ANTLab RESEARCH TOPICS

The research area focuses on advanced Mobile Radio Networks and next-generation cellular systems, addressing both theoretical and experimental challenges in 5G and 6G scenarios. Core activities include radio network planning and optimization, the design of efficient radio resource management (RRM) algorithms, and the study of innovative network architectures. Particular attention is devoted to emerging paradigms such as smart radio environments, where reconfigurable intelligent surfaces and programmable propagation are integrated into the network design. The research also investigates interference management techniques, including complex spectrum sharing scenarios involving heterogeneous networks and dynamic spectrum access.

A significant research thrust concerns data-driven network management. This includes the statistical analysis of network counters and Key Performance Indicators (KPIs), as well as the development of machine learning-based methods for performance prediction, anomaly detection, traffic forecasting, and self-optimization. AI-based approaches to network automation are explored to enable self-organizing and self-healing networks, reducing operational costs while improving reliability and quality of service. In parallel, the group studies digital twin-based solutions for network control and management, where virtual replicas of physical networks are used to test optimization strategies, evaluate what-if scenarios, and support real-time decision-making.

Methodologically, the research combines analytical modeling with extensive simulation and prototyping. Optimization theory and game-theoretic models are employed to design distributed and scalable algorithms for resource allocation, interference coordination, and spectrum sharing. System-level simulations are conducted using platforms such as NS-3 and Sionna, enabling performance evaluation under realistic conditions. Furthermore, developed solutions are validated through prototypical implementations using open-source Software Defined Radio (SDR) platforms, including OpenAirInterface and similar frameworks, allowing experimental assessment in controlled and real-world environments.

People involved

Antonio Capone, Ilario Filippini, Andrea Pimpinella, Paolo Fiore, Antonio Boiano, Viola Bernazzoli, Alberto Ceresoli, Marcello Morini, Chiara Rubaltelli.

The research area focuses on IoT systems and Wireless Internet technologies, with particular emphasis on Wi-Fi as a pervasive infrastructure for data collection, sensing, and protocol optimization.

With respect to IoT systems, the research spans all layers of the IoT protocol stack. At the application layer, activities concentrate on lightweight messaging protocols such as MQTT, MQTT-SN, and CoAP, addressing large-scale traffic analysis, protocol optimization, interoperability mechanisms, and distributed deployments. This includes the study of publish/subscribe architectures, advanced MQTT features, broker federation strategies, and systematic performance evaluation under realistic and large-scale workloads.

At the data link and connectivity layers, the research investigates short-range and long-range IoT technologies including Zigbee, Matter/Thread, BLE, LoRa/LoRaWAN, and NB-IoT. Activities include comparative performance evaluation, analytical modeling, scalability analysis, and optimization of communication strategies under dense and heterogeneous deployment scenarios.

In the Wi-Fi domain, the primary focus is the application of AI and machine learning techniques to extract high-level information from wireless traffic and physical-layer measurements. This includes localization, presence detection, and people counting through passive Wi-Fi monitoring, as well as learning-based mechanisms to enhance protocol performance, for example through reinforcement learning approaches for adaptive configuration and resource management.

A unifying theme across all activities is network data analysis at scale. The research addresses the design of optimized traffic analysis pipelines, including task-aware compression of network logs, scalable trace processing, and feature extraction mechanisms tailored to specific analytical or forensic objectives. Protocol enhancements are validated through both real-world experimentation and simulation campaigns, supported by embedded hardware programming, firmware development, and the deployment of large-scale experimental testbeds to ensure reproducibility and practical relevance.

The overall goal is to tightly integrate protocol engineering, wireless systems, and data-driven network intelligence, enabling scalable, interoperable, and analytically tractable IoT and Wireless Internet infrastructures.

People Involved:

Matteo Cesana, Alessandro E. C. Redondi, Marco Cominelli, Fabio Palmese, Antonio Boiano, Massimo Nobile.

The research area focuses on the design and implementation of advanced networked systems, addressing both architectural and systems-level challenges in modern datacenter, edge, and cloud environments. Core activities include the development of high-performance packet processing pipelines, the co-design of hardware and software abstractions, and the exploration of novel network stack architectures. Particular attention is devoted to full-stack innovation, where new hardware primitives for fast and programmable packet processing are tightly integrated with operating system mechanisms and runtime support. The group investigates how emerging technologies (such as SmartNICs, FPGA-accelerated network interfaces, and in-network acceleration) can be leveraged to improve performance, efficiency, and programmability across the entire system stack.

A significant research thrust concerns operating system and runtime support for next-generation networking. This includes the design of kernel extensions using eBPF for safe and dynamic programmability, as well as kernel-bypass techniques based on DPDK and AF_XDP to achieve low latency and high throughput. The group develops also advanced compiler techniques and programming abstractions that expose hardware acceleration capabilities to applications while preserving safety, portability, and ease of use. By bridging hardware capabilities with system software and application-level interfaces, the research aims to enable scalable, efficient, and flexible network services suitable for demanding workloads such as distributed systems, AI infrastructures, and high-performance cloud applications.

Methodologically, the research combines system design, prototyping, and experimental evaluation with analytical modeling and simulation. Experimental platforms include FPGA-accelerated NICs donated by AMD/Xilinx and SmartNICs featuring System-on-Chip accelerators from NVIDIA, which allow rapid prototyping and hardware/software co-design. Solutions are evaluated through detailed measurement campaigns and benchmarking under realistic workloads, complemented by simulation studies using established tools such as NS-3. This integrated experimental and analytical approach enables rigorous validation of new architectures and provides practical insights into the performance, scalability, and deployability of next-generation networked systems.

People Involved

Gianni Antichi, Filippo Carloni, Farbod Shahinfar, Marco Molè, Francesco Maria Tranquillo, Davide Palmiotti

The research area focuses on the security and resilience of modern and future communication infrastructures, addressing the evolving threat landscape in decentralized and high-speed environments. Core activities include the investigation of Zero-Knowledge proofs for authorizing end-to-end encrypted application traffic in the infrastracture layer and the usage of delegated middleboxes to detect anomalous call flows in Function-as-a-Service interactions.
Particular attention is devoted to the use cases relevant for critical sectors such as energy, transport, financial, and health. We also investigate technologies that help companies comply to regulations in those sectors when using edge computing and 5G public and private networks.

We use programmable-data-plane technologies for high-speed networks such as eBPF and NVIDIA Smart NICs, where we develop algorithms for network intrusion detection at wire speed.

Furthermore, the research explores the integration of Quantum Key Distribution (QKD) into existing application and network protocols to ensure long-term cryptographic sovereignty and secure communication against quantum threats.

People involved:

Giacomo Verticale, Davide Andreotti, Luca Giacometti, Samin Shokrivahed, Jialin Chen (陈佳琳) (visiting from Sichuan University)

Another major research area of the laboratory concerns Network Economics, with a specific focus on the structure, evolution, and value creation mechanisms of the telecommunications ecosystem. This line of research investigates how technological innovation, regulatory frameworks, and competitive dynamics jointly shape business models and investment incentives across the different layers of the ecosystem. Building on systemic approaches, the research adopts concepts such as value networks, multi-sided markets, and layered ecosystem models to move beyond traditional linear value chains and capture the complex interdependencies among infrastructure providers, service operators, digital platforms, vendors, and vertical industries. Particular attention is devoted to understanding how value is generated and distributed across layers, and why growth increasingly concentrates in enabling technologies (such as cloud computing, artificial intelligence, cybersecurity, and data analytics) rather than in traditional connectivity services.

Methodologically, this research combines quantitative market valuation, industrial economics, and strategic foresight techniques. A representative example is the work carried out within the RESTART programme, which developed a layered model of the European telecommunications ecosystem and provided a detailed estimation of its economic value and growth trajectories. By integrating statistical data, financial analysis, and scenario-building methodologies, the study explored multiple plausible futures up to 2040, assessing how alternative configurations could affect competition, investment capacity, technological sovereignty, and overall ecosystem value. Through scenario analysis and the identification of key strategic crossroads (e.g., infrastructure investment models, market consolidation, single market integration, and digital sovereignty initiatives), this research stream provides decision-support tools for policymakers and industry stakeholders, contributing to more sustainable, competitive, and innovation-driven telecommunications markets.

People involved:

This research area is in collaboration with the Digital Innovation Observatories team of the Management Engineering Department (Marta Valsecchi, Giovanni Miragliotta, Luca Dozio, Edoardo Meraviglia, Mattia Magnaghi, Claudio Conti, Luca Dell’Anna, Giulia Asquer). From the ANTLab team the person involved is Antonio Capone.