Nicola Piovesan, PhD


Nicola Piovesan

Nicola Piovesan is a Senior Researcher at Huawei Technologies, in Paris, France.

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.

His current research interests include energy efficiency in mobile networks, optimization, and machine learning in wireless communication systems.

Recent Publications

Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
Andrei-Laurentiu Bornea, Fadhel Ayed, Antonio De Domenico, Nicola Piovesan, Ali Maatouk

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field. The paper introduces and open-sources Telco-RAG, a customized RAG framework designed to handle the specific needs of telecommunications standards, particularly 3rd Generation Partnership Project (3GPP) documents. Telco-RAG addresses the critical challenges of implementing a RAG pipeline on highly technical content, paving the way for applying LLMs in telecommunications and offering guidelines for RAG implementation in other technical domains.

Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
Tingwei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu, Qingjiang Shi



Data-Driven Modelling and Optimization of Green Future Mobile Networks: From Machine Learning to Generative AI Tutorial
N. Piovesan, A. De Domenico, D. López-Pérez
IEEE International Conference on Machine Learning for Communications and Networking, Stockholm (Sweden), 5 May 2024

The fifth generation (5G) of radio technology is revolutionizing our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. Moreover, 5G technology is allowing for the first time to expand cellular systems into new ecosystems, thus impacting every industry. Despite its unprecedented capabilities, however, 5G networks can —and must— further improve in certain key technology areas, such as that of energy efficiency. While current third generation partnership project (3GPP) new radio (NR) deployments provide an improved energy efficiency of around 4x w.r.t. 3GPP long term evolution (LTE) ones, they still consume up to 3x more energy. This is mostly due to the more processing required to handle the wider bandwidth and the more antennas, and is resulting in increased carbon emissions and electricity bills for operators. Even if the 3GPP NR specification provides a rich set of tools to meet IMT-2020 energy efficiency requirements, such as carrier, channel, and symbol shutdown, among others, it is important to note that one of the main energy consumption challenges of 5G networks is the complexity of their optimization in wide-area deployments: A large-scale, stochastic, non-convex and non-linear optimization problem. In light of the increasing interest in this field, this one-of-a-kind tutorial shares the author's industrial and academic views on this 5G energy efficiency problem. In more details, the tutorial provides a fresh look at energy efficiency enabling technologies in 3GPP NR, in particular, massive MIMO, carrier aggregation, the lean carrier design and different shutdown methods. By leveraging on the concepts of big data and machine learning, the tutorial presents practical scenarios in which data collected from thousands of base stations can be successfully used to derive accurate machine learning models for the main building blocks of the energy efficiency optimization problem. Furthermore, it explores the possibility of a future where generative AI plays a central role in autonomously generating explainable models, thus advancing the quest for energy-efficient networks. In this context, the tutorial delves into the adoption of large language models (LLMs) in the industry and the need for foundational telecom models and specific evaluation frameworks for generative AI.

Selected Talks
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Data-driven modelling and optimization of green future mobile networks
EuCNC and 6G Summit

June 6, 2023

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AI/ML for 5G-Energy Consumption Modelling
ITU AI for Good

July 11, 2023

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A Journey Towards Energy Efficiency
Universitat Politècnica de València

September 14, 2023


Recent Patents

Method for shutting down a cell of one or more cells of a base station for wireless communication
N. Piovesan, A. De Domenico, N. Zhao, L. Madier
Filed patent

Devices and methods for real-time ML framework supporting network optimization
A. De Domenico, F. Ayed, N. Piovesan, N. Zhao, M. Spini, A. Maatouk
Filed patent



Awards & Honors
2024 · Huawei · Quality Star Award
2021 · Huawei · GTS President Award - Technology Innovation and Breakthrough Award
2020 · Huawei · Future Star Award
2016 · European Union · Marie Skłodowska-Curie fellowship
2015 · WorldSensing · Winner of the Smart City Big Data Contest

Using AI to make 5G more sustainable

Services to the research community

Reviewer of IEEE Transactions on Wireless Communications, IEEE Communications Magazine, IEEE Transactions on Green Communications and Networking, IEEE Internet of Things Journal, Wiley Transactions on Emerging Telecommunications Technologies, Elsevier Computer Networks, Elsevier Computer Communications, among others.

Technical Program Commitee Member of IEEE International Conference on Communications conference (ICC 2023, ICC 2024), IEEE Wireless Communications and Networking Conference (WCNC 2022), European Conference on Networks and Communications (EuCNC 2019, EuCNC 2020) and IEEE Vehicular Technology Conference (VTC-Fall 2019).

Co-chair of the Workshop on The Impact of Large Language Models on 6G Networks, in conjunction with IEEE ICC 2024, Denver, USA, June 2024.