Probabilistic small-cell caching: Performance analysis and optimization

Chen, Y; Ding, M; Li, J; Lin, Z; Mao, G; Hanzo, L

Probabilistic small-cell caching: Performance analysis and optimization

Chen, Y Ding, M Li, J Lin, Z Mao, G

Hanzo, L

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2017, 66 (5), pp. 4341 - 4354
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Ding, M	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Lin, Z	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.contributor.author	Hanzo, L	en_US
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2017, 66 (5), pp. 4341 - 4354	en_US
dc.identifier.issn	0018-9545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125308
dc.description.abstract	© 2016 IEEE. Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macrocell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) The SBSs are always active, which is referred to as the always-on architecture; and 2) the SBSs are activated on demand by mobile users (MUs), which is referred to as the dynamic on-off architecture. We focus our attention on the probability that MUs can successfully download content from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities bymaximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy, characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on-off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement, compared with the benchmark schemes.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102030
dc.relation	http://purl.org/au-research/grants/arc/DP110100538
dc.relation.ispartof	IEEE Transactions on Vehicular Technology	en_US
dc.relation.isbasedon	10.1109/TVT.2016.2606765	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Automobile Design & Engineering	en_US
dc.title	Probabilistic small-cell caching: Performance analysis and optimization	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	66	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1003 Industrial Biotechnology	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access	*
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	66	en_US

Abstract:

© 2016 IEEE. Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macrocell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) The SBSs are always active, which is referred to as the always-on architecture; and 2) the SBSs are activated on demand by mobile users (MUs), which is referred to as the dynamic on-off architecture. We focus our attention on the probability that MUs can successfully download content from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities bymaximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy, characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on-off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement, compared with the benchmark schemes.

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http://hdl.handle.net/10453/125308