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Blind identification of digital communication signals based on statistics of directional data

Published: 29 July 2008 Publication History

Abstract

This paper presents a new clustering algorithm to solve the blind identification problem of digital communication signal modulation types. the algorithm utilized the instantaneous frequency and instantaneous phase of sampling circular statistical data as a training sample to extract classification feature based on the statistical theory of directional data. And use the characteristic parameters to achieve a variety of different types of communication signals in the modulation recognition on the two-dimensional plane. This method is not only able to make identification classes, but also to achieve recognition subclasses. Simulation results show that the algorithm simple and efficient, and high in accuracy, robustness better and has strong practicality and feasibility.

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  1. Blind identification of digital communication signals based on statistics of directional data

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    cover image ACM Other conferences
    ICAIT '08: Proceedings of the 2008 International Conference on Advanced Infocomm Technology
    July 2008
    677 pages
    ISBN:9781605580883
    DOI:10.1145/1509315
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 29 July 2008

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    Author Tags

    1. clustering feature
    2. feature exaction
    3. pattern recognition
    4. sample trigonometric moments
    5. statistics of directional data

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    ICAIT '08 Paper Acceptance Rate 89 of 151 submissions, 59%;
    Overall Acceptance Rate 122 of 207 submissions, 59%

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