Energy consumption comparison between macro-micro and public femto deployment in a plausible LTE network

We study the energy consumptions of two strategies that increase the capacity of an LTE network: (1) the deployment of redundant macro and micro base stations by the operator at locations where the traffic is high, and (2) the deployment of publicly accessible femto base stations by home users. Previous studies show the deployment of publicly accessible residential femto base stations is considerably more energy efficient; however, the results are proposed using an abstracted model of LTE networks, where the coverage constraint was neglected in the study, as well as some other important physical and traffic layer specifications of LTE networks. We study a realistic scenario where coverage is provided by a set of non-redundant macro-micro base stations and additional capacity is provided by redundant macro-micro base stations or by femto base stations. We quantify the energy consumption of macro-micro and femto deployment strategies by using a simulation of a plausible LTE deployment in a mid-size metropolitan area, based on data obtained from an operator and using detailed models of heterogeneous devices, traffic, and physical layers. The metrics of interest are operator-energy-consumption/total-energy-consumption per unit of network capacity. For the scenarios we studied, we observe the following: (1) There is no significant difference between operator energy consumption of femto and macro-micro deployment strategies. From the point of view of society, i.e. total energy consumption, macro-micro deployment is even more energy efficient in some cases. This differs from the previous findings, which compared the energy consumption of femto and macro-micro deployment strategies, and found that femto deployment is considerably more energy efficient. (2) The deployment of femto base stations has a positive effect on mobile-terminal energy consumption; however, it is not significant compared to the macro-micro deployment strategy. (3) The energy saving that could be obtained by making macro and micro base stations more energy proportional is much higher than that of femto deployment.

[1]  Lorenzo Donatiello,et al.  Performance Evaluation of Computer and Communication Systems , 1993, Lecture Notes in Computer Science.

[2]  Khaled Ben Letaief,et al.  Multiuser OFDM with adaptive subcarrier, bit, and power allocation , 1999, IEEE J. Sel. Areas Commun..

[3]  M. Bjelica,et al.  Computer simulation in teaching fundamentals of communications: pro et contra , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[4]  Milan Z. Bjelica,et al.  Performance analysis of a novel QoS negotiation procedure , 2003, 6th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service, 2003. TELSIKS 2003..

[5]  Milan Bjelica CALCULATION OF GAUSSIAN PULSE PASSING THROUGH FIBERS WITH POSITIVE AND NEGATIVE DISPERSION , 2003 .

[6]  Zoran Petrovi Correlation-Based Similarity Measure for Multimedia Services With QoS Support MILAN BJELICA AND , 2004 .

[7]  Alexandre Proutière,et al.  On performance bounds for the integration of elastic and adaptive streaming flows , 2004, SIGMETRICS '04/Performance '04.

[8]  Donald F. Towsley,et al.  A study of the coverage of large-scale sensor networks , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[9]  Reinaldo A. Valenzuela,et al.  Coordinating multiple antenna cellular networks to achieve enormous spectral efficiency , 2006 .

[10]  M. Vojnovic,et al.  The Random Trip Model: Stability, Stationary Regime, and Perfect Simulation , 2006, IEEE/ACM Transactions on Networking.

[11]  Thomas Bonald,et al.  Insensitive queueing models for communication networks , 2006, valuetools '06.

[12]  Mung Chiang,et al.  Power Control in Wireless Cellular Networks , 2008, Found. Trends Netw..

[13]  Milan Bjelica,et al.  A Novel Service Retrieval Scheme , 2007, IEEE Communications Letters.

[14]  Milan Bjelica Experiment with User Modeling for Communication Service Retrieval , 2008, IEEE Communications Letters.

[15]  Thomas Bonald,et al.  Capacity Gains of Some Frequency Reuse Schemes in OFDMA Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Geoffrey Ye Li,et al.  Interference-Aware Energy-Efficient Power Optimization , 2009, 2009 IEEE International Conference on Communications.

[17]  V. Srinivasa Rao,et al.  Femtocells: Opportunities and Challenges for Business and Technology , 2009 .

[18]  Albrecht J. Fehske,et al.  Energy Efficiency Improvements through Micro Sites in Cellular Mobile Radio Networks , 2009, 2009 IEEE Globecom Workshops.

[19]  Min Young Chung,et al.  Power Control for Soft Fractional Frequency Reuse in OFDMA System , 2010, ICCSA.

[20]  Hans-Otto Scheck ICT & wireless networks and their impact on global warming , 2010, 2010 European Wireless Conference (EW).

[21]  Shraddha Jadhav,et al.  Accounting for the energy consumption of personal computing including portable devices , 2010, e-Energy.

[22]  Farrukh Nagar,et al.  Leveraging Advances in Mobile Broadband Technology to Improve Environmental Sustainability , 2013 .