Recently, several approaches were followed for the enhancement and better resource utilization in mobile networks; this is to achieve energy efficient consumption for production and delivery of an information bit. Using Cognitive Femto cells (as a member of the small base stations’ family) proves that, it is an efficient solution for achieving this goal[1]. The use of Energy Efficiency term η has become one of the major indices for measuring the performance of these systems. ηis the measure of the overall system Capacity (C) in bps/Hz versus the Consumed Energy (E) in Joules [2]. In consistence with many researches, analytic models and empirical measurements, η will be investigated throughout the course of this work. Cognitive Base Stations (CBS) (as an element of the system model) which performs the traffic offloading operations is proved to enhance ηperformance. In this work, a combination of both analytic and simulation models are used to construct a practical system model. The obtained model is then used to illustrate the effect of different operational parameters that are involved in the ηproblem. On the other hand, the current paper tries to focus on the selection criteria that may be used to design the cooperative cognitive networks in order to achieve the best ηindices. Both of CBSs radii as well as the inter-separation distances (between CBSs and MBS location) are examined to obtain best ηindex for different operation scenarios; in addition, both of capacity and energy consumption are taken into consideration based on practical operating measures. This work proposed several nonlinear equations with fixed parameters to be used by field engineers to achieve the results with minimum reduced computation complexity. So, the current work may be of importance for the regulator bodies as well as the cognitive mobile operators.
References
[1]
Correia, L.M., Zeller, D., Blume, O. and Ferling, Y. (2010) Challenges and Enabling Technologies for Energy Aware Mobile Radio Networks. IEEE Communications Magazine Special Issue on Green Radio, 48, 66-72.
http://dx.doi.org/10.1109/MCOM.2010.5621969
[2]
ITU (2014) Energy Efficiency Metrics and Measurement Methods for Telecommunication Equipment L.1310, “Pre-Published Recommendations”. Telecommunication Standardization Sector of ITU.
[3]
Gruber, M., Blume, O., Ferling, D., Zeller, D., Imran, M.A. and Calvanese-Strinati, E. (2009) EARTH—Energy Aware Radio and Network. IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo, 13-16 September 2009, 1-5. http://dx.doi.org/10.1109/pimrc.2009.5449938
[4]
Hossain, E., Bhargava, V.K. and Fettw, G.P. (2012) Green Radio Communication Networks. Cambridge University Press, United Kingdom. http://dx.doi.org/10.1017/CBO9781139084284
[5]
EARTH (Energy Aware Radio and Network Technologies—August 2014). EARTH.
https://www.ict-earth.eu/publications/deliverables/deliverables.html.
[6]
Green Network Technologies INFSO-ICT-247733, Deliverable D3.2, EARTH.
https://bscw.ict-earth.eu/pub/bscw.cgi/d70460/EARTH_WP3_D3.2.pdf.
[7]
FP7-ICT-2009-4-247733-EARTH Book (2012) EU Funded Research Project—January 2010 to June 2012, EARTH (Energy Aware Radio and Network Technologies). https://www.ict-earth.eu.
[8]
FP7 in Brief (2014) Office for Official Publications of the European Communities, Luxembourg.
http://ec.europa.eu/research/fp7/pdf/fp7-inbrief_en.pdf.
[9]
7th Framework Programme for Research and Technological Development (2014) European Commission, Research & Innovation. http://ec.europa.eu/research/fp7/understanding/fp7inbrief/funding-schemes_en.html
[10]
Magnus Olsson, E.A. (2012) A Methodology to Evaluate Radio Network Energy Efficiency at System Level. 1st ETSI TC EE Workshop, Genoa, Italy.
[11]
TR 36.814, Further Advancements for E-UTRA Physical Layer Aspects RAN1 (2010) 3GPP in 2EPS.
http://www.in2eps.com/3g36/tk-3gpp-36-814.html.
[12]
Auer, G., Giannini, V., Godor, I., Skillermark, P., Olsson, M., Imran, M.A., et al. (2011) Cellular Energy Efficiency Evaluation Framework. Proceedings of the IEEE 73rd Vehicular Technology Conference, Yokohama, 15-18 May 2011, 1-6. http://dx.doi.org/10.1109/VETECS.2011.5956750
[13]
Gür, G., Bayhan, S. and Alagöz, F. (2013) Energy Efficiency Impact of Cognitive Femtocells in Heterogeneous Wireless Networks. Proceedings of the 1st ACM Workshop on Cognitive Radio Architectures for Broadband—CRAB’13, Miami, 4 October 2013, 53-60. http://dx.doi.org/10.1145/2508478.2508480
[14]
Bayhan, S. and Alagoz, F. (2012) Scheduling in Centralized Cognitive Radio Networks for Energy Efficiency. IEEE Transactions on Vehicular Technology, 62, 582-595. http://dx.doi.org/10.1109/TVT.2012.2225650
[15]
Goldsmith, A. (2005) Wireless Communication. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511841224
[16]
Nandy, S.C. Voronoi Diagrams. Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata 700108. http://www.tcs.tifr.res.in/~ghosh/subhas-lecture.pdf
FP7 Project Management (2014) Eurescom Archive Website, July 2014.
http://archive.eurescom.eu/services/project_management/default.asp
[19]
Abate, Z. (2009) WiMax RF Systems Engineering. Artech House, Boston and London.
[20]
Gallager, R.G. (2001) Claude E. Shannon: A Retrospective on His Life, Work, and Impact. IEEE Transactions on Information Theory, 47, 2681-2695. http://dx.doi.org/10.1109/18.959253
[21]
ETSI TR 136 913 V9.0.0, LTE; (2010-2012) Requirements for Further Advancements for Evolved Universal Terrestrial Radio Access (E-UTRA) (LTE-Advanced). Centre, 3GPP Mobile Competence.
[22]
D6.13.7: Test Scenarios and Calibration Cases Issues 2. (2006) World Initiative New Radio (WINNER II), Deliverable, IST-4-027756.
[23]
Requirements Related to Technical Performance for IMT-Advanced Radio Interface(s), M.2134, Report ITU-R. (2008).
https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2134-2008-PDF-E.pdf