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Student Number 985203005
Author Po-kuan Shih(Iղh)
Author's Email Address No Public.
Statistics This thesis had been viewed 465 times. Download 142 times.
Department Communication Engineering
Year 2010
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title A Feature-Based Automatic Modulation Classification Technique Using High-Order Statistics
Date of Defense 2011-06-24
Page Count 59
Keyword
  • autoregressive channel model
  • cumulant
  • feature-based AMC (FB-AMC)
  • high-order statistics
  • multipath fading channel
  • Abstract Automatic modulation classification (AMC) is a classical topic in signal classification field. This technique is often used when the transmitted signals are adaptive. So far, recognition of signals passing through non-AWGN channels is still a hard task. High-order statistics is the most adopted method of being designed for classification in non-AWGN situations. We use high-order statistical parameters to obtain estimated channel coefficients and design a multiple-layered decision structure with cumulants. We will discuss the difference of algorithms for static and time-varying channel models, and compare the classification rate in different receiving conditions.
    Table of Content Contents .................................................................................. III
    List of Figures .......................................................................... V
    List of Tables ........................................................................... VI
    Chapter 1 Introduction ............................................................ 1
    1.1 Background .......................................................................................................... 1
    1.2 Motivation ............................................................................................................ 2
    1.3 Organization ......................................................................................................... 3
    Chapter 2 Classification Algorithm ........................................ 4
    2.1 Performance Measurement ................................................................................... 4
    2.2 Categories of Classification Algorithms .............................................................. 5
    2.3 High-Order Statistics ............................................................................................ 6
    2.3.1 Basic Introduction.......................................................................................... 6
    2.3.2 Gaussian Processes ........................................................................................ 9
    2.4 System Description ............................................................................................ 10
    2.5 AWGN Channels ................................................................................................ 11
    2.6 Multipath Fading Channels ................................................................................ 13
    2.6.1 Signal Model................................................................................................ 14
    2.6.2 Normalized Cumulant Feature ..................................................................... 14
    2.6.3 Received Cumulant...................................................................................... 15
    2.6.4 Channel Preprocessing-Fixed Tap Position ................................................. 16
    Chapter 3 Proposed Algorithm ............................................. 19
    3.1 Preliminary ......................................................................................................... 19
    3.2 HOS Features Selection ..................................................................................... 19
    3.3 Decision Technique ............................................................................................ 21
    3.4 Channel Preprocessing-Most Dominant Path .................................................... 23
    3.5 Time-Varying Multipath Channels .................................................................... 26
    3.5.1 Signal Model................................................................................................ 26
    3.5.2 Received Cumulants .................................................................................... 27
    3.5.3 Modified Algorithm-Partial Statistics ......................................................... 27
    Chapter 4 Simulation Results ................................................ 30
    4.1 AWGN Channels ................................................................................................ 30
    4.2 Static Multipath Channels .................................................................................. 32
    4.2.1 Comparison with Single Cumulant, {BPSK, QPSK} ................................. 33
    4.2.2 Comparison with Single Cumulant, {BPSK, QPSK, 16QAM, 64QAM} ... 35
    4.2.3 Comparison with Decision Tree .................................................................. 37
    4.3 Time-Varying Multipath Channels .................................................................... 43
    4.4 Performance Analysis ........................................................................................ 45
    Chapter 5 Conclusions ........................................................... 46
    Appendix .................................................................................. 47
    A. Signal Model for Static Channels ........................................................................ 47
    B. Signal Model for Time-Varying Channels .......................................................... 53
    Bibliography ............................................................................ 57
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    Advisor
  • Dah-chung Chang(ij)
  • Jia-chin Lin(Lży)
  • Files
  • 985203005.pdf
  • approve in 2 years
    Date of Submission 2011-08-31

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