近年來，正交多頻分工系統(Orthogonal Frequency Division Multiplexing ,
OFDM)是越來越受到重視的技術，在本篇論文中將以此系統和感知無線電
(Cognitive Radios , CR)結合並加以討論。
在論文中吾人主要的目標是要在一頻帶中搜尋未被其他系統佔據使用的頻
率，並改善多路徑衰減(Multi-path fading)對搜尋過程所造成的影響.為了偵測其他
系統所在頻率，吾人使用兩個步驟：步驟一，使用自回歸模型(Autoregressive Model)並結合最大概似法(Maximum Likelihood , ML)去估計其他系統粗略的頻帶位置；步驟二，使用偵測器並根據步驟一所估計的結果，修正並補償步驟一因多路徑衰減所造成的失真。之後吾人使用大量的電腦模擬去分析並加以探討。

In this thesis, spectrum sensing of an orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) is addressed. The goal is to identify the portions of the spectrum that are unused by primary user systems and other CR systems, called existing user (EU) systems altogether, with the emphasis on conquering the challenge imposed by multipath fading channel. The sensing of EU systems consists of two steps. In the first step, the maximum likelihood (ML) estimates of the frequency bands of EU systems are calculated; in the second step, detection is performed at each suspected band to decide whether an EU system is truly in operation. The idea is that an EU system appears at a segment of continuous subcarriers. This fact can be exploited by employing measurements at a continual subcarriers and executing the sensing along the frequency domain. An autoregressive (AR) model is adopted to track the variation of the received EU signal strength along frequencies. It is shown by simulations that the proposed spectrum sensing algorithm is robust in a severe frequency-selective fading channel.

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