Nicola Piovesan, PhD

Biography

Nicola Piovesan

Nicola Piovesan is a Senior Researcher at Huawei Technologies, in Paris, France. His research activity focuses on large-scale network modeling, data-driven network optimization, green networking, and the integration of artificial intelligence in the telecom domain.

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.

Nicola has authored over 30 research articles and holds co-inventorship in over 10 patent applications. His achievements have been recognized with the Huawei GTS President Award in 2021 and the Huawei Quality Star Award in 2024, acknowledging his successful research application into product development.

Recent Publications

Hermes: A Large Language Model Framework on the Journey to Autonomous Networks
Fadhel Ayed, Ali Maatouk, Nicola Piovesan, Antonio De Domenico, Merouane Debbah, Zhi-Quan Luo
Submitted
Preprint

The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Twins (NDTs) have shown promise in enhancing network intelligence, but the successful implementation of this technology is constrained by use case-specific architectures, limiting its role in advancing network autonomy. A more capable network intelligence, or "telecommunications brain", is needed to enable seamless, autonomous management of cellular network. Large Language Models (LLMs) have emerged as potential enablers for this vision but face challenges in network modeling, especially in reasoning and handling diverse data types. To address these gaps, we introduce Hermes, a chain of LLM agents that uses "blueprints" for constructing NDT instances through structured and explainable logical steps. Hermes allows automatic, reliable, and accurate network modeling of diverse use cases and configurations, thus marking progress toward fully autonomous network operations.

Waste Factor and Waste Figure: A Unified Theory for Modeling and Analyzing Wasted Power in Radio Access Networks for Improved Sustainability
Theodore S. Rappaport, Mingjun Ying, Nicola Piovesan, Antonio De Domenico, Dipankar Shakya
IEEE Open Journal of the Communications Society
Paper Preprint

This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and RANs. Also, the networks used to connect data centers and AI computing engines with users for ML applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We delineate the methodology for applying W across various network configurations, including MISO, SIMO, and MIMO systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy performance of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other KPIs to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit can significantly contribute to the sustainability and energy optimization of next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies.

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Talks

Selected Talks
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Network Modelling and Optimization for the 6G Era: Human Expertise Meets AI
Abu Dhabi 6G summit

November 14, 2024

Event Video  
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Data-Driven Modelling and Optimization of Green Future Mobile Networks
ITU AI for Good Global Summit

May 30, 2024

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

July 11, 2023

Event Video  

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Recent Patents

Method for activating energy saving for a base station
A. De Domenico, L. Madier, N. Piovesan, H. Feng
Filed patent

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

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Other

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

Press
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, ICC 2025), 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, and of the Workshop on The Impact of Multi-Modal Large Language Models on 6G and Beyond, in conjunction with IEEE Globecom 2024, Cape Town, South Africa, December 2024.