Nicola Piovesan

Assistant Researcher @ CTTC
PhD Candidate @ UPC
External Researcher @ Nokia Bell Labs
European Commission Marie Skladowska-Curie Fellow


I'm an Assistant Researcher at the Mobile Networks Department of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). I 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. Currently, I'm pursuing a Ph.D. at the department of Network Engineering of the Technical University of Catalonia (UPC), Spain.

Since September 2016, I'm working as European Commission's Marie Skłodowska-Curie fellow in the EU H2020 MSCA SCAVENGE (Sustainable Cellular Networks Harvesting Ambient Energy) project.

My current research interests include energy harvesting in wireless communications, energy transfer systems, resource allocation in wireless communication system and artificial intelligence.


My research project investigates on possible integration architectures between the energy harvesting mobile network and the smart electricity grid. In particular, 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. The goals are (i) to satisfy the demand of communication networks while avoiding energy outages in zones with high user density and/or low ambient energy availability (ii) provide ancillary services to the grid.

The exptected results are to build a theory-to-practice understanding of energy routing systems and of the associated performance trade-offs. In particular, I am studying the design of joint traffic/energy allocation policies for environmentally powered networks with energy transfer capabilities, taking into account complexity constraints, energy storage capability and partial system-state knowledge at the base stations.

EU logoThis project has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 675891 (SCAVENGE).


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

Modeling the Environment in Deep Reinforcement Learning: the case of Energy Harvesting Base Stations
Nicola Piovesan, Marco Miozzo, Paolo Dini

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

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

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.

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.

SCAVENGE WP6 Intermediate report
Angel F. Gambin, Nicola Piovesan, Marco Miozzo, Michele Rossi, Paolo Dini
EU Research Results
Document PDF

The work described in this deliverable is centered around the management of energy in 5G mobile networks, with the main goals of: i) improving the energy balance across Base Stations (BSs) and other network elements, ii) understanding how energy can be exchanged either among BSs and/or traded with the main power grid (referred to as Smart Grid, SG), iii) understanding how energy harvested from ambient energy sources can be utilized within network elements to achieve: an improved user experience, and a decreased energy consumption (carbon footprint) for the mobile network infrastructure. These goals have been tackled following a systematic approach entailing: 1) the study of the state of the art on energy efficient techniques for 5G networks, 2) the investigation of several promising ways in which energy can be exchanged among network elements, 3) the proposal of new techniques for energy management, namely, “energy cooperation” and “energy trading”, which are expounded and numerically evaluated in this report

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)

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.

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

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.

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

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 provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, non-convex, non relaxed formulation is applied in such a way to force the solution towards a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.

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

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.

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.

Studio di Support Vector Machine per la classificazione e la regressione statistica
Nicola Piovesan
B.Sc. thesis, 2013.

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.

Talks and Posters

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


Services to the research community

Reviewer of IEEE Transactions on Wireless Communications, IEEE Transactions on Green Communications and Networking, IEEE Internet of Things Journal, Wiley Transactions on Emerging Telecommunications Technologies, Elsevier Computer Networks, among others.

Technical Program Commitee Member of European Conference on Networks and Communications (EuCNC 2019) and IEEE Vehicular Technology Conference (VTC-Fall 2019).