Nicola Piovesan

Nicola Piovesan, PhD

Senior Researcher

Nicola Piovesan is a Senior Researcher at Huawei Technologies, in Paris, France. His research activity focuses on generative AI in next-generation telecom networks, large-scale network modeling, and green networking.

He earned his PhD degree in Network Engineering at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, in 2020, and he received the BSc degree in Information Engineering and the MSc in Telecommunication Engineering from the University of Padova, Italy, in 2013 and 2016, respectively.

From 2016 to 2019, he was an Assistant Researcher at the Mobile Networks Department of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). In 2016, he has been awarded with a European Commission’s Marie Skłodowska-Curie fellowship to work as early-stage researcher in the EU H2020 MSCA SCAVENGE (Sustainable Cellular Networks Harvesting Ambient Energy) project.

In 2019, he was at Nokia Bell Labs as a visiting researcher in the Small Cells Research Department.

Dr. Piovesan has contributed to multiple industrial and academic projects, published over 40 research articles, and is co-inventor of more than 10 patents. His work has been recognized with the Huawei GTS President Award (2021) and the Huawei Quality Star Award (2024), acknowledging his successful translation of research into real-world product development. He is actively involved in collaborative initiatives on AI and energy efficiency in next-generation networks.

Recent Publications

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Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

Preprint

Submitted

Authors: Mohamed Sana, Nicola Piovesan, Antonio De Domenico, Yibin Kang, Haozhe Zhang, Merouane Debbah, Fadhel Ayed

Preprint Dataset

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

Preprint

Submitted

Authors: Vincenzo Colle, Mohamed Sana, Nicola Piovesan, Antonio De Domenico, Fadhel Ayed, Merouane Debbah

Preprint Dataset

The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathematically intensive tasks. Although recent advancements have improved the performance of LLMs in general mathematical reasoning, their effectiveness within specialized domains, such as signal processing, network optimization, and performance analysis, remains largely unexplored. To address this gap, we introduce TeleMath, the first benchmark dataset specifically designed to evaluate LLM performance in solving mathematical problems with numerical solutions in the telecommunications domain. Comprising 500 question-answer (QnA) pairs, TeleMath covers a wide spectrum of topics in the telecommunications field. This paper outlines the proposed QnAs generation pipeline, starting from a selected seed of problems crafted by Subject Matter Experts. The evaluation of a wide range of open-source LLMs reveals that best performance on TeleMath is achieved by recent models explicitly designed for mathematical or logical reasoning. In contrast, general-purpose models, even those with a large number of parameters, often struggle with these challenges. We have released the dataset and the evaluation code to ease result reproducibility and support future research.