About

Salvatore D'Oro joined Northeastern University in 2017 as an Associate Research Professor within the Institute for the Wireless Internet of Things (WIoT). His research interests focus on Open RAN, network slicing and virtualization techniques for 5G systems and beyond, physical layer security, and artificial intelligence and machine learning for networking applications. He has contributed to the development of innovative technologies in wireless communications, including patents for Open RAN adapters, AI-powered control of drone networks, and methods for multi-access edge computing network slicing in 5G networks. His work has been recognized through multiple patents and his inclusion among the top 2% of most-cited scientists worldwide by Stanford University.

Selected publications

• A development and validation framework for AI/ML-driven rApps in open RAN: a case study on network energy saving

  Journal on Wireless Communications and Networking · 2026-03-14

  articleOpen access

  Open radio access network (RAN) leverages the RAN intelligent controller (RIC) to enable artificial intelligence/machine learning (AI/ML)-driven network automation. However, a gap remains between algorithmic research and deployable, standards-compliant rApp prototypes with verifiable behavior. This paper addresses this gap by introducing an integrated development and validation framework that supports the full lifecycle of AI/ML-based rApps, from prototyping to functional verification. The framework includes a standards-compliant non-real-time RIC (Non-RT RIC) architecture with supporting functions, an interface for integrating RAN simulators, and a visualization dashboard that displays system state and control actions, enabling traceability of end-to-end control loops. We demonstrate the framework through a case study involving the design and implementation of a predictive network energy saving rApp. In closed-loop experiments, instrumented logs and visualizations indicate that the control decisions of the rApp adhere to the intended operational logic, allowing repeatable functional validation. We also discuss challenges for real-world deployment and study limitations. Overall, the proposed framework provides a practical methodology and toolset that accelerate the transition from algorithmic concept to deployable, validated rApps, advancing reliable AI/ML solutions within the O-RAN ecosystem and offering direct applicability to energy saving as well as other O-RAN use cases.

  PublisherDOI

• Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges

  IEEE Open Journal of Vehicular Technology · 2025-01-01 · 5 citations

  articleOpen access

  The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially in the last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modeling the problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.

  PublisherDOI

• AutoPilot: Steering OFDM Pilots Away From Jamming Using Deep Learning

  2025-02-17

  article

  Pilot jamming attacks are among the most cost-effective, yet successful, attacks against wireless networks designed around fixed pilot locations, e.g., IEEE 802.11a/g/p. In this paper, we aim at improving the security and robustness of those systems against this class of attacks. Specifically, we present AutoPilot, a data-driven and reconfigurable transceiver designed to combat narrowband jamming attacks, with specific focus on pilot jamming. AutoPilot embeds a customized RF front-end to effectively demodulate waveforms transmitted using non-standard compliant pilot configurations as well as two data-driven logic units that are designed to (i) adapt via pseudo-randomization pilot configurations according to the current channel conditions and attack profiles; and (ii) detect rapidly and with high accuracy pilots non conforming to standard-defined pilot sequences. We introduce and describe AutoPilot design and eventually present a prototype that is evaluated via over-the-air experiments on an actual wireless testbed. Our results show that AutoPilot can detect pilots with an accuracy as high as 99% on over-the-air unseen data and can improve network throughput under jamming attacks by 4x at high jammer powers. Finally, by randomizing pilots location, AutoPilot prevents eavesdroppers from demodulating eavesdropped packets, thus providing an increased layer of security to the network.

  PublisherDOI

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

  arXiv (Cornell University) · 2025-05-15

  preprintOpen access

  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. We pledge to release both LibIQ and the dataset created as a publicly available framework upon acceptance.

  PublisherDOI

• 5G Aero: A Prototyping Platform for Evaluating Aerial 5G Communications

  ArXiv.org · 2025-06-10

  preprintOpen access

  The application of small-factor, 5G-enabled Unmanned Aerial Vehicles (UAVs) has recently gained significant interest in various aerial and Industry 4.0 applications. However, ensuring reliable, high-throughput, and low-latency 5G communication in aerial applications remains a critical and underexplored problem. This paper presents the 5th generation (5G) Aero, a compact UAV optimized for 5G connectivity, aimed at fulfilling stringent 3rd Generation Partnership Project (3GPP) requirements. We conduct a set of experiments in an indoor environment, evaluating the UAV's ability to establish high-throughput, low-latency communications in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions. Our findings demonstrate that the 5G Aero meets the required 3GPP standards for Command and Control (C2) packets latency in both LoS and NLoS, and video latency in LoS communications and it maintains acceptable latency levels for video transmission in NLoS conditions. Additionally, we show that the 5G module installed on the UAV introduces a negligible 1% decrease in flight time, showing that 5G technologies can be integrated into commercial off-the-shelf UAVs with minimal impact on battery lifetime. This paper contributes to the literature by demonstrating the practical capabilities of current 5G networks to support advanced UAV operations in telecommunications, offering insights into potential enhancements and optimizations for UAV performance in 5G networks

  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

• Intent-based Radio Scheduler for RAN Slicing: Learning to deal with different network scenarios

  arXiv (Cornell University) · 2025-01-01 · 1 citations

  preprintOpen access

  The future mobile network has the complex mission of distributing available radio resources among various applications with different requirements. The radio access network slicing enables the creation of different logical networks by isolating and using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources available among the slices to fulfill their service-level agreement (SLA) requirements, prioritizing critical slices while minimizing the number of intent violations. Moreover, ensuring that the RRS can deal with a high diversity of network scenarios is essential. Several recent papers present advances in machine learning-based RRS. However, the scenarios and slice variety are restricted, which inhibits solid conclusions about the generalization capabilities of the models after deployment in real networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a radio access network (RAN) slicing context. The proposed method protects high-priority slices when the available radio resources cannot fulfill all the slices. It uses transfer learning to reduce the number of training steps required. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines. The results show that by using transfer learning, the required number of training steps could be reduced by a factor of eight without hurting performance.

  PublisherOA PDFDOI

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

  Computer Networks · 2025-06-03 · 7 citations

  preprintOpen access
  PublisherOA PDFDOI

• CORMO-RAN: Lossless Migration of xApps in O-RAN

  ArXiv.org · 2025-06-24

  preprintOpen access

  Open Radio Access Network (RAN) is a key paradigm to attain unprecedented flexibility of the RAN via disaggregation and Artificial Intelligence (AI)-based applications called xApps. In dense areas with many active RAN nodes, compute resources are engineered to support potentially hundreds of xApps monitoring and controlling the RAN to achieve operator's intents. However, such resources might become underutilized during low-traffic periods, where most cells are sleeping and, given the reduced RAN complexity, only a few xApps are needed for its control. In this paper, we propose CORMO-RAN, a data-driven orchestrator that dynamically activates compute nodes based on xApp load to save energy, and performs lossless migration of xApps from nodes to be turned off to active ones while ensuring xApp availability during migration. CORMO-RAN tackles the trade-off among service availability, scalability, and energy consumption while (i) preserving xApps' internal state to prevent RAN performance degradation during migration; (ii) accounting for xApp diversity in state size and timing constraints; and (iii) implementing several migration strategies and providing guidelines on best strategies to use based on resource availability and requirements. We prototype CORMO-RAN as an rApp, and experimentally evaluate it on an O-RAN private 5G testbed hosted on a Red Hat OpenShift cluster with commercial radio units. Results demonstrate that CORMO-RAN is effective in minimizing energy consumption of the RAN Intelligent Controller (RIC) cluster, yielding up to 64% energy saving when compared to existing approaches.

  PublisherOA PDFDOI

• AutoRAN: Automated and Zero-Touch Open RAN Systems

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

  preprintOpen access

  [...] 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

Awards & honors

• Patent for Adapter Enables Use of Open RAN Technologies on L…
• Patent for AI-Powered Control of Drone Networks
• Patent for Automated Control of Drone Swarm Networks
• Patent for Improved Management of Cellular Networks
• Patent for Improving O-RAN Efficiency and Flexibility