Nicola Piovesan, PhD

Senior Researcher @ Huawei
European Commission Marie Skłodowska-Curie Fellow

Biography

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.

Publications

2022
Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach
Nicola Piovesan, David López-Pérez, Antonio De Domenico, Xinli Geng, Harvey Bao
Submitted

Sleep mode strategies for energy efficient cell-free massive MIMO in 5G deployments
Felip Riera-Palou, Guillem Femenias, David López-Pérez, Nicola Piovesan, Antonio De Domenico
Submitted

Modelling User Transfer during Dynamic Carrier Shutdown in Green 5G Networks
Antonio De Domenico, David López-Pérez, Nicola Piovesan, Xinli Geng, Harvey Bao
Submitted

Rate, Power, and Energy Efficiency trade-offs in Massive MIMO Systems with Carrier Aggregation
Alessio Zappone, David López-Pérez, Antonio De Domenico, Nicola Piovesan, Harvey Bao
Submitted

Carrier Aggregation for Improved Rate versus Power trade-off in Massive MIMO Systems
Alessio Zappone, David López-Pérez, Antonio De Domenico, Nicola Piovesan, Harvey Bao
IEEE Global Communications Conference (GLOBECOM), 4-8 December 2022, Rio de Janeiro (Brazil)

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation capabilities, and tackles the rate versus power consumption trade-off, by jointly optimizing the number of employed carriers, transmit antennas, base station density, and transmit power. A provably convergent algorithm is developed together with closed-form results for the individual optimization of the considered resources, Numerical results show how the use of carrier aggregation represents an effective way of reducing the number of deployed antennas, reducing the power consumption without sacrificing the rate performance

Machine Learning and Analytical Power Consumption Models for 5G Base Stations
Nicola Piovesan, David López-Pérez, Antonio De Domenico, Xinli Geng, Harvey Bao, Mérouane Debbah
IEEE Communications Magazine
Paper 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 multi-carrier BSs, which builds on a large data collection campaign. At first, we define a new machine learning architecture that allows modelling multiple 5G BS products. Then, we exploit the knowledge gathered by our machine learning framework to derive a realistic but also analytically tractable power consumption model, which can help driving both theoretical analyses as well as feature standardisation, development and optimisation frameworks. Notably, we demonstrate that such model has high precision, and it is able of capturing the benefits of energy saving mechanisms. We believe this analytical model represents a fundamental tool for understanding 5G BSs power consumption, and accurately optimising the network energy efficiency.

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning
David López-Pérez, Antonio De Domenico, Nicola Piovesan, Harvey Bao, Xinli Geng, Song Qitao, Mérouane Debbah
IEEE Communications Surveys and Tutorials
Paper Preprint

Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -- from a theoretical and a practical viewpoint -- the main energy efficiency enabling technologies that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key enabling technologies, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -- and the status on -- green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.



2021
Mobile Traffic Forecasting for Green 5G Networks
Nicola Piovesan, Antonio De Domenico, David López-Pérez, Harvey Bao, Xinli Geng, Xie Wang, Mérouane Debbah
IEEE Global Communications Conference (GLOBECOM), 7-11 December 2021, Madrid (Spain)
Paper

The energy consumption and carbon footprint of the fifth-generation (5G) of mobile technology is a current concern to mobile network operators (MNOs). These are currently attempting to lower both their carbon emissions and electricity bills by investigating new schemes that allow adapting the network transmission capabilities to the end-users' quality of service (QoS) requirements. Many of such schemes rely on accurate traffic forecasting, and as a consequence, there is a large effort on investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting users' behaviours. Most of the works in the literature, however, focus on predicting the traffic when energy saving features, e.g. carrier shutdown, are not implemented or activated. However, the prediction task becomes much more challenging when energy saving features are adopted due to their impact to the actual measured traffic. In this paper, we consider a scenario in which part of the base stations implement energy saving schemes, which allow them to dynamically switch off part of their hardware to reduce their power consumption. Then, we present a ML framework based on graph convolutional networks (GCNs) for traffic forecasting in such dynamic scenarios, and compare its performance with other statistical and ML prediction algorithms. The proposed GCN framework provides significant accuracy gains. Moreover, we provide an analysis of the impact of spatial correlation ---captured by the GCN model--- on the achieved performance.

Energy Efficiency of Multi-Carrier Massive MIMO Networks: Massive MIMO Meets Carrier Aggregation
David López-Pérez, Antonio De Domenico, Nicola Piovesan, Xinli Geng, Harvey Bao, Mérouane Debbah
IEEE Global Communications Conference (GLOBECOM), 7-11 December 2021, Madrid (Spain)
Paper

The energy consumption of cellular networks, despite the high energy efficiency of the fifth generation (5G) of mobile technology, is still a challenge. The fundamental problem arises due to the complexity of optimising the operation of the available rich set of energy efficiency features in large-scale deployments. To assist such optimisation, a large body of research --with the resulting understanding and algorithms-- exists, particularly on the energy efficiency of single-cell massive multiple-input multiple-output systems. However, other fundamental cellular features, such as those relating to multi-carrier systems, remain largely unexplored. In this paper, we show how multi-carrier features, such as carrier aggregation, can play a significant role in energy savings, and question the need for hundreds of antennas and transceiver chains at the base stations as an urgent solution to increase the energy efficiency of next generation networks.

Forecasting Mobile Traffic to Achieve Greener 5G Networks: When Machine Learning is Key
Nicola Piovesan, Antonio De Domenico, Matteo Bernabè, David López-Pérez, Harvey Bao, Xinli Geng, Xie Wang, Mérouane Debbah
IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 27-30 September 2021, Lucca (Italy)
Paper

To minimise the energy consumption of the fifth generation (5G) of mobile technology, it is necessary to adapt the transmission capabilities of 5G networks to the end-users' quality of service (QoS) requirements. In this line, researchers are investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting user behaviour. In this paper, we focus on the task of forecasting mobile traffic, which is a key step to dynamically adapt network parameters to space-time traffic variations by optimizing energy efficiency features, such as carrier shutdown. Specifically, we present a state-of-the-art ML framework based on Graph convolutional Networks, and compare its performance with simpler schemes, characterized by a lower complexity. Our analysis highlights advantages and drawbacks of these solutions, paying special attention to the importance of the error metric selection, feature pre-processing and historical data availability.



2020
Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids
Marco Miozzo, Nicola Piovesan, Dagnachew Temesgene, Paolo Dini
Deep Learning for Unmanned Systems
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 throughput optimization problem considering both centralized and distributed Deep Reinforcement Learning implementations. We provide an exhaustive discussion on the reference scenario, the techniques adopted, the achieved performance, the complexity and the feasibility of the proposed models, together with the energy and cost savings attained. Results demonstrate that Deep Q-Learning based algorithms represent a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.

Joint Load Control and Energy Sharing for Renewable Powered Small Base Stations: a Machine Learning Approach
Nicola Piovesan, David López-Pérez, Marco Miozzo, Paolo Dini
IEEE Transactions on Green Communication and Networking
Paper

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 mobile operators. The use of renewable energy to supply the small base stations has been recently considered as a mean to reduce the energy footprint of the mobile networks. In this paper, we consider a hierarchical structure in which part of the base stations are powered exclusively by solar panels and batteries. Base stations are grouped in clusters and connected in a micro-grid. A central controller enables base station sleep mode and energy sharing among the base stations based on the available energy budget and the traffic demands. We propose three different implementations of the controller through Machine Learning models, namely Imitation Learning, Q-Learning and Deep Q-Learning, capable of learning optimal sleep mode and energy sharing policies. We provide an exhaustive discussion on the achieved performance, complexity and feasibility of the proposed models together with the energy and cost savings attained.

Network Resource Allocation Policies with Energy Transfer Capabilities
Nicola Piovesan
Ph.D. dissertation
Thesis

During the last decades, mobile network operators have witnessed an exponential increase in the traffic demand, mainly due to the high request of services from a huge amount of users. The trend is of a further increase in both the traffic demand and the number of connected devices over the next years. The traffic load is expected to have an annual growth rate of 53% for the mobile network alone, and the upcoming industrial era, which will connect different types of devices to the mobile infrastructure including human and machine type communications, will definitely exacerbate such an increasing trend. The current directions anticipate that future mobile networks will be composed of ultra dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist. Accordingly, the traditional Macro BSs layer will be complemented or replaced with multiple overlapping tiers of small BSs (SBSs), which will allow extending the system capacity. However, the massive use of Information and Communication Technology (ICT) and the dense deployment of network elements is going to increase the level of energy consumed by the telecommunication infrastructure and its carbon footprint on the environment. Current estimations indicates that 10% of the worldwide electricity generation is due to the ICT industry and this value is forecasted to reach 51% by 2030, which imply that 23% of the carbon footprint by human activity will be due to ICT. Environmental sustainability is thus a key requirement for designing next generation mobile networks. Recently, the use of Renewable Energy Sources (RESs) for supplying network elements has attracted the attention of the research community, where the interest is driven by the increased efficiency and the reduced costs of energy harvesters and storage devices, specially when installed to supply SBSs. Such a solution has been demonstrated to be environmentally and economically sustainable in both rural and urban areas. However, RESs will entail a higher management complexity. In fact, environmental energy is inherently erratic and intermittent, which may cause a fluctuating energy inflow and produce service outage. A proper control of how the energy is drained and balanced across network elements is therefore necessary for a self-sustainable network design. In this dissertation, we focus on energy harvested through solar panels that is deemed the most appropriate due to the good efficiency of commercial photovoltaic panels as well as the wide availability of the solar source for typical installations. The characteristics of this energy source are analyzed in the first technical part of the dissertation, by considering an approach based on the extraction of features from collected data of solar energy radiation. In the second technical part of the thesis we introduce our proposed scenario. A federation of BSs together with the distributed harvesters and storage devices at the SBS sites form a micro-grid, whose operations are managed by an energy management system in charge of controlling the intermittent and erratic energy budget from the RESs. We consider load control (i.e., enabling sleep mode in the SBSs) as a method to properly manage energy inflow and spending, based on the traffic demand. Moreover, in the third technical part, we introduce the possibility of improving the network energy efficiency by sharing the exceeding energy that may be available at some BS sites within the micro-grid. Finally, centralized controller implementations based on supervised and reinforcement learning are proposed in the last technical part of the dissertation. The controller is in charge of opportunistically operating the network to achieve efficient utilization of the harvested energy and prevent SBSs blackout.

Modeling the Environment in Deep Reinforcement Learning: the case of Energy Harvesting Base Stations
Nicola Piovesan, Marco Miozzo, Paolo Dini
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 4-8 May 2020, Barcelona (Spain)
Paper

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 the energy inflow and spending among base stations observing the environment and interacting with it. In particular, we provide a study on the performance achieved by this approach when considering different representations of the environment. Numerical results demonstrate that using a good level of abstraction in the choice of the representation variables may enable a proper mapping of the environment into actions to take, so as to maximize the numerical reward.



2019
Coordinated Load Control of Renewable Powered Small Base Stations through Layered Learning
Marco Miozzo, Nicola Piovesan, Paolo Dini
IEEE Transactions on Green Communications and Networking
Paper

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 networks and enabling cost saving for the operators. The complexity of the system and the variability of the harvesting process suggest the adoption of learning methods. Here, we investigate techniques based on the Layered Learning paradigm to control dense networks of SBSs powered solely by solar energy. In the first layer, SBSs locally select switch ON/OFF policies according to their energy income and traffic demand based on a Heuristically Accelerated Reinforcement Learning method. The second layer relies on an Artificial Neural Network that estimates the network load conditions to implement a centralized controller enforcing local agent decisions. Simulation results prove that the control of the proposed framework mimics the behavior of the upper bound obtained offline with Dynamic Programming. Moreover, the proposed layered framework outperforms both a greedy and a distributed Reinforcement Learning solution in terms of throughput and energy efficiency under different traffic conditions.

Joint Load Control and Energy Sharing for Autonomous Operation of 5G Mobile Networks in Micro-Grids
Nicola Piovesan, Dagnachew Temesgene, Marco Miozzo, Paolo Dini
IEEE Access
Paper

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 advocate for future mobile networks with a hierarchical cell structure and powered by energy harvesting hardware. Base stations within the same geographical area are grouped in a micro-grid and operate almost autonomously from the power grid. To achieve this goal, we target the design of optimal traffic and computational load control method with energy sharing within the micro-grid. We solve the optimization problem using a graph-based method and we demonstrate, via software simulations, that a combination of load control plus energy sharing represents a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.



2018
Unsupervised Learning of Representations from Solar Energy Data
Nicola Piovesan, Paolo Dini
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 9–12 September 2018, Bologna (Italy)
Paper Preprint

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 autoencoder, are used to extract meaningful features from the traces of the solar energy generation. We interpret the latent variables characterizing the solar energy generation process by analyzing the similarities of 67 cities in Europe, North-Africa and Middle-East through an agglomerative hierarchical clustering algorithm. This analysis provides also a comparison between the feature extraction capabilities of the PCA and the autoencoder.

Optimal Placement of Baseband Functions for Energy Harvesting Virtual Small Cells
Dagnachew Temesgene, Nicola Piovesan, Marco Miozzo, Paolo Dini
IEEE Vehicular Technology Conference: VTC2018-Fall, 27–30 August 2018, Chicago (USA)
Preprint

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 sustainability and economic advantages. Power consumption due to baseband processing has a huge share in the total power consumption breakdown of smaller base stations. Given that such base stations are powered by EH, in addition to QoS constraints, energy availability also conditions the decision on where to place each baseband function in the system. This work focuses on determining the performance bounds of an optimal placement of baseband functional split option in virtualized small cells that are solely powered by EH. The work applies Dynamic Programming (DP), in particular, Shortest Path search is used to determine the optimal functional split option considering traffic requirements and available energy budget.

Optimal Direct Load Control of Renewable Powered Small Cells: Performance Evaluation and Bounds
Nicola Piovesan, Marco Miozzo, Paolo Dini
IEEE Wireless Communications and Networking Conference (WCNC), 15-18 April 2018, Barcelona (Spain)
Paper Preprint

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 demands. We analyze the optimal ON/OFF policies considering different energy and traffic scenarios. Then, we evaluate network performance in terms of system drop rate and grid energy consumption. The obtained results are compared with a greedy approach. This study allows to elaborate on the behavior and performance bounds of the system and gives a guidance for approximated policy search methods.

Energy sustainable paradigms and methods for future mobile networks: A survey
Nicola Piovesan, Angel F. Gambin, Marco Miozzo, Michele Rossi, Paolo Dini
Elsevier Computer Communications, 2018.
Paper Preprint

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 required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.



2017
Optimal Direct Load Control of Renewable Powered Small Cells: A Shortest Path Approach
Nicola Piovesan, Paolo Dini
Internet Technology Letters, Wiley, 2017.
Paper

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/OFF policy for the SBSs. Simulation results demonstrate that the proposed algorithm is able to adapt to the varying conditions of the environment, namely renewable energy arrivals and traffic demands. The key benefit of our study is that it allows to elaborate on the behavior and performance bounds of the system.



2016
Cooperative Localization in WSNs: a Hybrid Convex/non-Convex Solution
Nicola Piovesan, Tomaso Erseghe
IEEE Transactions on Signal and Information Processing over Networks, 2016.
Paper Preprint

Data Analytics for Smart Parking Applications
Nicola Piovesan, Leo Turi, Enrico Toigo, Borja Martinez, Michele Rossi
MDPI Sensors, 2016.
Paper

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 automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

Distributed algorithms for localization in Wireless Sensor Networks
Nicola Piovesan
M.Sc. thesis, 2016.
Thesis  

We introduce the concept of localization in Wireless Sensor Networks, starting from ranging measurements available at sensor nodes. We explore different solutions available in the literature and then we introduce the algorithms proposed by two recent publications. Finally, we propose a new hybrid ADMM algorithm that, considering both the original non-convex problem and a convexification of the problem, allows to obtain better performances.



2013
Support Vector Machines for classification and statistical regression
Nicola Piovesan
B.Sc. thesis, 2013.
Thesis  

Il lavoro studia inizialmente l’utilizzo di Support Vector Machine per la classificazione. Vengono introdotte SVM lineari per dati separabili e non separabili e SVM non lineari con Kernel. In seguito, quanto visto in via teorica viene applicato a due casi di classificazione (Iris di Fisher e classificazione dei dati di una analisi SPECT). Per finire vengono introdotte le SVM per la regressione statistica.

Patents

Method for AI/ML-based Network Energy Consumption Estimation
D. López-Pérez, N. Piovesan, A. De Domenico, H. Bao, Y. Wang
Filed patent

Method to Maximize Energy Efficiency in a Multi-Carrier Cellular Network
A. De Domenico, D. López-Pérez, N. Piovesan, N. Zhao, H. Bao
Filed patent

Method for UE-centric Carrier Shutdown
D. López-Pérez, A. De Domenico, N. Piovesan, X. Geng, H. Bao
Filed patent

Tutorials

Breaking the Energy Consumption Growth in Future Mobile Networks: 5G Enhancements and Machine Learning
N. Piovesan, A. De Domenico, D. López-Pérez
IEEE International Conference on Communications (ICC), 16-20 May 2022, Seoul (South Korea) Conference  

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. Despite its capabilities, however, 5G networks must improve in certain key technology areas, such as that of energy efficiency. While current 3GPP NR deployments provide an improved energy efficiency of around 4x w.r.t. 3GPP LTE ones, they still consume up to 3x more energy. Even if the 3GPP NR specification provides several tools to meet IMT-2020 energy efficiency requirements, 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 view on this 5G energy efficiency problem. In details, the tutorial provides a fresh look at energy efficiency enabling technologies in 3GPP NR. Moreover, 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 used to derive accurate machine learning models for the main building blocks of the energy efficiency optimization problem.

Talks and Posters

Machine Learning and Analytical Power Consumption Models for 5G Base Stations
Next Generation Mobile Networks (NGMN) Green Future Networks
December 2nd, 2021
Online

Forecasting Mobile Traffic to Achieve Greener 5G Networks: When Machine Learning is Key
Huawei AI forum
May 27th, 2021
Huawei, Paris, France

European Training Networks: My experience
WindMill kickoff school
November 19th, 2019
Aalborg University, Aalborg, Denmark

Energy-aware network control
SoftCom 2019
September 22th, 2019
Split, Croatia

The talk focuses on the control methods for the management of mobile networks with energy harvesting capabilities. We introduce a two-tier mobile network consisting of macro base stations and renewable energy powered small base stations. Online optimization approaches based on machine learning are proposed as a way to optimize the utilization of the harvested energy by intelligently switching on/off the small base stations. Moreover, Multi-access Edge Computing (MEC) is introduced, where the dynamic placement of functional splits is adopted as a way to further reduce the mobile network energy consumptions.

Network resource allocation policies with energy transfer capabilities: Latest research results and future steps
SCAVENGE workshop
September 27th, 2018
CTTC, Barcelona, Spain

Network resource allocation policies with energy transfer capabilities
1st CTTC Workshop
September 21th, 2018
Sitges, Spain

This project investigates on possible integration architectures between the energy harvesting mobile network and the smart electricity grid. The main scope is to study the capability of 5G mobile networks of intelligently routing energy in a micro grid of interconnected conventional/renewable energy sources and loads, in order to (i) satisfy the demand of communication networks while avoiding energy outages in zones with high user density and/or low ambient energy availability and (ii) to provide ancillary services to the grid.

SCAVENGE: a European Training Network for PhD students
Science week 2017
November 16th, 2017
CTTC, Barcelona, Spain

In this talk we introduce our work in SCAVENGE, an European Training Network. The project is about sustainable design for mobile communication systems by engineering the integration and the control of renewable energy sources within communication network elements, such as base stations and mobile phones. We will describe the main objectives of the Network, detailing the training and research activities that we have been carrying out during our first year of work.

Network resource allocation policies with energy transfer capabilities
SCAVENGE mid-term review
September 21th, 2017
Paris, France

This project investigates on possible integration architectures between the energy harvesting mobile network and the smart electricity grid. The main scope is to study the capability of 5G mobile networks of intelligently routing energy in a micro grid of interconnected conventional/renewable energy sources and loads, in order to (i) satisfy the demand of communication networks while avoiding energy outages in zones with high user density and/or low ambient energy availability and (ii) to provide ancillary services to the grid.

Optimal energy conservation policies for self-sustainable Small Cells
SCAVENGE workshop
June 2nd, 2017
Strathclyde University, Glasgow, UK

Current trends anticipate that 5G mobile networks will be composed of ultra-dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist to provide the 1000x network capacity increase that is required by 2020. Accordingly, the traditional macro cell layer will be complemented or replaced with multiple overlapping tiers of smaller cells, which extend the system capacity, thanks to a higher spatial reuse and to a better spectral efficiency. We introduce a two-tier cellular network architecture, where self-sustainable small cells (SCs), solely relying on energy harvesting and storage, can offload the grid-connected macro base station. The available energy at the SC must be allocated in an optimal way, in order to jointly minimize the consumption of grid energy and maximize the system performance. The proposed offline optimal algorithm, based on the label correcting algorithm, finds the optimal energy allocation policy, after transforming the problem into a shortest-path problem.

Presentation of the SCAVENGE ESR-05
SCAVENGE initial training school
November 21st, 2016.
CTTC, Barcelona, Spain

Data analytics for Smart Parking Applications
DEI Awards
June 5th, 2015.
University of Padova, Padova, Italy


Other

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


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 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).