Paper Info
Title
Energy Sustainable Mobile Networks via Energy Routing, Learning and Foresighted Optimization.
Abstract
The design of self-sustainable base station (BS) deployments is addressed in this paper: BSs have energy harvesting and storage capabilities, they can use ambient energy to serve the local traffic or store it for later use. A dedicated power packet grid allows energy transfer across BSs, compensating for imbalance in the harvested energy or in the traffic load. Some BSs are offgrid, i.e., they can only use the locally harvested energy and that transferred from other BSs, whereas others are ongrid, i.e., they can also purchase energy from the power grid. Within this setup, an optimization problem is formulated where: energy harvested and traffic processes are estimated at the BSs through Gaussian Processes (GPs), and a Model Predictive Control (MPC) framework is devised for the computation of energy allocation and transfer schedules. Numerical results, obtained using real energy harvesting and traffic profiles, show substantial improvements in terms of energy self-sustainability of the system, outage probability (zero in most cases), and in the amount of energy purchased from the power grid, which is of more than halved with respect to the case where the optimization does not consider GP forecasting and MPC.
Year
Venue
Field
2018
arXiv: Networking and Internet Architecture
Base station,Mathematical optimization,Computer science,Network packet,Model predictive control,Energy harvesting,Schedule,Global Positioning System,Optimization problem,Grid,Distributed computing
DocType
Volume
Citations 
Journal
abs/1803.06173
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Angel Fernandez Gambin100.34
Maria Scalabrin282.41
Michele Rossi36311.68