Open AccessDual‐threshold sleep mode control scheme for small cells
Author(s) -
Yu Guanding,
Chen Qimei,
Yin Rui
Publication year - 2014
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2013.0831
Subject(s) - sleep mode , computer science , dual (grammatical number) , markov chain , heuristic , reinforcement learning , energy consumption , base station , quality of service , optimal control , mode (computer interface) , q learning , efficient energy use , markov process , homogeneous , mathematical optimization , computer network , artificial intelligence , mathematics , machine learning , power (physics) , power consumption , engineering , art , physics , literature , quantum mechanics , operating system , statistics , combinatorics , electrical engineering
Sleep mode control is essential to the energy efficiency of small cell networks. However, frequently switching on/off small cell base stations (SBSs) may cause the degradation to the quality‐of‐service of their users and the increase of network operational cost as well. In this study, the authors propose a novel dual‐threshold‐based sleep mode control strategy for small cell networks. The motivation of using dual‐thresholds to control the sleep mode is to minimise the network energy consumption while avoiding the frequent mode transitions of SBSs at the same time. They utilise the Markov chain method to analyse the performance of the proposed strategy. Optimisation problems are formulated to achieve the optimal dual‐thresholds for two different scenarios: the homogeneous threshold scenario in which uniform dual‐thresholds are applied to all SBSs and the heterogeneous threshold scenario where different dual‐thresholds are assigned to SBSs. For the homogeneous threshold scenario, they develop an optimal solution which is based on exhaustive searching. A reinforcement learning‐based algorithm and a heuristic algorithm are proposed for the heterogeneous threshold scenario, respectively. Simulation results are presented to demonstrate the performance of the author's proposed algorithms.