Nicola Piovesan, PhD

Senior Researcher @ Huawei
European Commission Marie Skłodowska-Curie Fellow


Nicola Piovesan

Nicola Piovesan is a Senior Researcher at the Advanced Wireless Technology Lab, 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

FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments
Mert Unsal, Ali Maatouk, Antonio De Domenico, Nicola Piovesan, Fadhel Ayed
NeurIPS 2023, 10-16 December 2023, New Orleans (USA)

As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments. The size of deep learning models makes it difficult to deploy them on low-power or resource-constrained devices, leading to long inference times and high energy consumption. To address these challenges, we propose FlexTrain, a framework that accommodates the diverse storage and computational resources available on different devices during the training phase. FlexTrain enables efficient deployment of deep learning models, while respecting device constraints, minimizing communication costs, and ensuring seamless integration with diverse devices. We demonstrate the effectiveness of FlexTrain on the CIFAR-100 dataset, where a single global model trained with FlexTrain can be easily deployed on heterogeneous devices, saving training time and energy consumption. We also extend FlexTrain to the federated learning setting, showing that our approach outperforms standard federated learning benchmarks on both CIFAR-10 and CIFAR-100 datasets.

TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Ali Maatouk, Fadhel Ayed, Nicola Piovesan, Antonio De Domenico, Merouane Debbah, Zhi-Quan Luo

We introduce TeleQnA, the first benchmark dataset designed to evaluate the knowledge of Large Language Models (LLMs) in telecommunications. Comprising 10,000 questions and answers, this dataset draws from diverse sources, including standards and research articles. This paper outlines the automated question generation framework responsible for creating this dataset, along with how human input was integrated at various stages to ensure the quality of the questions. Afterwards, using the provided dataset, an evaluation is conducted to assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The results highlight that these models struggle with complex standards related questions but exhibit proficiency in addressing general telecom-related inquiries. Additionally, our results showcase how incorporating telecom knowledge context significantly enhances their performance, thus shedding light on the need for a specialized telecom foundation model. Finally, the dataset is shared with active telecom professionals, whose performance is subsequently benchmarked against that of the LLMs. The findings illustrate that LLMs can rival the performance of active professionals in telecom knowledge, thanks to their capacity to process vast amounts of information, underscoring the potential of LLMs within this domain. The dataset has been made publicly accessible on GitHub.


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

Device and Method for Real-Time Prediction of QoE in Voice Services
A. De Domenico, F. Ayed, D. Lopez-Perez, N. Piovesan, W. Li, X. Wei, J. Chen
Filed patent

Telecomunications System for Controlling Network Resource Allocation to User Subjective Requirements and Method Therefor
N. Piovesan, F. Ayed, D. López-Pérez, A. De Domenico, W. Xing, H. Bao
Filed patent



Awards & Honors
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), 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.