Nicola Piovesan

Biography

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

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
Submitted
Abstract 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.

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

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.

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Talks

Upcoming

Data-Driven Modelling and Optimization of Green Future Mobile Networks
ITU AI for Good Global Summit - Session on Green AI: Sustainability, AI and 6G
May 30th, 2024
Geneva, Switzerland
Event

Launch of Green Telecom Competition on Smart Energy Supply Scheduling
ITU AI for Good Global Summit - Session on AI for the future of climate prediction
May 30th, 2024
Geneva, Switzerland
Event

Selected Talks

Data-driven modelling and optimization of green future mobile networks

EuCNC and 6G Summit

June 6, 2023

Event

AI/ML for 5G-Energy Consumption Modelling

ITU AI for Good

July 11, 2023

Event Video

A Journey Towards Energy Efficiency

Universitat Politècnica de València

September 14, 2023

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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), 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.

Nicola Piovesan, PhD