Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Part I Communication architectures and models for green radio networks
- Part II Physical communications techniques for green radio networks
- Part III Base station power-management techniques for green radio networks
- Part IV Wireless access techniques for green radio networks
- 12 Cross-layer design of adaptive packet scheduling for green radio networks
- 13 Energy-efficient relaying for cooperative cellular wireless networks
- 14 Energy performance in TDD-CDMA multi-hop cellular networks
- 15 Resource allocation for green communication in relay-based cellular networks
- Part V Green radio test-bed, experimental results, and standardization activities
- Index
- References
13 - Energy-efficient relaying for cooperative cellular wireless networks
from Part IV - Wireless access techniques for green radio networks
Published online by Cambridge University Press: 05 August 2012
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Part I Communication architectures and models for green radio networks
- Part II Physical communications techniques for green radio networks
- Part III Base station power-management techniques for green radio networks
- Part IV Wireless access techniques for green radio networks
- 12 Cross-layer design of adaptive packet scheduling for green radio networks
- 13 Energy-efficient relaying for cooperative cellular wireless networks
- 14 Energy performance in TDD-CDMA multi-hop cellular networks
- 15 Resource allocation for green communication in relay-based cellular networks
- Part V Green radio test-bed, experimental results, and standardization activities
- Index
- References
Summary
Introduction
The continuously growing demand for ubiquitous network access has led to the rapid development of wireless cellular networks during the last decade. The subscriber number and service traffic in cellular networks have explosively escalated. It is reported that there are now more than 5 billion mobile phone connections worldwide, and more than a billion mobile phone connections have been added globally in just 18 months [1]. The Asia-Pacific region including India and China is the main source of growth, accounting for 47% of global mobile connections at the end of June 2010. The penetration of mobile phones is growing rapidly in developing countries, and the number of subscribers will be astounding, since the world population is expected to reach 9.15 billion in 2050. In parallel with the rapid growth of the number of connections, the service types and traffic load have experienced significant evolvement from voice and short messaging service (SMS) to video and multimedia internet. Such tremendous growth in the information and communication technology (ICT) industry has made it become one of the leading sources of world energy consumption and it is expected to grow dramatically in the future. There are currently more than 4 million base stations (BSs) serving mobile users, each consuming an average of 25 MWh per year. In 2007, four Chinese operators consumed 20 billion KWh, which is equivalent to 8 million tons of coal combustion. ICT already represents around 2%of total carbon emissions, and this is expected to increase from 0.53 billion tonnes (Gt) carbon dioxide equivalent (CO2e) in 2002 to 1.43GtCO2e in 2020 [2].
- Type
- Chapter
- Information
- Green Radio Communication Networks , pp. 286 - 308Publisher: Cambridge University PressPrint publication year: 2012
References
[1] BBC News, “Over 5 billion mobile phone connections worldwide,” [Online] Available: www.bbc.co.uk/news/10569081
[2] The climate group, “Enabling the low carbon economy in the information age,” SMART 2020 Report.
[3] TREND, “Towards real energy-efficient network design,” [Online] Available: www.fp7-trend.eu/
[4] C2POWER, “Cognitive radio and cooperative strategies for power saving in multi-standard wireless devices,” [Online] Available: www.ict-c2power.eu/
[5] EARTH, “Energy aware radio and network technologies,” [Online] Available: https://www.ict-earth.eu/
[6] et al., “Cell zooming for cost-efficient green cellular networks,” IEEE Commun. Magazine, vol. 48, no. 11, pp. 74–79, Nov. 2010.Google Scholar
[7] et al., “Optimal energy savings in cellular access networks,” in IEEE International Conference on Communications Workshops, June 2009, pp. 1–5.Google Scholar
[8] et al., “Green mobile access network with dynamic base station energy saving,” in Proc. of MobiCom'09 (Poster), Sep. 2009.Google Scholar
[9] et al., “Increasing downlink cellular throughput with limited network MIMO coordination,” IEEE Trans. Wireless Commun., vol. 8, no. 6, pp. 2983–2989, June 2009.Google Scholar
[10] et al., “Relay station shared by multiple base stations for intercell interference mitigation,” in IEEE C802.16m-08/1436rl, Nov. 2008.Google Scholar
[11] , , and , “User cooperation diversity part I and part II,” IEEE Trans. Commun., vol. 51, no. 11, pp. 1927–1948, Nov. 2003.Google Scholar
[12] , , and , “Cooperative communication in wireless networks,” IEEE Commun. Magazine, vol. 42, no. 10, pp. 74–80, Oct. 2004.Google Scholar
[13] 3GPP TR 36.814, “Further advancements for E-UTRA physical layer aspects (Release 9),” [Online] Available: www.3gpp.org/ftp/Specs/html-info/36814.htm
[14] and , “Energy-efficient antenna sharing and relaying for wireless networks,” in Proc. of IEEE WCNC'00, Mar. 2000.Google Scholar
[15] , , and , “Power consumption reduction by multi-hop transmission in cellular networks,” in Proc. of IEEE VTC-F'04, Sept. 2004.Google Scholar
[16] and , “NXG04-3: does multi-hop communication extend the battery life of mobile terminals?,” in Proc. of IEEE GLOBECOM'06, Nov. 2006.Google Scholar
[17] and , “A novel relay selection method for decode-and-forward relaying,” in Proc. of Canadian Conference on Electrical and Computer Engineering (CCECE'08), Niagara Falls, ON. May 2008.Google Scholar
[18] , “Maximizing system lifetime in wireless sensor networks,” in Proc. of 4th Int. Symp. Information Processing in Sensor Networks, Apr. 2005.Google Scholar
[19] et al., “Energy-efficient cooperative communication in clustered wireless sensor networks,” in Proc. of IEEE Milcom'06, Oct. 2006.Google Scholar
[20] , , and , “Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios,” IEEE Network, pp. 26–30, June 2010.Google Scholar
[21] et al., “Minimizing energy consumption using cognitive radio,” in Proc. of IEEE International Performance, Computing and Communications Conference (IPCCC'08), Dec. 2008.Google Scholar
[22] and , “User cooperation for energy-efficient cellular communications,” in Proc. of IEEE ICC'10, May 2010.Google Scholar
[23] and , “Selection cooperation in multi-source cooperative networks,” IEEE Trans. Wireless Commun., vol. 7, no. 1, pp. 118–127, Jan. 2008.Google Scholar
[24] et al., “Energy-efficient cooperative relaying over fading channels with simple relay selection,” IEEE Trans. Wireless Commun., vol. 7, no. 8, pp. 3013–3025, Aug. 2008.Google Scholar
[25] and , “Joint optimization of relay strategies and resource allocations in cooperative cellular networks,” IEEE Journal on Selected Areas in Commun., vol. 25, no. 2, pp. 328–339, Feb. 2007.Google Scholar
[26] et al., “Semi-distributed user relaying algorithm for amplify-and-forward wireless relay networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1348–1357, Apr. 2008.Google Scholar
[27] et al., “Relay selection in multi-node cooperative communications: when to cooperate and whom to cooperate with?,” in Proc. of IEEE GLOBECOM'06, Nov. 2006.Google Scholar
[28] and , “A novel relay selection method for decode-and-forward relaying,” in Proc. of Canadian Conference on Electrical and Computer Engineering (CCECE'08), Niagara Falls, ON, May 2008.Google Scholar
[29] et al., “A simple cooperative diversity method based on network path selection,” IEEE Journal on Selected Areas in Commun., vol. 24, no. 3, pp. 659–672, Mar. 2006.Google Scholar
[30] , , and , “Statistical decision making in adaptive modulation and coding for 3G wireless systems,” IEEE Trans. Veh. Tech., vol. 54, no. 6, pp. 2066–2073, Nov. 2005.Google Scholar
[31] , , and , “Distributed optimal relay selection in wireless cooperative networks with finite state Markov channels,” IEEE Trans. Veh. Tech., vol. 59, no. 5, pp. 2149–2158, June 2010.Google Scholar
[32] and , “Restless bandits, linear programming relaxations, and a primal dual index heuristic,” Operations Research, vol. 48, no. 1, pp. 80–90, 2000.Google Scholar
[33] , “Coding performance of hybrid ARQ schemes,” IEEE Trans. Commun., vol. 54, no. 6, pp. 1017–1029, June 2006.Google Scholar
[34] , , and , “Modulation-adaptive cooperation schemes for wireless networks,” in Proc. of IEEE VTC-S'08, May. 2008.Google Scholar
[35] and , “Low-complexitymaximum-likelihood detection of coded signals sent over finite-state Markov channels,” IEEE Trans. Commun., vol. 50, no. 4, pp. 524–531, Apr. 2002.Google Scholar
[36] and , “On verifying the first-order Markovian assumption for a Rayleigh fading channel model,” IEEE Trans. Veh. Tech., vol. 45, no. 2, pp. 353–357, May 1996.Google Scholar
[37] et al., “The HMM-based modeling for the energy level prediction in wireless sensor networks,” in Proc. of IEEE 2nd Conf. on Industrial Electronics and Applications, Harbin, P. R. China, May 2007.Google Scholar
[38] et al., “Transmission scheduling for optimizing sensor network lifetime: a stochastic shortest path approach,” IEEE Trans. Signal Proc., vol. 55, no. 5, pp. 2294–2309, 2007.Google Scholar