此篇論文在處理良好的頻率追蹤在正交分頻多工系統裡。無論是在時域或在頻域中去執行頻率偏移量的估測，執行良好的頻率追蹤通常是使用多個或一個完整的正交多頻分工的試驗區塊或數據訓練。這裡我們所使用的方法是採用單一樣品，而不是採用多個樣品，這問題可以大大的簡化在所收到之頻域中正交分頻多工的區塊資料，且仍然達到合理的追蹤性能。這證明我們的單一樣品的最大似頻率偏移估測量可以是不偏不移的而且均方誤差將更接近CRB，當訊號雜訊比增加。

This thesis deals with fine frequency tracking in orthogonal frequency division multiplexing (OFDM) systems. The usual practice for fine frequency tracking is to use multiple or an entire OFDM block of pilot or training data, either in the time domain or in the frequency domain to perform frequency offset estimation. Here we show that, instead of the elaborate approach of using multiple pilot samples, the problem can be greatly simplified by using only a single pilot sample from a selected time slot in the received time-domain OFDM block to still achieve reasonable tracking performance. It is proven that our single sample ML frequency offset estimator tends to be unbiased and its mean square error (MSE) will approach the Cramer-Rao bound (CRB) as SNR is increased.

CONTENTS

ACKNOWLEDGEMENT	I
CHINESE ABSTRACT	II
ENGLISH ABSTRACT	III
CONTENTS	IV
LIST OF FIGURES	VI
CHAPTER 1 INTRODUCTION	1
CHAPTER 2 MAXIMUM LIKEHOOD ESTIMATION	4
CHAPTER 3 MULTIPATH FADING AND FADING CHANNEL MODEL 	8
            3.1 Multi-path Propagation	8
               3.1.1 Frequency Selective and Nonselective (Flat) Fading	9
               3.1.2 Slow and Fast Fading Channel	9
            3.2 Fading Models	10
               3.2.1 Rayleigh Fading Model	10
               3.2.2 Ricean Fading	12
CHAPTER 4 THE OFDM SYSTEM	13
            4.1 The Multi-carrier System Model	13
CHAPTER 5 ACQUISITION AND TRACKING IN OFDM	18
CHAPTER 6 ML TRACKING TECHNIQUE BASED ON
A SINGLE TIME SLOT SAMPLE	20
6.1 Signal model	20
6.2 ML Frequency Tracking Based on 
Signal Time Slot Sample	21
CHAPTER 7 SIMULATION RESULTS	25
CHAPTER 8 CONCLUSION	28
REFERENCE	29

LIST OF FIGURE

Figure 4.1   The OFDM system structure	17
Figure 7.1   (a) Estimator variances vs. SNR of  	26
Figure 7.1   (b) Estimator bias vs. SNR of  	26
Figure 7.2   (a) Estimator variances vs. SNR of  	27
Figure 7.2   (b) Estimator bias vs. SNR of  	27

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