應用高階統計之特徵法自動調變辨識技術

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姓名

施博淦(Po-kuan Shih) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

應用高階統計之特徵法自動調變辨識技術
(A Feature-Based Automatic Modulation Classification Technique Using High-Order Statistics)

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摘要(中)

在訊號辨識的領域裡，自動訊號辨識是一門古典的題目。這項技術較常應用在當傳送訊號具有可適性時的情況。針對通過非AWGN通道的訊號進行辨識的工作至今仍是一項困難的挑戰，高階統計法是最常被設計應用於針對此狀況的辨識技術。我們使用高階統計參數來估測通道係數，並使用累計量來設計一個多階層決策架構。我們將討論在靜態和時變通道模型下的演算法差異，並比較在不同接收條件下的辨識率。

摘要(英)

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.

關鍵字(中)

★ 特徵法自動調變辨識
★ 多路徑衰減通道
★ 自回歸通道模型
★ 統計量
★ 高階統計值

關鍵字(英)

★ feature-based AMC (FB-AMC)
★ multipath fading channel
★ autoregressive channel model
★ high-order statistics
★ cumulant

論文目次

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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指導教授

林嘉慶、張大中
(Jia-chin Lin、Dah-chung Chang)

審核日期

2011-8-31

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