Skip to main content Accessibility help
×
Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-19T21:13:04.666Z Has data issue: false hasContentIssue false

1 - Fundamental trade-offs on the design of green radio networks

from Part I - Communication architectures and models for green radio networks

Published online by Cambridge University Press:  05 August 2012

Yan Chen
Affiliation:
Huawei Technologies, China
Shunqing Zhang
Affiliation:
Huawei Technologies, China
Shugong Xu
Affiliation:
Huawei Technologies, China
Ekram Hossain
Affiliation:
University of Manitoba, Canada
Vijay K. Bhargava
Affiliation:
University of British Columbia, Vancouver
Gerhard P. Fettweis
Affiliation:
Technische Universität, Dresden
Get access

Summary

Introduction

There is currently a global concern about the rise in the emission of pollutants and energy consumption. The carbon dioxide (CO2) footprint of the information and communications technologies (ICT) industry, as pointed out by [1], is 25% of the 2007 carbon footprint for cars worldwide, which is similar to that of the whole aviation industry. Within the ICT industry, the mobile network is recognized as being among the biggest energy users. The exponentially growing data traffic in mobile networks has made the issue an even grander challenge in the future. In a data forecast report provided by Cisco [2], it has been pointed out that the global mobile data traffic will increase 26-fold between 2010 and 2015. In particular, unexpectedly strong growth in 2010 has been observed mainly due to the accelerated adoption of smartphones. For instance, China Unicom's 3G traffic increased 62% in a single quarter from Q1 to Q2 of 2010, while AT&T reported a 30-fold traffic growth from Q3 2009 to Q3 2010. The unprecedented expansion of wireless networks will result in a tremendous increase in energy consumption, which will further leave a significant environmental footprint. Therefore, it is now a practical issue and demanding challenge for mobile operators to maintain sustainable capacity growth and, at the same time, to limit the electricity bill. For instance, Vodafone Group has announced the goal of reducing its CO2 emissions by 50% against its 2007 baseline of 1.23 million tonnes, by the year of 2020 [3].

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] G. P., Fettweis and E., Zimmermann, “ICT energy consumption - trends and challenges,” in Proc. of 11th International Symposium on Wireless Personal Mulitimedia Communications, Lapland, Finland, Sept. 2008.Google Scholar
[2] Cisco, , “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2010 C2015,” in White Paper, Feb. 2011.Google Scholar
[3] www.vodafone.com/start/media_relations/news/group_press_releases/2007/01.html
[4] Huawei Technologies Co., Ltd., “Improving energy efficiency, lower CO2 emission and TCO,” in Energy Efficiency Solution White Paper. [Online] Available: www.huawei.com/en/static/hw-076768.pdf
[5] R., Irmer, “Evolution of LTE - operator requirements and some potential solutions,” in Proc. of 5th International FOKUS IMS Workshop, Berlin, Germany, Nov. 2009.Google Scholar
[6] R., Tafazolli, “EARTH - energy aware radio and network technologies,” in Proc. of Next Generation Wireless Green Networks Workshop, Paris, France, Nov. 2009.Google Scholar
[7] L., Correia, et al., “Challenges and enabling technologies for energy aware mobile radio networks,” IEEE Communications Magazine, vol. 48, no. 11, pp. 30–37, Nov. 2010.Google Scholar
[8] C., Han, et al., “Green radio: radio techniques to enable energy-efficient wireless networks,” IEEE Communications Magazine, vol. 48, no. 11, pp. 46–56, Nov. 2010.Google Scholar
[9] Y., Chen, et al., “Fundamental trade-offs on green wireless communications,” IEEE Communications Magazine, vol. 49, no. 6, Jun. 2011.Google Scholar
[10] C. E., Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, pp. 379–423, 1948.Google Scholar
[11] L., Kleinrock, Queueing Systems Volume I: Theory. John Wiley & Sons, 1975.Google Scholar
[12] D. P., Bertsekas, Dynamic Programming - Deterministic and Stochastic Models. New Jersey, NJ, USA; Prentice Hall, 1987.Google Scholar
[13] O., Arnold, et al., “Power-consumption modeling of different base station types in heterogeneous cellular networks,” in Proc. of Future Network and Mobile Summit, 2010.Google Scholar
[14] S., Cui, A. J., Goldsmith, and A., Bahai, “Energy-constrained modulation optimization,” IEEE Trans. Wireless Commun., vol. 4, no. 5, pp. 2349–2360, Sept. 2005.Google Scholar
[15] G., Miao, et al., “Cross-layer optimization for energy-efficient wireless communications: a survey,” Wiley Journal Wireless Communications and Mobile Computing, vol. 9, pp. 529–542, Apr. 2009.Google Scholar
[16] G., Miao, et al., “Interference-aware energy-efficient power optimization,” in Proc. of IEEE International Communications Conference (ICC), Dresden, Germany, Jun. 2009.Google Scholar
[17] Y., Chen, S., Zhang, and S., Xu, “Impact of non-ideal efficiency on bits per joule performance of base station transmissions,” in Proc. of IEEE Vechicular Technology Conference (VTC), May 2011.Google Scholar
[18] Y., Chen, S., Zhang, and S., Xu, “Characterizing energy efficiency and deployment efficiency relations for green architecture design,” in Proc. of IEEE International Communications Conference (ICC), Cape Town, South Africa, May 2010.Google Scholar
[19] H., Kim, et al., “A cross-layer approach to energy efficiency for adaptive MIMO systems exploiting spare capacity,” IEEE Trans. Wireless Commun., vol. 8, no. 8, pp. 4264–4275, Aug. 2009.Google Scholar
[20] J., Mitola and G. Q., Maguire, “Cognitive radio: making software radios more personal,” IEEE Personal Commun. Mag., vol. 6, no. 4, pp. 13–18, Jun. 1999.Google Scholar
[21] S., Zhang, Y., Chen, and S., Xu, “Improving energy efficiency through bandwidth, power, and adaptive modulation,” in Proc. of IEEE Vechicular Technology Conference (VTC), Ottawa, Canada, Sept. 2010.Google Scholar
[22] S., Zhang, Y., Chen, and S., Xu, “Joint bandwidth-power allocation for energy efficient transmission in multi-user systems,” in Proc. of IEEE Globe Com, Nov. 2010.Google Scholar
[23] D., Grace, et al., “Using cognitive radio to deliver green communications,” in Proc. of 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Jun. 2009.Google Scholar
[24] E., Uysal-Biyikoglu, B., Prabhakar, and A. E., Gamal, “Energy-efficient packet transmission over a wireless link,” IEEE/ACM Trans. Netw., vol. 10, no. 4, pp. 487–499, Aug. 2002.Google Scholar
[25] V., Lau, “Delay optimal cross-layer design for SDMA/OFDMA systems – stochastic decomposition and stochastic learning,” Invited Talk, Stanford 2009. [Online]. Available: www.ece.ust.hk/eeknlau/HKUST-Office-HomePage/Books_Papers_and_Patents_files/Delay-Optimal-Cross-Layer-Design-v1.0.pdfGoogle Scholar
[26] D., Wu and R., Negi, “Effective capacity: a wireless link model for support of quality of service,” IEEE Transactions on Wireless Communications, vol. 2, no. 4, pp. 630–643, Jul. 2003.Google Scholar
[27] D. S. W., Hui, V. K. N., Lau, and H. L., Wong, “Cross-layer design for OFDMA wireless systems with heterogeneous delay requirements,” IEEE Transactions on Wireless Communications, vol. 6, no. 8, pp. 2872–2880, Aug. 2007.Google Scholar
[28] R. A., Berry and R., Gallager, “Communication over fading channels with delay constraints,” IEEE Trans. Inf. Theory, vol. 48, no. 5, pp. 1135–1148, May 2002.Google Scholar
[29] M. J., Neely, “Optimal energy and delay trade-offs for multi-user wireless downlinks,” IEEE Trans. Inf. Theory, vol. 53, no. 9, pp. 3095–3113, Sept. 2007.Google Scholar
[30] V., Lau and Y., Chen, “Delay-optimal power and precoder adaptation for multi-stream mimo systems,” IEEE Trans. Wireless Commun., vol. 8, no. 6, pp. 3104–3111, Jun. 2009.Google Scholar
[31] K., Johansson, “Cost-effective deployment strategies for heterogeneous wireless networks,” Ph.D. dissertation, KTH Information and Communication Technology, Stockholm, Sweden, Nov. 2007.Google Scholar
[32] H., Claussen, L. T. W., Ho, and L. G., Samuel, “Financial analysis of a pico-cellular home network deployment,” in Proc. of IEEE International Communications Conference (ICC), Glasgow, Scotland, Jun. 2007.Google Scholar
[33] B., Badic, et al., “Energy efficiency radio access architectures for green radio: large versus small cell size deployment,” in Proc. of IEEE 70th Vehicular Technology Conference (VTC Fall), Anchorage, USA, Honolulu, USA, Dec. 2009.Google Scholar
[34] A. J., Febske, F., Richter, and G. P., Fettweis, “Energy efficiency improvements through micro sites in cellular mobile radio networks,” in Proc. of 2nd GreenCom Workshop, parallel with IEEE GLOBECOM, Honolulu, USA, Dec. 2009.Google Scholar
[35] F., Richter, et al., “Traffic demand and energy efficiency in heterogeneous cellular mobile radio networks,” in Proc. of IEEE VTCSpring, Taipei, China, May 2010.Google Scholar
[36] A., Fehske, P., Marsch, and G., Fettweis, “Bit per joule efficiency of cooperating base stations in cellular networks,” in Proc. of IEEE Globecom, GreenComm Workshop, Miami, Dec. 2010.Google Scholar
[37] M. A., Imran and R., Tafazolli, “Energy effiiency analysis of idealized cooperated multipoint communication system (CoMP),” in Proc. of IEEE PIMRC Green Workshop, Istanbul, Turkey, Sept. 2010.Google Scholar
[38] E., Oh, et al., “Toward dynamic energy-efficient operation of cellular network infrastructure,” IEEE Communications Magazine, vol. 49, no. 6, pp. 56–61, Jun. 2011.Google Scholar
[39] 3GPP draft TR 36.927, “Potential solutions for energy saving for E-UTRAN.” [Online]. Available: www.3gpp.org/ftp/Specs/html-info/36927.htm
[40] Z., Niu, et al., “Cell zooming for cost-efficient green cellular networks,” IEEE Communications Magazine, vol. 48, no. 11, Jun. 2010.Google Scholar
[41] M. A., Marsan, et al., “Optimal energy savings in cellular access networks,” in Proc. of IEEE Globecom, GreenCom Workshop, Dec. 2009.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×