博碩士論文 985203005 完整後設資料紀錄

DC 欄位	值	語言
DC.contributor	通訊工程學系	zh_TW
DC.creator	施博淦	zh_TW
DC.creator	Po-kuan Shih	en_US
dc.date.accessioned	2011-8-31T07:39:07Z
dc.date.available	2011-8-31T07:39:07Z
dc.date.issued	2011
dc.identifier.uri	http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=985203005
dc.contributor.department	通訊工程學系	zh_TW
DC.description	國立中央大學	zh_TW
DC.description	National Central University	en_US
dc.description.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。	zh_TW
dc.description.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.	en_US
DC.subject	特徵法自動調變辨識	zh_TW
DC.subject	多路徑衰減通道	zh_TW
DC.subject	自回歸通道模型	zh_TW
DC.subject	統計量	zh_TW
DC.subject	高 階統計值	zh_TW
DC.subject	feature-based AMC (FB-AMC)	en_US
DC.subject	multipath fading channel	en_US
DC.subject	autoregressive channel model	en_US
DC.subject	high-order statistics	en_US
DC.subject	cumulant	en_US
DC.title	應用高階統計之特徵法自動調變辨識技術	zh_TW
dc.language.iso	zh-TW	zh-TW
DC.title	A Feature-Based Automatic Modulation Classification Technique Using High-Order Statistics	en_US
DC.type	博碩士論文	zh_TW
DC.type	thesis	en_US
DC.publisher	National Central University	en_US