Publications
2020
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
Submitted
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)
Abstract
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
Abstract
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
Abstract
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)
Abstract
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
Abstract
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)
Abstract
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)
Abstract
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.
, Angel F. Gambin, Marco Miozzo, Michele Rossi, Paolo Dini
Elsevier Computer Communications, 2018.
Abstract
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.
Abstract
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.
Abstract
Paper
Preprint
Code
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.
You can request the code by sending me an email. Please give credits to this paper if you use the code for your research.
, Leo Turi, Enrico Toigo, Borja Martinez, Michele Rossi
MDPI Sensors, 2016.
Abstract
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.
2016.
Abstract
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
Studio di Support Vector Machine per la classificazione e la regressione statistica
Nicola Piovesan
B.Sc. thesis, 2013.
Abstract
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.
This project has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 675891 (SCAVENGE).
