Sustainable Mobile Networks
Mobile network capacity is continuously demanded to increase to provide a higher and higher volume of data traffic. Such growth has a hidden cost: the increase of the network energy consumption (with its related expenses) and therefore, the generation of greenhouse gases at a high level.
Digital Power Group and GSMA have estimated that all the ICT industry is already responsible of about 4% of the carbon footprint generated by the human activity, the same as the airplane emissions. The trend is a further increase due to the estimated 70% annual growth rate of mobile data traffic. Therefore it is impossible nowadays to design next generation mobile networks without addressing energy saving.
Sustainable design principles must be applied to mobile networks:
- Lower energy consumption through the entire life-cycle (from manufacture to daily use).
- Minimize environmental footprint by reducing greenhouse gas (GHG) emissions.
- Limit resource consumption through waste-free/no-toxic manufacturing, a preference for renewable resources, and an emphasis on recycled materials.
In this research line the application of sustainable design principles to mobile networks is studied.
Three technological objectives have been targeting to achieve this goal:
- Network energy efficiency: the design of network architecture and management schemes that reduce the consumption of the energy resource without damaging system performance. The key issue is to achieve proportionality between energy consumption and traffic load, while maintaining an adequate quality of service for the users.
- Mobile demand-response: the design of network architecture and control schemes that balance energy inflow and spending in presence of equipment powered by harvested ambient energy. The main issue is to satisfy the traffic demands and to meet the quality of service requirements using the available energy budget coming from intermittent and erratic sources (e.g., renewable energy).
- Mobile energy-sharing: the design of control schemes and architecture enabling the exchange of energy (e.g., from the renewables) among the network nodes to increase the level of self-sustainability of the mobile system.
We state energy saving in mobile networks as a decision making process. It is formulated as a single or multiple agent problem. Dynamic Programming, Reinforcement Learning, Grey Relational Analysis, Genetic Algorithms, Fuzzy Logic are tools used for solving our problems. System modeling is for us a key part of our proposals. For this, we take particular attention on it by using data analytics from real or synthetic traces. Statistical inference and machine learning are tools utilized for this analysis.
- D.Temesgene, M. Miozzo, P. Dini, Dynamic Functional Split Selection in Energy Harvesting Virtual Small Cells Using Temporal Difference Learning , in Proceedings of IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 9-12 September 2018, Bologna, (Italy).
- N. Piovesan, P. Dini, Unsupervised Learning of Representations from Solar Energy Data , in Proceedings of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 9-12 September 2018, Bologna (Italy).
- D.Temesgene, N. Piovesan, M. Miozzo, P. Dini, Optimal Placement of Baseband Functions for Energy Harvesting Virtual Small Cells , in Proceedings of IEEE 88th Vehicular Technology Conference: VTC2018-Fall, 27-30 August 2018, Chicago (USA).
- M. Miozzo, P. Dini, Layered Learning Radio Resource Management for Energy Harvesting Small Base Stations , in Proceedings of 2018 IEEE Vehicular Technology Conference (VTC Spring), 3-6 June 2018, Porto (Portugal).
- N. Piovesan, M. Miozzo, P. Dini, Optimal Direct Load Control of Renewable Powered Small Cells: Performance Evaluation and Bounds , IEEE Wireless Communications and Networking Conference (WCNC), April 2018, Barcelona, Spain.
- N. Piovesan, A. Fernandez Gambin, M. Miozzo, M. Rossi, P. Dini, Energy sustainable paradigms and methods for future mobile networks: A survey , Elsevier Computer Communications, 2018.
- D.Temesgene, J. Núñez, P. Dini, Softwarization and Optimization for Sustainable Future Mobile Networks: A Survey , IEEE Access, Vol. 5, pp. 25421 – 25436, December 2017.
- H. D. Trinh, N.Bui, J. Widmer, L. Giupponi, P. Dini, Analysis and Modeling of Mobile Traffic Using Real Traces, in Proceedings of IEEE PIMRC 2018, Montreal, CA, October 2017.
- N. Piovesan, P. Dini, Optimal Direct Load Control of Renewable Powered Small Cells: A Shortest Path Approach , Wiley Internet Technology Letters, pp. e7, September 2017.
- J. Baranda, M. Miozzo, P. Dini, J. Núñez, J. Mangues, Backhaul Routing and Base Station Sleep Mode Engagement in Energy Harvesting Cellular Networks , in Proceedings of ACM MSWIM 2016, 13-17 November 2016, Malta.
- A. Dudnikova, P. Dini, L. Giupponi, D. Panno, Fuzzy Multiple Criteria Switch Off Method for Dense Heterogeneous Networks, in Proceedings of IEEE CAMAD 2015, 7-9 September 2015, Guilford, UK.
- A. Dudnikova, P. Dini, L. Giupponi, D. Panno, Multi?Criteria Decision for Small Cell Switch Off in Ultra?Dense LTE Networks, in Proceedings of IEEE ConTel 2015, 13-15 July 2015, Graz, Austria
- M. Miozzo, Smart Energy Management in Mobile Networks Powered with Renewable Energies, International Symposium on Energy Challenges and Mechanics (ECM3), 7-9 July 2015, Aberdeen, Scotland (UK) (invited)
D. Zordan, M. Miozzo, P. Dini, M. Rossi, When Telecommunication Networks Meet Energy Grids: Cellular Networks with Energy Harvesting and Trading Capabilities, IEEE Communication Magazine, June 2015
- M. Miozzo, L. Giupponi, M. Rossi, P. Dini, Distributed Q-Learning for Energy Harvesting Heterogeneous Networks, in Proceedings of IEEE ICC 2015 workshop on Green Communications and Networks with Energy Harvesting, Smart Grids and Renewable Energies, 12 June 2015, London (UK)
- P. Dini, M. Miozzo, N. Baldo, Sustainable Energy in ICT Industry: Supplying Mobile Networks with RES , International Symposium on Energy Challenges and Mechanics (ECM2), 8-10 August 2014, Aberdeen, Scotland (UK) (invited)
M. Miozzo, D. Zordan, P. Dini, M. Rossi, SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes , in Proceedings of IEEE Energy Conference, 13-16 May 2014, Dubrovnik (Croatia)
- P. Dini, M. Miozzo, N. Bui, N. Baldo, A Model to Analyze the Energy Savings of Base Station Sleep Mode in LTE HetNets, in Proc. of IEEE GreenCom 2013, Beijing, China, August 2013
- G. Piro, M. Miozzo, G. Forte, N. Baldo, L.A. Grieco, G. Boggia, P. Dini, HetNets Powered by Renewable Energy Sources: Sustainable Next-Generation Cellular Networks, IEEE Internet Computing, vol. 17, no. 1, pp. 32-39, Jan.-Feb. 2013
- N. Baldo, P. Dini, J. Mangues, M. Miozzo, J. Núñez, Small cells, wireless backhaul and renewable energy: a solution for disaster aftermath communications, in Proceedings of 4th International Conference on Cognitive Radio and Advanced Spectrum Management (COGART 2011) – Cognitive and Self-Organizing Networks for Disasters Aftermath Assistance, 26-29 October 2011, Barcelona (Spain)
Thesis and Internships
- E. Cuoccio, Reinforcement learning for energy harvesting 5G mobile networks, director: P. Dini, advisors: A. Pascual and G. Boggia, joint Master Thesis by Universitat Politecnica de Catalunya and Politecnico di Bari, July 2016.
- A. Dudnikova, Multi-criteria decision analysis for sleep mode in 5G networks, advisors: L. Giupponi, P. Dini, PhD internship, Università degli studi di Catania, February-September 2015.
If you are interested in working with us (as a trainee, PhD student or post-doc) please send an email with your CV and a tentative research proposal.
The activities of this research line are inserted within the following projects:
Relation with past research lines
This research line is the clear evolution of the activities carried out in the past “Green Wireless Networks” research line.
Moreover, the group working in this line has also contributed to the past research lines on “Smart IP Flow and Mobility Management Techniques” and “Cognitive Network Optimization Techniques”.