Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks
Dense cellular networks (DenseNets) are fast becoming a reality with the large scale deployment of base stations aimed at meeting the explosive data traffic demand. In legacy systems, however, this comes at the cost of higher network interference and energy consumption. In order to support network d...
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doaj-8a211855a50c4989939357ecf90760252021-03-29T20:00:42ZengIEEEIEEE Access2169-35362017-01-01545546910.1109/ACCESS.2016.26434417792582Stochastic Geometric Analysis of Energy-Efficient Dense Cellular NetworksArman Shojaeifard0https://orcid.org/0000-0003-0826-1996Kai-Kit Wong1Khairi Ashour Hamdi2Emad Alsusa3Daniel K. C. So4Jie Tang5Department of Electronic and Electrical Engineering, University College London, London, U.K.Department of Electronic and Electrical Engineering, University College London, London, U.K.School of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.School of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.School of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.School of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaDense cellular networks (DenseNets) are fast becoming a reality with the large scale deployment of base stations aimed at meeting the explosive data traffic demand. In legacy systems, however, this comes at the cost of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made “load-proportional” thus allowing certain portions of the network to activate on-demand. In this paper, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The proposed model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communications functionalities. Using this stochastic geometric framework, we identify the most energy-efficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. According to state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments.https://ieeexplore.ieee.org/document/7792582/Network densificationload-proportionalityspatial-correlationsoptimal deployment solutionpower savingssleep modes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Arman Shojaeifard Kai-Kit Wong Khairi Ashour Hamdi Emad Alsusa Daniel K. C. So Jie Tang |
spellingShingle |
Arman Shojaeifard Kai-Kit Wong Khairi Ashour Hamdi Emad Alsusa Daniel K. C. So Jie Tang Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks IEEE Access Network densification load-proportionality spatial-correlations optimal deployment solution power savings sleep modes |
author_facet |
Arman Shojaeifard Kai-Kit Wong Khairi Ashour Hamdi Emad Alsusa Daniel K. C. So Jie Tang |
author_sort |
Arman Shojaeifard |
title |
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks |
title_short |
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks |
title_full |
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks |
title_fullStr |
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks |
title_full_unstemmed |
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks |
title_sort |
stochastic geometric analysis of energy-efficient dense cellular networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2017-01-01 |
description |
Dense cellular networks (DenseNets) are fast becoming a reality with the large scale deployment of base stations aimed at meeting the explosive data traffic demand. In legacy systems, however, this comes at the cost of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made “load-proportional” thus allowing certain portions of the network to activate on-demand. In this paper, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The proposed model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communications functionalities. Using this stochastic geometric framework, we identify the most energy-efficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. According to state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments. |
topic |
Network densification load-proportionality spatial-correlations optimal deployment solution power savings sleep modes |
url |
https://ieeexplore.ieee.org/document/7792582/ |
work_keys_str_mv |
AT armanshojaeifard stochasticgeometricanalysisofenergyefficientdensecellularnetworks AT kaikitwong stochasticgeometricanalysisofenergyefficientdensecellularnetworks AT khairiashourhamdi stochasticgeometricanalysisofenergyefficientdensecellularnetworks AT emadalsusa stochasticgeometricanalysisofenergyefficientdensecellularnetworks AT danielkcso stochasticgeometricanalysisofenergyefficientdensecellularnetworks AT jietang stochasticgeometricanalysisofenergyefficientdensecellularnetworks |
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1724195533496516608 |