Energy efficiency of small cell backhaul networks based on Gauss–Markov mobile models
- Author(s): Xiaohu Ge 1 ; Song Tu 1 ; Tao Han 1 ; Qiang Li 1 ; Guoqiang Mao 2, 3
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View affiliations
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Affiliations:
1:
School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China;
2: School of Computing and Communication, The University of Technology Sydney, Sydney, Australia;
3: National ICT Australia (NICTA), Sydney, Australia
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Affiliations:
1:
School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China;
- Source:
Volume 4, Issue 2,
March 2015,
p.
158 – 167
DOI: 10.1049/iet-net.2014.0081 , Print ISSN 2047-4954, Online ISSN 2047-4962
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(http://creativecommons.org/licenses/by-nc/3.0/)
To satisfy the recent growth of mobile data usage, small cells are recommended to deploy into conventional cellular networks. However, the massive backhaul traffic is a troublesome problem for small cell networks, especial in wireless backhaul transmission links. In this study, backhaul traffic models are first presented considering the Gauss–Markov mobile models of mobile stations in small cell networks. Furthermore, an energy efficiency model of small cell backhaul networks with Gauss–Markov mobile models has been proposed. Numerical results indicate that the energy efficiency of small cell backhaul networks can be optimised by trade-off the number and radius of small cells in cellular networks.
Inspec keywords: telecommunication power management; telecommunication traffic; energy conservation; cellular radio; Gaussian processes; radio links; Markov processes
Other keywords: massive backhaul traffic; Gauss-Markov mobile model; small cell backhaul network energy efficiency; cellular network; cells radius; mobile station; wireless backhaul transmission link
Subjects: Mobile radio systems; Electrical/electronic equipment (energy utilisation); Probability theory, stochastic processes, and statistics; Markov processes
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