{"id":722,"date":"2026-08-25T08:27:45","date_gmt":"2026-08-25T08:27:45","guid":{"rendered":"https:\/\/nof.dnac.org\/2026\/?page_id=722"},"modified":"2026-08-25T08:54:35","modified_gmt":"2026-08-25T08:54:35","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/nof.dnac.org\/2026\/tutorials\/","title":{"rendered":"Tutorials"},"content":{"rendered":"

Tutorials\u00a0<\/strong><\/span><\/h2>\n

Tutorial #1<\/strong><\/h3>\n

Title:<\/strong> Beyond Connectivity:<\/span> SD-WAN for Edge Network Automation and Decision Intelligence<\/strong><\/em><\/span><\/p>\n

Tutorial Duration: 1.5 hours<\/strong><\/p>\n

Abstract:\u00a0<\/strong>Software-Defined Wide Area Networking (SD-WAN) has evolved from a cost-effective alternative to traditional, provider-managed wide-area networking solutions into a foundational technology for network automation, edge optimization, and distributed service delivery in modern enterprise and service provider environments. By decoupling the control and data planes and providing centralized policy-driven orchestration, SD-WAN enables automated dynamic traffic steering, real-time analytics, and integration with cloud and edge infrastructures. This tutorial positions SD-WAN as an enabler of network automation at the network edge, with direct relevance to emerging 6G use cases that demand ultra-low latency, pervasive connectivity, and intelligent service orchestration. In future 6G platforms where the network becomes a programmable service delivery substrate, SD-WAN automation capabilities, combined with Artificial Intelligence (AI) insights and network management, can help realize a platform to support distributed AI, by providing dynamic service placement, automated SLA-aware routing, fine-grained traffic segmentation, and adaptive resource scaling across edge nodes and cloud endpoints. The tutorial delivers a comprehensive view of SD-WAN technology and highlights its effectiveness through specific use cases that demonstrate how automated control, analytics, and orchestration facilitate digital transformation for modern enterprises. It will cover architectural principles, automation techniques, AI-enhanced decision intelligence, and integration challenges, illustrated with practical scenarios and testbed demonstrations that showcase both current deployments and forward-looking trends.<\/p>\n

\"Guido

Guido Maier<\/b>
(Politecnico di Milano, Italy)<\/p><\/div><\/div>\n

Bio: <\/strong>Guido\u00a0Maier (Senior member IEEE)<\/b><\/em>, received his Laurea degree in Electronic Engineering at Politecnico di Milano (Italy) in 1995 and his Ph.D. degree in Telecommunication Engineering at the same university in 2000.\u00a0 Until February 2006 he has been researcher at CoreCom (research consortium supported by Pirelli in Milan, Italy), where he achieved the position of Head of the Optical Networking Laboratory.\u00a0 On March 2006 he joined the Politecnico di Milano as Assistant Professor. In 2015 he became Associate Professor. His main areas of interest are: optical network modeling, design and optimization; SDN orchestration and control-plane architectures; SD-WAN and NFV.\u00a0 He is author of more than 150 papers in the area of Networking published in international journals and conference proceedings (h-index 29) and 6 patents. He has been involved in industrial and European research projects, and is currently PI of the project WatchEDGE \u2013 NextGeneratioEU funds. In 2016 he co-founded the start-up SWAN networks, spin-off of Politecnico di Milano. He is editor of the journal Optical Switching and Routing, General Chair of DRCN 2020, DRCN 2021, IEEE NetSoft 2022 and IEEE HPSR 2025, and TPC member in many international conferences. He is IEEE Senior Member.<\/p>\n

 <\/p>\n

\"Sebastian

Sebastian Troia<\/b>
(Politecnico di Milano, Italy)<\/p><\/div><\/div>\n

Bio: <\/strong>Sebastian Troia (member IEEE)<\/b><\/em>\u00a0is an Associate Professor at the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy, and a Fulbright Fellow at the University of Texas at Dallas, USA. He received his Ph.D. in Information Technology (Telecommunications) cum laude\u00a0from Politecnico di Milano in 2020 and is a partner at SWAN Networks, a Politecnico di Milano spin-off specializing in SDN\/SD-WAN orchestration. His research focuses on edge network softwarization and Machine Learning for SDN, SD-WAN, and multi-layer optical\/IP networks. He has contributed to several European projects, including H2020 Metro-Haul and NGI Atlantic, and served as an editor for the ITU-T FG-ML5G. He has authored over 80 publications and is active in TPC and organizing committees for major conferences such as IEEE ICC, GLOBECOM, NetSoft, and others. He is the founder and co-organizer of the Edge Network Softwarization (ENS) workshop series, now in its 5th edition in conjunction with IEEE NetSoft 2026. In 2025, he was honoured to receive the IEEE ComSoc EMEA Outstanding Young Researcher Award, a recognition of his contributions to the field of telecommunication networks. In 2026, he was also a recipient of the IEEE Communications Society Charles Kao Award for the most outstanding paper published in the IEEE\/Optica Journal of Optical Communications and Networking.<\/p>\n

\n

Tutorial #2<\/strong><\/h3>\n

Title: Improving Wireless Next-Generation Industrial IoT (IIoT) Networks with Reinforcement\u00a0Learning<\/em><\/span><\/strong><\/p>\n

Tutorial Duration: 1.5 hours<\/strong><\/p>\n

Abstract: <\/strong>The Industrial Internet of Things (IIoT) is transforming industrial automation by integrating advanced Information and Communication Technologies (ICT) with Artificial Intelligence (AI) to enable flexible, adaptive, and intelligent manufacturing systems. Future industrial environments are expected to increasingly rely on wireless communications to support applications such as autonomous robotics, process monitoring, predictive maintenance, and human-machine interaction. These applications exhibit highly heterogeneous traffic characteristics and impose stringent Quality of Service (QoS) requirements in terms of latency, reliability, scalability, and energy efficiency. Traditional wireless communication and resource management approaches often struggle to cope with the complexity and dynamics of modern industrial environments. Consequently, AI-driven networking solutions are emerging as key enablers of next-generation industrial wireless systems. In particular, the introduction of the Non-Public Network (NPN) paradigm by the 3GPP, together with the vision of AI-native 6G networks, has created new opportunities for the adoption of Reinforcement Learning (RL) techniques in industrial communications.<\/p>\n

This tutorial provides a brief overview of RL and Multi-Agent Reinforcement Learning (MARL) for wireless IIoT systems. Starting from the fundamentals of industrial communications and NPN architectures, the tutorial introduces the main RL concepts and discusses how learning-based approaches can be used to optimize radio resource management, scheduling, and medium access control protocols in industrial scenarios. Particular attention is devoted to practical implementation aspects, including state and reward design, scalability, and deployment constraints. The tutorial is complemented by two representative industrial case studies. The first addresses dynamic radio resource allocation for mobile control panels through a centralized RL framework implemented at the base station. The second focuses on distributed MARL-based MAC protocol learning for process<\/div>\n
monitoring applications, where wireless IoT devices collaboratively learn collision-free channel access strategies. Through these examples, participants will gain insights into the design, implementation, and practical challenges of AI-native wireless industrial networks.<\/div>\n
<\/div>\n
<\/div>\n
\"Luciano

Luciano Miuccio<\/b>
(University of Catania, Italy)<\/p><\/div><\/div><\/div>\n

<\/div>\n
\n

Bio: <\/strong>Luciano Miuccio<\/em><\/strong> received the B.Sc. degree in Electronics Engineering and the M.Sc. degree (cum laude) in Telecommunications Engineering from the University of Catania, Italy, in 2015 and 2018, respectively. In November 2022, he received the Ph.D. degree in Systems, Energy, Computer and Telecommunications Engineering from the University of Catania, with the additional label of \u201cDoctor Europaeus\u201d. Since March 2023, he has been an Assistant Professor of Telecommunications with the Department of Electrical, Electronic and Computer Engineering, University of Catania. From May 2019 to September 2019, he worked as an Early-Stage Researcher on methodologies for the multi-objective optimization of parametric systems. In 2022, he was a Visiting Ph.D. Student at the Centre for Wireless Communications (CWC), University of Oulu, Finland, under the supervision of Prof. Mehdi Bennis. In October 2025, he returned to the same institution as a Visiting Researcher. His research interests include AI-driven radio resource management for B5G\/6G networks, green networking, non-orthogonal multiple access (NOMA), wireless IoT systems, and multi-agent reinforcement learning for communication networks.
\nHe has authored and co-authored numerous scientific publications on reinforcement learning, protocol learning, and intelligent resource management for wireless systems. Dr. Miuccio has served on the Technical Program Committees of several international conferences, including IEEE ICC, IEEE GLOBECOM, IEEE ICMLCN, and IEEE VTC. He has delivered tutorials at international conferences such as IEEE RTSI, IEEE EUROCON, and European Wireless, focusing on reinforcement learning and protocol learning for communication networks. He has also contributed to conference organization as Special Session Chair and Workshop Co-Chair at IEEE RTSI, IEEE EUROCON, IEEE CSCN, and IEEE PiCom. He currently serves as an Associate Editor for Ad Hoc Networks<\/i>, Telecommunication Systems<\/i>, and Wireless Networks<\/i>.<\/p>\n<\/div>\n

\"Salvatore

Salvatore Riolo<\/b>
(University of Catania, Italy)<\/p><\/div><\/div>\n

Bio: Salvatore Riolo<\/em><\/strong> received the B.Sc. degree in Electronics Engineering, the M.Sc. degree (cum laude) in Telecommunications Engineering, and the Ph.D. degree in Systems, Energy, Computer and Telecommunications Engineering from the University of Catania, Italy, in 2012, 2017, and 2021, respectively. Since January 2022, he has been an Assistant Professor of Telecommunications with the Department of Electrical, Electronic and Computer Engineering, University of Catania. From August 2017 to March 2018, he worked as an Early-Stage Researcher in the project \u201cHigh Bit Rate Device-to-Device Services for 5G Mobile Networks.\u201d In 2022, he was a Visiting Researcher at the Centre for Wireless Communications (CWC), University of Oulu, Finland. His research interests include radio resource management for B5G\/6G networks, green networking, massive machine-type communications, AI-native radio access networks, and the application of reinforcement learning techniques to wireless communication systems. His recent research activities focus on intelligent resource management, wireless IoT systems, and AI-enabled communication networks for next-generation industrial and mobile applications. Dr. Riolo has served on the Technical Program Committees of numerous international conferences, including IEEE ICC, IEEE GLOBECOM, and IEEE ICMLCN. He has contributed to conference organization as Workshop Co-Chair at IEEE EUROCON, IEEE CSCN, and IEEE PiCom. Furthermore, he has served as an instructor in multiple international tutorials on reinforcement learning for communication networks, including tutorials delivered at IEEE RTSI and European Wireless. He currently serves as an Associate Editor for Computer Networks<\/i>, Physical Communication<\/i>, and Wireless Personal <\/i>Communications<\/i>.<\/p>\n

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