Nicola Piovesan

Nicola Piovesan
Huawei Technologies · Advanced Wireless Technology

Doctor of Philosophy

About

18
Publications
6,647
Reads
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191
Citations
Introduction
Nicola Piovesan is a researcher at Huawei Technologies. He earned the PhD degree in Network Engineering at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain in 2020, and received the B.Sc. degree in Information Engineering and the M.Sc. in Telecommunication Engineering from the University of Padova, Italy, in 2013 and 2016, respectively. His current research interests include energy efficiency, optimization and machine learning in wireless communication systems.
Additional affiliations
July 2020 - June 2022
Huawei Technologies
Position
  • Researcher
February 2019 - August 2019
Nokia Bell Labs
Position
  • Researcher
September 2016 - September 2019
CTTC Catalan Telecommunications Technology Centre
Position
  • Research Assistant
Education
October 2016 - June 2020
Universitat Politècnica de Catalunya
Field of study
  • Network engineering
October 2013 - April 2016
University of Padova
Field of study
  • Telecommunication engineering
October 2009 - July 2013
University of Padova
Field of study
  • Information engineering

Publications

Publications (18)
Article
Full-text available
We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an a...
Article
Full-text available
In this letter, we propose an optimal direct load control of renewable powered small base stations (SBSs) in a two-tier mobile network based on dynamic programming (DP). We represent the DP optimization using Graph Theory and state the problem as a Shortest Path search. We use the Label Correcting Method to explore the graph and find the optimal ON...
Article
Full-text available
In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service...
Conference Paper
Full-text available
In this paper, we propose an unsupervised method to learn hidden features of the solar energy generation from a PV system that may give a more accurate characterization of the process. In a first step, solar radiation data is converted into instantaneous solar power through a detailed source model. Then, two different approaches, namely PCA and aut...
Preprint
The energy consumption of the fifth generation(5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations (BSs) power consumption. In this article, we propose a novel model for a realistic characterisation of the power consumption of 5G...
Chapter
In this chapter, we describe the design of controlling schemes for energy self-sustainable mobile networks through Deep Learning. The goal is to enable an intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. To achieve this goal, we formulate an on-line grid energy and network throughpu...
Article
Full-text available
The deployment of dense networks of small base stations represents one of the most promising solutions for future mobile networks to meet the foreseen increasing traffic demands. However, such an infrastructure consumes a considerable amount of energy, which, in turn, may represent an issue for the environment and the operational expenses of the mo...
Conference Paper
Full-text available
In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energy. We propose a centralized control algorithm based on Deep Reinforcement Learning. The single agent is able to learn how to efficiently balance t...
Article
Full-text available
The massive deployment of Small Base Stations (SBSs) represents one of the most promising solutions adopted by 5G cellular networks to meet the foreseen huge traffic demand. The usage of renewable energies for powering the SBSs attracted particular attention for reducing the energy footprint and, thus, mitigating the environmental impact of mobile...
Article
Full-text available
In this paper, we focus on the design of energy self-sustainable mobile networks, by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. Many papers are available in the literature on this problem, however, we are approaching this issue from a different angle. In fact, we advo...
Conference Paper
Full-text available
Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainabili...
Preprint
Full-text available
Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainabili...
Conference Paper
Full-text available
In this paper, we propose an optimal direct load control of renewable powered small base stations based on Dynamic Programming. The optimization is represented using Graph Theory and the problem is stated as a Shortest Path problem. The proposed optimal algorithm is able to adapt to the varying conditions of renewable energy sources and traffic dem...
Article
We propose an efficient solution to peer-to-peer localization in a wireless sensor network which works in two stages. At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares, Xavier, and Gomes. The convex problem is efficiently solved in a distributed way by an ADMM approach, which...

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Projects

Project (1)
Project
SCAVENGE is a project funded by the European Union in the framework of the H2020 Marie Skłodowska Curie Action - Innovative Training Networks - European Training Networks. SCAVENGE tackles sustainable design, protocols, architectures and algorithms for next generation 5G cellular networks. Our overall purpose is to allow mobile systems and especially their constituting base stations, femto, small-cells, mobile devices and sensors to take advantage of sources harvesting ambient energy (such as renewable sources). Besides, SCAVENGE will set up a training network for early-stage researchers (ESRs), who will contribute to the design and implementation of sustainable 5G mobile networks in Europe, and lead the related key scientific, technological, and industrial initiatives. More info at: www.scavenge.eu