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

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/48393

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/48393

Title:	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
Authors:	施博淦;Po-kuan Shih
Contributors:	通訊工程研究所
Keywords:	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;feature-based AMC (FB-AMC);multipath fading channel;autoregressive channel model;high-order statistics;cumulant
Date:	2011-08-31
Issue Date:	2012-01-05 14:53:19 (UTC+8)
Abstract:	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。 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.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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