNs as a foundational element of the IoT infrastructure.

  PublisherDOI

• AI-assisted Agile Propagation Modeling for Real-time Digital Twin Wireless Networks

  arXiv (Cornell University) · 2024

  • Computer Science
  • Computer Science
  • Telecommunications

  Accurate channel modeling in real-time faces remarkable challenge due to the complexities of traditional methods such as ray tracing and field measurements. AI-based techniques have emerged to address these limitations, offering rapid, precise predictions of channel properties through ground truth data. This paper introduces an innovative approach to real-time, high-fidelity propagation modeling through advanced deep learning. Our model integrates 3D geographical data and rough propagation estimates to generate precise path gain predictions. By positioning the transmitter centrally, we simplify the model and enhance its computational efficiency, making it amenable to larger scenarios. Our approach achieves a normalized Root Mean Squared Error of less than 0.035 dB over a 37,210 square meter area, processing in just 46 ms on a GPU and 183 ms on a CPU. This performance significantly surpasses traditional high-fidelity ray tracing methods, which require approximately three orders of magnitude more time. Additionally, the model's adaptability to real-world data highlights its potential to revolutionize wireless network design and optimization, through enabling real-time creation of adaptive digital twins of real-world wireless scenarios in dynamic environments.

  PublisherOA PDFDOI

• Editorial special issue: Extended papers from the 18th wireless on-demand Network Systems and Services “WONS 2023” conference

  Computer Communications · 2024-10-23

  articleCorresponding
  PublisherDOI

• Design and performance evaluation of SEANet, a software-defined networking platform for the Internet of Underwater Things

  Computer Networks · 2024-06-12 · 6 citations

  preprintOpen access
  PublisherOA PDFDOI

• Experimental Evaluation of the Performance of UAV-assisted Data Collection for Wake-up Radio-enabled Wireless Networks

  2024-06-24 · 1 citations

  articleSenior author

  Wireless Sensor Networks (WSNs) are pivotal in various applications, including precision agriculture, ecological surveillance, and the Internet of Things (IoT). However, energy limitations of battery-powered nodes are a critical challenge, necessitating optimization of energy efficiency for maximal network lifetime. Existing strategies like duty cycling and Wake-up Radio (WuR) technology have been employed to mitigate energy consumption and latency, but they present challenges in scenarios with sparse deployments and short communication ranges. This paper introduces and evaluates the performance of Unmanned Aerial Vehicle (UAV)-assisted mobile data collection for WuR-enabled WSNs through physical and simulated experiments. We propose two one-hop UAV-based data collection strategies: a naïve strategy, which follows a predetermined fixed path, and an adaptive strategy, which optimizes the collection route based on recorded metadata. Our evaluation includes multiple experiment categories, measuring collection reliability, collection cycle duration, successful data collection time (latency), and node awake time to infer network lifetime. Results indicate that the adaptive strategy outperforms the naïve strategy across all metrics. Furthermore, WuR-based scenarios demonstrate lower latency and considerably lower node awake time compared to duty cycle-based scenarios, leading to several orders of magnitude longer network lifetime. Remarkably, our results suggest that the use of WuR technology alone achieves unprecedented network lifetimes, regardless of whether data collection paths are optimized. This underscores the significance of WuR as the technology of choice for all energy critical WSN applications.

  PublisherDOI

• Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges

  IEEE Communications Surveys & Tutorials · 2023 · 855 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes virtualized RANs where disaggregated components are connected via open interfaces and optimized by intelligent controllers. The result is a new paradigm for the RAN design, deployment, and operations: O-RAN networks can be built with multivendor, interoperable components, and can be programmatically optimized through a centralized abstraction layer and datadriven closed-loop control. Therefore, understanding O-RAN, its architecture, its interfaces, and workflows is key for researchers and practitioners in the wireless community. In this article, we present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early research results. We provide a deep dive of the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges of O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, security, and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Modeling Networks with Multiple Physical Layers-The Case for Multi-Radio Networks

  NSF · $175k · 2006–2010

• SGER: Small Antennas for Angle of Arrival Determination and Accurate Localization

  NSF · $59k · 2007–2009

• MRI: Development of the Northeastern University Marine Observatory NETwork (NU MONET)

  NSF · $448k · 2014–2018

Frequent coauthors

• Chiara Petrioli

  Sapienza University of Rome

  63 shared

• Tommaso Melodia

  50 shared

• Salvatore D’Oro

  24 shared

• Leonardo Bonati

  Northeastern University

  24 shared

• Michele Polese

  21 shared

• Kaushik Chowdhury

  Boston University

  15 shared

• Imrich Chlamtac

  KPR Institute of Engineering and Technology

  14 shared

• Miead Tehrani Moayyed

  Northeastern University

  13 shared

Labs

• Northeastern University College of Engineering Wireless Internet of Things LabPI

Awards & honors

• 2017 Faculty Research Team Award
• 2015 ACM Distinguished Scientist