An Energy Saving Scheme Based on Evolutionary Potential Power Allocation

Article Preview

Abstract:

According to SMART 2020, Internet and ICT (Information and communication technology) could reduce emissions by 15 percent in annual energy costs by 2020. One of the main methods to reduce the energy costs in communication system is to reduce the transmitting power of eNBs. In our study, we explore the problem of downlink energy saving for LTE/LTE-A network based on Orthogonal Frequency Division Multiplexing (OFDM). We propose an adaptive eNB transmitting power allocation scheme based on inter-eNB coordination. This power allocation scheme works in distributive way by being formulated as an evolutionary potential game. Numerical results prove that our proposed algorithm notably reduces the overall transmitting power, while throughputs of either overall or edge users are guaranteed at the same time.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

56-62

Citation:

Online since:

August 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] CMCC 3GPP Contribution RP-100674. Updated Study Item Proposal: Network Energy Saving for E-UTRAN. 3GPP TSG RAN#48. Seoul, Korea. June, (2010).

Google Scholar

[2] SMART 2020: Enabling the low carbon economy in the information age. A report by The Climate Group on behalf of the Global e-Sustainability Initiative (GeSI), (2008).

Google Scholar

[3] 3GPP TS 36. 300 v8. 8. 0. Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network Technical Specification. (2009).

DOI: 10.3403/bsiso17515

Google Scholar

[4] Eun-Sun Jung, Nitin H. Vaidya. Improving IEEE 802. 11 power saving mechanism. Wireless Networks 2008, vol. 14: pp.375-391.

DOI: 10.1007/s11276-006-0726-6

Google Scholar

[5] Junfeng Xiao. An Enhanced Energy Saving Mechanism in IEEE 802. 16e. IEEE GLOBECOM (2006).

Google Scholar

[6] Mugen Peng, Wenbo Wang. An Adaptive Energy Saving Mechanism in the Wireless Packet Access Network. IEEE WCNC (2008).

Google Scholar

[7] D. Monderer, L. Shapley. Potential games. Games and Economics Behavior 1996; vol. 14: 124-143.

DOI: 10.1006/game.1996.0044

Google Scholar

[8] M. Voorneveld. Best-response potential games. Economics letters 2000, vol. 66: pp.289-295.

DOI: 10.1016/s0165-1765(99)00196-2

Google Scholar

[9] P. Dubey, O. Haimanko, A. Zapechelnyuk. Strategic complements and substitutes and potential games. Games and Economics Behavior 2006, vol. 54: pp.77-94.

DOI: 10.1016/j.geb.2004.10.007

Google Scholar

[10] Kennedy, J., Eberhart, R. C. Particle swarm optimization. Proceedings of the IEEE International Conference Neural Networks 1995, Vol. 4: pp.1942-48.

Google Scholar

[11] C. Lacatus and D. C. Popescu. Adaptive interference avoidance for dynamic wireless systems: a game theoretic approach. IEEE Journal on Select Areas in Communications 2007, vol. 1: pp.189-202.

DOI: 10.1109/jstsp.2007.897060

Google Scholar

[12] J. O. Neel. Analysis and design of cognitive radio networks and distributed radio resource management algorithms. Ph.D. dissertation, Virginia Polytechnic and State University, (2006).

Google Scholar

[13] P. Pischella, J.C. Belfiore, Power Control in Distributed Cooperative OFDMA Cellular Networks. IEEE Transactions on Wireless Communication 2008, vol. 7: pp.1900-05.

DOI: 10.1109/twc.2008.061039

Google Scholar

[14] Utku Ozan Candogan, Ishai Menache, Asuman Ozdaglar, et al. Near-optimal power control in wireless networks: a potential game approach. IEEE INFOCOM (2010).

DOI: 10.1109/infcom.2010.5462017

Google Scholar

[15] TR 25. 892, Feasibility Study for Orthogonal Frequency Division Multiplexing (OFDM) for UTRAN enhancement". R3-101458, 3GPP RAN 3, "Consideration on Inter-RAT energy saving.

Google Scholar