Load-adaptive networking for energy-efficient wireless access

Energy-efficient operation is essential for mobile network operators to meet the growing demand for higher data rates while managing rising operating costs. Here, the main challenge is to guarantee the quality of user experience whilst saving energy. This challenge demands adaptive algorithms that enable a load-aware network operation that dynamically configures different network elements according to user needs. To this end, in this paper, we present an adaptive and context-aware power management framework for networks composed of different radio access technologies. We implement and evaluate our framework in an indoor and outdoor testbed. The experimental results confirm that significant energy can be saved in practice by efficiently adapting resources to the actual traffic demand.

[1]  Sourjya Bhaumik,et al.  Breathe to stay cool: adjusting cell sizes to reduce energy consumption , 2010, Green Networking '10.

[2]  Karina Mabell Gomez,et al.  Energy-saving framework for wireless access infrastructures , 2013, 2013 IEEE Online Conference on Green Communications (OnlineGreenComm).

[3]  Gerhard Fettweis,et al.  The global footprint of mobile communications: The ecological and economic perspective , 2011, IEEE Communications Magazine.

[4]  Gilbert Micallef,et al.  Cell size breathing and possibilities to introduce cell sleep mode , 2010, 2010 European Wireless Conference (EW).

[5]  Josip Lorincz,et al.  Heuristic approach for optimized energy savings in wireless access networks , 2010, SoftCOM 2010, 18th International Conference on Software, Telecommunications and Computer Networks.

[6]  L. Chiaraviglio,et al.  Optimal Energy Savings in Cellular Access Networks , 2009, 2009 IEEE International Conference on Communications Workshops.

[7]  Marco Ajmone Marsan,et al.  Energy efficient wireless Internet access with cooperative cellular networks , 2011, Comput. Networks.

[8]  Sergey D. Andreev,et al.  Energy efficient communications for future broadband cellular networks , 2012, Comput. Commun..

[9]  Josip Lorincz,et al.  Heuristic Algorithms for Optimization of Energy Consumption in Wireless Access Networks , 2011, KSII Trans. Internet Inf. Syst..

[10]  Karina Mabell Gomez,et al.  MORFEO: Saving energy in wireless access infrastructures , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[11]  Panagiotis Demestichas,et al.  Introduction of cognitive systems in the wireless world — Research achievements and future challenges for end-to-end efficiency , 2010, 2010 Future Network & Mobile Summit.

[12]  Zhisheng Niu,et al.  Energy-Efficient Cellular Network Planning under Insufficient Cell Zooming , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[13]  Lukas Kencl,et al.  Energy savings for cellular network with evaluation of impact on data traffic performance , 2010, 2010 European Wireless Conference (EW).

[14]  Marco Ajmone Marsan,et al.  A simple analytical model for the energy-efficient activation of access points in dense WLANs , 2010, e-Energy.

[15]  Kevin C. Almeroth,et al.  Green WLANs: On-Demand WLAN Infrastructures , 2009, Mob. Networks Appl..

[16]  Haiyun Luo,et al.  Traffic-driven power saving in operational 3G cellular networks , 2011, MobiCom.

[17]  S. E. Elayoubi,et al.  System Selection and Sleep Mode for Energy Saving in Cooperative 2G/3G Networks , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[18]  Abdulbaki Uzun,et al.  Towards a dynamic adaption of capacity in mobile telephony networks using context information , 2011, 2011 11th International Conference on ITS Telecommunications.

[19]  Fabrizio Granelli,et al.  Energino: A hardware and software solution for energy consumption monitoring , 2012, 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt).

[20]  Josip Lorincz,et al.  Energy savings in wireless access networks through optimized network management , 2010, IEEE 5th International Symposium on Wireless Pervasive Computing 2010.

[21]  Loutfi Nuaymi,et al.  Environmental friendly mobile radio networks: Approaches of the European OPERA-Net 2 project , 2013, ICT 2013.

[22]  Marco Ajmone Marsan,et al.  Cell wilting and blossoming for energy efficiency , 2011, IEEE Wireless Communications.

[23]  Shiao-Li Tsao,et al.  A survey of energy efficient MAC protocols for IEEE 802.11 WLAN , 2011, Comput. Commun..

[24]  Marco Ajmone Marsan,et al.  TREND: Toward real energy-efficient network design , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[25]  Sebastian Göndör,et al.  Energy optimisation in heterogeneous multi-RAT networks , 2011, 2011 15th International Conference on Intelligence in Next Generation Networks.

[26]  Mamoun Guenach,et al.  Toward green copper broadband access networks , 2011, IEEE Communications Magazine.

[27]  Tijani Chahed,et al.  Optimal control for base station sleep mode in energy efficient radio access networks , 2011, 2011 Proceedings IEEE INFOCOM.

[28]  Muhammad Ali Imran,et al.  EARTH — Energy Aware Radio and Network Technologies , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[29]  S. Gondor,et al.  Visualizing the effects of power management algorithms for mobile networks under realistic conditions , 2012, 2012 Electronics Goes Green 2012+.

[30]  Vincenzo Mancuso,et al.  On the minimization of power consumption in base stations using on/off power amplifiers , 2011, 2011 IEEE Online Conference on Green Communications.