Modeling energy consumption in cellular networks

In this paper we present a new analysis of energy consumption in cellular networks. We focus on the distribution of energy consumed by a base station for one isolated cell. We first define the energy consumption model in which the consumed energy is divided into two parts: The additive part and the broadcast part. The broadcast part is the part of energy which is oblivious of the number of mobile stations but depends on the farthest terminal, for instance, the energy effort necessary to maintain the beacon signal. The additive part is due to the communication power which depends on both the positions, mobility and activity of all the users. We evaluate by closed form expressions the mean and variance of the consumed energy. Our analytic evaluation is based on the hypothesis that mobiles are distributed according to a Poisson point process. We show that the two parts of energy are of the same order of magnitude and that substantial gain can be obtained by power control. We then consider the impact of mobility on the energy consumption. We apply our model to two case studies: The first one is to optimize the cell radius from the energetic point of view, the second one is to dimension the battery of a base station in sites that do not have access to permanent power supply.

[1]  François Baccelli,et al.  Stochastic Geometry and Wireless Networks, Volume 2: Applications , 2009, Found. Trends Netw..

[2]  Jukka K. Nurminen,et al.  Applicability of different models of burstiness to energy consumption estimation , 2012, 2012 8th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP).

[3]  Athanasios V. Vasilakos,et al.  A Survey of Green Mobile Networks: Opportunities and Challenges , 2012, Mob. Networks Appl..

[4]  Yunnan Wu,et al.  Minimum-energy multicast in mobile ad hoc networks using network coding , 2004, Information Theory Workshop.

[5]  François Baccelli,et al.  Stochastic Geometry and Wireless Networks, Volume 1: Theory , 2009, Found. Trends Netw..

[6]  Laurent Decreusefond,et al.  Stein's method for Brownian approximations , 2012, 1207.3517.

[7]  Xin Wang,et al.  Mobile Ad-Hoc Networks: Applications , 2011 .

[8]  Santanu Kumar Rath,et al.  Mobility Based Clustering Algorithm and the Energy Consumption Model of Dynamic Nodes in Mobile Ad Hoc Network , 2008, 2008 International Conference on Information Technology.

[9]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[10]  François Baccelli,et al.  Stochastic geometry and wireless networks , 2009 .

[11]  Laurent Decreusefond,et al.  Stochastic Modeling and Analysis of Telecom Networks: Decreusefond/Stochastic Modeling and Analysis of Telecom Networks , 2012 .

[12]  Cheng-Xiang Wang,et al.  Energy Efficiency Evaluation of Cellular Networks Based on Spatial Distributions of Traffic Load and Power Consumption , 2013, IEEE Transactions on Wireless Communications.

[13]  Philippe Martins,et al.  Robust methods for LTE and WiMAX dimensioning , 2012, 6th International ICST Conference on Performance Evaluation Methodologies and Tools.

[14]  Minoru Etoh,et al.  Energy Consumption Issues on Mobile Network Systems , 2008, 2008 International Symposium on Applications and the Internet.

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

[16]  Mung Chiang,et al.  Energy–Robustness Tradeoff in Cellular Network Power Control , 2009, IEEE/ACM Transactions on Networking.

[17]  G. Fettweis,et al.  ICT ENERGY CONSUMPTION – TRENDS AND CHALLENGES , 2008 .

[18]  Thanh Tung Vu Spatial models for cellular network planning , 2012 .