About

Tommaso Melodia is the William Lincoln Smith Professor of Electrical and Computer Engineering at Northeastern University. He received the 'Laurea' and Doctorate degrees in telecommunications engineering from the University of Rome 'La Sapienza' in 2001 and 2005, respectively, and earned his Ph.D. in electrical and computer engineering from Georgia Institute of Technology in 2007. Between 2007 and 2014, he served as an Assistant and then Associate Professor at SUNY Buffalo before joining Northeastern University in 2014. His research interests encompass modeling, optimization, and experimental evaluation of wireless networks, with applications including intra-body networks of implantable devices, tactical cognitive radio networks, multimedia sensor networks, underwater networks, and mobile cloud computing. Prof. Melodia serves on the editorial boards of several prominent journals such as IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, and Computer Networks (Elsevier). He has received numerous honors, including a National Science Foundation CAREER Award, and is recognized as a Fellow of the Association for Computing Machinery (ACM), IEEE Fellow, and a Fellow of the National Academy of Inventors (NAI). His contributions to open radio access network architectures and AI-native wireless networks have been acknowledged through his selection as an ACM Fellow and other prestigious awards.

Research topics

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

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 accessSenior author

  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

• StormShield: Fingerprint-Based Detection and Mitigation of RRC Signaling Storms in O-RAN 5G RANs

  ArXiv.org · 2026-05-13

  articleOpen access

  5G networks provide low-latency, high throughput, and massive connectivity, yet the control plane remains exposed to several security threats. Among the most common and impactful threats are Denial-of-Service (DoS) attacks, with Radio Resource Control (RRC) signaling storms being particularly effective and difficult to mitigate. In this attack, a malicious User Equipment (UE) aims to exhaust Next Generation Node Base (gNB) resources, preventing legitimate UEs from establishing a connection. Existing defenses are typically limited to detection, only evaluated through numerical simulations, and cannot discern between high-load network conditions and attacks. Most of them also assume static setups and do not take mobility into account. In this paper, we first evaluate the feasibility of the signaling storm attack by using the OpenAirInterface(OAI) 5G protocol stack. Then, we propose StormShield, a signaling storm attack detection and mitigation technique implemented as an xApp on an O-RAN Near-Real-Time (near-RT) RAN Intelligent Controller (RIC). It fingerprints and blocks Malicious UEs (MUEs) before gNB resources are exhausted. We prototyped our solution on an Over-The-Air (OTA) testbed with OAI, NVIDIA Aerial, and two different gNB setups. The first one leverages an USRP X410 Software-defined Radio (SDR) with 8.1 functional split; the second a commercial Foxconn Radio Unit (RU) with 7.2 functional split. Our experimental evaluation demonstrates that StormShield effectively prevents gNB resource exhaustion, identifying and blocking MUEs with an average detection accuracy of 97.6% within 106.5 ms from the beginning of the attack.

  PublisherOA PDFDOI

• StormShield: Fingerprint-Based Detection and Mitigation of RRC Signaling Storms in O-RAN 5G RANs

  arXiv (Cornell University) · 2026-05-13

  preprintOpen access

  5G networks provide low-latency, high throughput, and massive connectivity, yet the control plane remains exposed to several security threats. Among the most common and impactful threats are Denial-of-Service (DoS) attacks, with Radio Resource Control (RRC) signaling storms being particularly effective and difficult to mitigate. In this attack, a malicious User Equipment (UE) aims to exhaust Next Generation Node Base (gNB) resources, preventing legitimate UEs from establishing a connection. Existing defenses are typically limited to detection, only evaluated through numerical simulations, and cannot discern between high-load network conditions and attacks. Most of them also assume static setups and do not take mobility into account. In this paper, we first evaluate the feasibility of the signaling storm attack by using the OpenAirInterface(OAI) 5G protocol stack. Then, we propose StormShield, a signaling storm attack detection and mitigation technique implemented as an xApp on an O-RAN Near-Real-Time (near-RT) RAN Intelligent Controller (RIC). It fingerprints and blocks Malicious UEs (MUEs) before gNB resources are exhausted. We prototyped our solution on an Over-The-Air (OTA) testbed with OAI, NVIDIA Aerial, and two different gNB setups. The first one leverages an USRP X410 Software-defined Radio (SDR) with 8.1 functional split; the second a commercial Foxconn Radio Unit (RU) with 7.2 functional split. Our experimental evaluation demonstrates that StormShield effectively prevents gNB resource exhaustion, identifying and blocking MUEs with an average detection accuracy of 97.6% within 106.5 ms from the beginning of the attack.

  PublisherDOI

• xDevSM: An Open-Source Framework for Portable, AI-Ready xApps Across Heterogeneous O-RAN Deployments

  Open MIND · 2026-02-03

  preprintSenior author

  Openness and programmability in the O-RAN architecture enable closed-loop control of the Radio Access Network (RAN). Artificial Intelligence (AI)-driven xApps, in the near-real-time RAN Intelligent Controller (RIC), can learn from network data, anticipate future conditions, and dynamically adapt radio configurations. However, their development and adoption are hindered by the complexity of low-level RAN control and monitoring message models exposed over the O-RAN E2 interface, limited interoperability across heterogeneous RAN software stacks, and the lack of developer-friendly frameworks. In this paper, we introduce xDevSM, a framework that significantly lowers the barrier to xApp development by unifying observability and control in O-RAN deployment. By exposing a rich set of Key Performance Measurements (KPMs) and enabling fine-grained radio resource management controls, xDevSM provides the essential foundation for practical AI-driven xApps. We validate xDevSM on real-world testbeds, leveraging Commercial Off-the-Shelf (COTS) devices together with heterogeneous RAN hardware, including Universal Software Radio Peripheral (USRP)-based Software-defined Radios (SDRs) and Foxconn radio units, and show its seamless interoperability across multiple open-source RAN software stacks. Furthermore, we discuss and evaluate the capabilities of our framework through three O-RAN-based scenarios of high interest: (i) KPM-based monitoring of network performance, (ii) slice-level Physical Resource Block (PRB) allocation control across multiple User Equipments (UEs) and slices, and (iii) mobility-aware handover control, showing that xDevSM can implement intelligent closed-loop applications, laying the groundwork for learning-based optimization in heterogeneous RAN deployments. xDevSM is open source and available as foundational tool for the research community.

  DOI

• SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control

  Open MIND · 2026-01-29

  preprintSenior author

  Deep reinforcement learning (DRL) promises adaptive control for future mobile networks but conventional agents remain reactive: they act on past and current measurements and cannot leverage short-term forecasts of exogenous KPIs such as bandwidth. Augmenting agents with predictions can overcome this temporal myopia, yet uptake in networking is scarce because forecast-aware agents act as closed-boxes; operators cannot tell whether predictions guide decisions or justify the added complexity. We propose SIA, the first interpreter that exposes in real time how forecast-augmented DRL agents operate. SIA fuses Symbolic AI abstractions with per-KPI Knowledge Graphs to produce explanations, and includes a new Influence Score metric. SIA achieves sub-millisecond speed, over 200x faster than existing XAI methods. We evaluate SIA on three diverse networking use cases, uncovering hidden issues, including temporal misalignment in forecast integration and reward-design biases that trigger counter-productive policies. These insights enable targeted fixes: a redesigned agent achieves a 9% higher average bitrate in video streaming, and SIA's online Action-Refinement module improves RAN-slicing reward by 25% without retraining. By making anticipatory DRL transparent and tunable, SIA lowers the barrier to proactive control in next-generation mobile networks.

  DOI

• Predicting Conflict Impact on Performance in O-RAN

  Open MIND · 2026-03-09

  preprintSenior author

  The O-RAN Alliance promotes the integration of intelligent autonomous agents to control the Radio Access Network (RAN). This improves flexibility, performance, and observability in the RAN, but introduces new challenges, such as the detection and management of conflicts among the intelligent autonomous agents. A solution consists of profiling the agents before deployment to gather statistical information about their decision-making behavior, then using the information to estimate the level of conflict among agents with different goals. This approach enables determining the occurrence of conflicts among agents, but does not provide information about the impact on RAN performance, including potential service degradation. The problem becomes more complex when agents generate control actions at different timescales, which makes conflict severity hard to predict. In this paper, we present a novel approach that fills this gap. Our solution leverages the same data used to determine conflict severity but extends its use to predict the impact of such conflicts on RAN performance based on the frequency at which each agent generates actions, giving more weight to faster applications, which exert control more frequently. Via a prototype, we demonstrate that our solution is viable and accurately predicts conflict impact on RAN performance.

  DOI

• Performance Characterization of dApps in Open Radio Access Networks

  arXiv (Cornell University) · 2026-05-06

  preprintOpen access

  Despite recommendations to deploy real-time Open Radio Access Network (O-RAN) applications (dApps) in containerized environments, existing approaches predominantly rely on bare-metal servers. Moreover, current dApp deployments offer limited visibility into the resource usage patterns of both intelligent and non-intelligent dApps, hindering informed deployment decisions. This work addresses these gaps by implementing and evaluating representative dApps across multiple deployment scenarios (bare-metal and containers) to characterize the trade-offs in latency, scalability, and resource utilization. Additionally, we identify key performance bottlenecks and demonstrate how offloading dApps to emerging hardware accelerators, such as smart Network Interface Cards (NICs), can alleviate these limitations and improve real-time responsiveness in O-RAN systems.

  PublisherDOI

• RANalyzer: Automated Continuous RAN Software Evaluation and Regression Analysis

  ArXiv.org · 2026-04-25

  articleOpen access

  Software-driven O-RAN architectures enable rapid innovation through frequent, independent updates to virtualized components. However, attributing performance variations to specific software changes is challenging due to the stochastic nature of wireless systems, where channel conditions, interference, and hardware variability confound analysis. Traditional threshold-based monitoring and manual troubleshooting do not scale with modern software evolution. This paper presents RANalyzer, an automated test analysis framework that quantifies the performance impact of software updates beyond what can be explained by wireless channel conditions. RANalyzer combines LLM-assisted semantic extraction with residuals analysis. The first categorizes code changes by affected protocol layers and functional components, while the second provides insights on the effect of load, channel, or code changes on the test performance. We contribute an extensive dataset collected over more than two years of continuous over-the-air testing on an experimental O-RAN testbed, comprising over 8,600 automated tests across 69 releases of the OAI stack. By modeling expected performance and interpreting deviations as software-induced effects, we identify degraded instances attributable to code changes and correlate them with specific change categories. The framework can be integrated into CI/CD/CT pipelines for automated, continuous evaluation of software updates at scale.

  PublisherOA PDF

• ARCHES: Adaptive Real-Time Switching of AI Models for the RAN

  ArXiv.org · 2026-04-25

  articleOpen accessSenior author

  Artificial Intelligence (AI) has become a powerful tool for model-free Radio Access Network (RAN) signal processing and optimization. However, designing a single model that generalizes across all radio environments is challenging. Specialized AI models outperform conventional algorithms only under specific conditions, while their higher compute and energy cost makes unconditional execution impractical at the base station. This creates a need for real-time expert switching: dynamically activating the most appropriate AI or conventional expert based on current network conditions. To address this, we propose ARCHES (Adaptive Real-time CUDA Hot-swapping of Experts in the RAN Stack), a framework hosting multiple AI-based and conventional signal processing experts within a GPU-accelerated PHY pipeline, dynamically selecting the most appropriate expert at slot-boundary granularity without dropping or corrupting in-flight data. ARCHES includes a lightweight CUDA switch kernel for zero-gap output selection, a dApp-based control plane that collects cross-layer telemetry and drives the switching policy, and a reusable process for policy design based on controlled perturbation, monotonicity filtering, and hierarchical clustering. We validate ARCHES on UL channel estimation, switching between an AI-based and a Minimum Mean Square Error (MMSE) estimator under changing propagation and interference conditions. Implemented on the X5G platform with NVIDIA Aerial and OpenAirInterface (OAI), ARCHES achieves median UL PHY throughput gains of 5.32% and 7.23% under good and poor conditions, with a control-loop latency of ~140 us and sub-microsecond decision inference. Under good conditions, defaulting to MMSE saves 15.8 W of GPU power (9.6%) and 17 percentage points of GPU utilization versus unconditional AI execution, validating the performance-per-watt tradeoff that motivates adaptive expert selection.

  PublisherOA PDF

• BLINC: Context-Specific Causal Learning for Automated RAN Configuration

  arXiv (Cornell University) · 2026-04-29

  preprintOpen accessSenior author

  Radio Access Network (RAN) configuration has traditionally required significant manual effort due to indirect causal dependencies between observable Key Performance Indicators (KPIs), and context-dependent characteristics, where the optimal configurations vary with network conditions. Although recent data-driven approaches improve parameter tuning, they remain limited in distinguishing causal direction from statistical correlation and in generalizing across diverse operating contexts. To address these challenges, we propose BLINC (Bayesian Large Language Model (LLM)-Driven Intelligent Network Configuration), an LLM-assisted Bayesian Network framework that integrates telecommunications domain knowledge into causal structure learning. Trained and validated on a private 5G deployment, our method achieves throughput improvement of 63.5% with 19.7% reduction on block error rate over data-only baselines through joint optimization of power control and link adaptation parameters. The framework provides interpretable causal structure, while also quantifying prediction uncertainty. We also demonstrate the ability of the Bayesian Network framework to adapt to different deployment scenarios and propose an incremental Conditional Probability Distribution (CPD) update mechanism with learning rate for continuous model adaptation as network conditions evolve.

  PublisherDOI

Recent grants

• MRI: SEANet: Development of a Software-Defined Networking Testbed for the Internet of Underwater Things

  NSF · $1.1M · 2017–2021

• CAREER: Towards Ultrasonic Networking for Implantable Biomedical Devices

  NSF · $175k · 2013–2014

• NeTS: Small: Beyond Separate-Then-Centralize: A Cellular Operating System to Optimize Software-Defined 5G Wireless Networks

  NSF · $350k · 2016–2020

• CCRI: Grand: Colosseum: Opening and Expanding the World's Largest Wireless Network Emulator to the Wireless Networking Community

  NSF · $7.1M · 2019–2026

• CAREER: Towards Ultrasonic Networking for Implantable Biomedical Devices

  NSF · $427k · 2014–2019

Frequent coauthors

• Salvatore D’Oro

  113 shared

• Michele Polese

  93 shared

• Francesco Restuccia

  83 shared

• Leonardo Bonati

  Northeastern University

  75 shared

• Thomas Zajkowski

  University of Southern California

  64 shared

• Mihail L. Sichitiu

  64 shared

• Jinyang Li

  University of Maryland, College Park

  64 shared

• M. Hadi Amini

  64 shared

Labs

• Wireless Networks and Embedded Systems LaboratoryPI

Awards & honors

• Fellow of the Association for Computing Machinery (ACM) (202…
• COE Faculty Fellow
• COE Faculty Research Team Award
• IEEE Fellow
• IEEE Communications Society Distinguished Lecturer