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A Low Complexity PTS Technique using Threshold for PAPR Reduction in OFDM Systems

Title
A Low Complexity PTS Technique using Threshold for PAPR Reduction in OFDM Systems
Author
이병호
Keywords
Traffic classification; unsupervised learning; k-nearest neighbor; clustering
Issue Date
2012-09
Publisher
한국인터넷정보학회
Citation
KSII Transactions on Internet and Information Systems, Sep 2012, 6(9), P.2191-2201, 11P.
Abstract
Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.
URI
http://www.itiis.org/digital-library/manuscript/396http://hdl.handle.net/20.500.11754/49198
ISSN
1976-7277
DOI
10.3837/tiis.2012.09.012
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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