Mining frequent partial periodic patterns in spectrum usage data

Pei Huang; Chin Jung Liu; Li Xiao; Jin Chen

doi:10.1109/IWQoS.2012.6245969

Mining frequent partial periodic patterns in spectrum usage data

Pei Huang, Chin Jung Liu, Li Xiao, Jin Chen

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.

Original language	English
Title of host publication	2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012
DOIs	https://doi.org/10.1109/IWQoS.2012.6245969
State	Published - 2012
Event	2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012 - Coimbra, Portugal Duration: Jun 4 2012 → Jun 5 2012

Publication series

Name	IEEE International Workshop on Quality of Service, IWQoS
ISSN (Print)	1548-615X

Conference

Conference	2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012
Country/Territory	Portugal
City	Coimbra
Period	6/4/12 → 6/5/12

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/IWQoS.2012.6245969

Cite this

@inproceedings{cb83f51536d64b9184f9fc7d1e75f017,

title = "Mining frequent partial periodic patterns in spectrum usage data",

abstract = "Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.",

author = "Pei Huang and Liu, {Chin Jung} and Li Xiao and Jin Chen",

year = "2012",

doi = "10.1109/IWQoS.2012.6245969",

language = "English",

isbn = "9781467312981",

series = "IEEE International Workshop on Quality of Service, IWQoS",

booktitle = "2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012",

note = "2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012 ; Conference date: 04-06-2012 Through 05-06-2012",

}

Huang, P, Liu, CJ, Xiao, L & Chen, J 2012, Mining frequent partial periodic patterns in spectrum usage data. in 2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012., 6245969, IEEE International Workshop on Quality of Service, IWQoS, 2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012, Coimbra, Portugal, 6/4/12. https://doi.org/10.1109/IWQoS.2012.6245969

TY - GEN

T1 - Mining frequent partial periodic patterns in spectrum usage data

AU - Huang, Pei

AU - Liu, Chin Jung

AU - Xiao, Li

AU - Chen, Jin

PY - 2012

Y1 - 2012

N2 - Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.

AB - Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.

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U2 - 10.1109/IWQoS.2012.6245969

DO - 10.1109/IWQoS.2012.6245969

M3 - Conference contribution

AN - SCOPUS:84866603523

SN - 9781467312981

T3 - IEEE International Workshop on Quality of Service, IWQoS

BT - 2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012

T2 - 2012 IEEE 20th International Workshop on Quality of Service, IWQoS 2012

Y2 - 4 June 2012 through 5 June 2012

ER -

Mining frequent partial periodic patterns in spectrum usage data

Abstract

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