Reducing the energy consumption of small cell networks subject to QoE constraints

Small cell networks (SCNs) are widely considered as a promising solution for future cellular deployments. Lately, the benefits of small cells to improve spectrum utilization and the user quality of experience (QoE) have been well documented. In addition, the power consumption of current deployments, for instance due to idle power and cooling equipment, is a major concern for operators. Small cells offer the opportunity for more dynamic power management of base stations, due to coverage overlaps and larger spatio-temporal load fluctuations. Yet, such power management decisions (e.g. turning off a base station) should not lead to excessive performance degradation for users associated with it or additional power consumption. This tradeoff becomes significantly more challenging to evaluate in future networks, due to the diversity of services offered to users beyond the traditional voice calls, as well as the complexity of traffic scheduling algorithms. The goal of this paper is to make a first step towards an analytical investigation of this tradeoff. To this end, we propose a number of QoE constraints that a power management decision should consider, and analytically relate them to key parameters such as user traffic mix, cell load, user density, etc. We then use this framework to perform a preliminary study of the potential energy savings an operator could achieve, while guaranteeing the satisfaction of these constraints. Our results provide some qualitative and quantitative insights on the interesting tradeoff between switch-off duration and number of small cells one can safely switch off.

[1]  Zhisheng Niu,et al.  Cell zooming for cost-efficient green cellular networks , 2010, IEEE Communications Magazine.

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

[3]  Debasis Mitra,et al.  Design of generalized processor sharing schedulers which statistically multiplex heterogeneous QoS classes , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[4]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[5]  Samuli Aalto,et al.  Impact of Size-Based Scheduling on Flow Level Performance in Wireless Downlink Data Channels , 2007, ITC.

[6]  Suresh Singh,et al.  Greening of the internet , 2003, SIGCOMM '03.

[7]  Chee Wei Tan Optimal power control in Rayleigh-fading heterogeneous networks , 2011, 2011 Proceedings IEEE INFOCOM.

[8]  Shlomo Shamai,et al.  Downlink Multicell Processing with Limited-Backhaul Capacity , 2009, EURASIP J. Adv. Signal Process..

[9]  Marco Ajmone Marsan,et al.  Energy-efficient management of UMTS access networks , 2009, 2009 21st International Teletraffic Congress.

[10]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.

[11]  Mérouane Debbah,et al.  Shadowing time-scale admission and power control for small cell networks , 2012, The 15th International Symposium on Wireless Personal Multimedia Communications.

[12]  Alexandre Proutière,et al.  Wireless downlink data channels: user performance and cell dimensioning , 2003, MobiCom '03.

[13]  Giuseppe Piro,et al.  Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey , 2013, IEEE Communications Surveys & Tutorials.

[14]  Marco Ajmone Marsan,et al.  On the effectiveness of single and multiple base station sleep modes in cellular networks , 2013, Comput. Networks.

[15]  Jie Wang,et al.  A New Call Admission Control Strategyfor LTE Femtocell Networks , 2013, CSE 2013.