此篇論文所討論的是在如何在正交分頻多工系統裡處理精確的頻率追蹤。對精確的頻率追蹤而言，一般常使用一個或多個完整的正交多頻分工的導航區塊或是訓練資料，在時域或頻域之中來執行頻率偏移的估測。這裡我們所探討的方法是使用在時域中所接收到正交分頻多工的區塊資料中挑選一個單一時隙樣本，而不是採用多重樣本。這樣可以大大的簡化計算複雜度，而且仍然可以達到合理的頻率追蹤效能。我們的單一樣本的最大頻率偏移估計器可被證實是不偏的，當訊號雜訊比增加時，它的均方誤差將趨近於CRB。 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.
