多輸入及多輸出正交分頻多工系統能在無線通訊中增加傳送資料容量。而對於頻率選擇性的衰退通道下，系統需要知道通道的參數資訊，以便於解回傳送的信號。本論文中，我們提出一種使用最佳訓練序列(training sequence)的調適性遞迴最小平方演算法(RLS)去估測通道，以改進其效能及降低複雜度。它是利用先前我們已經計算出的通道參數去幫助我們估測通道，而不去計算複雜度較高的反矩陣。最後，模擬結果也顯示出在較低的信號雜訊比(SNR)和較小的都卜勒頻率下，有較佳的均方誤差(MSE)和位元錯誤率(BER)。 Multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can be used to increase capacity in wireless communication. For the frequency-selective fading channel in wireband system, it is required to know the knowledge of channel parameters. In this thesis, we propose an adaptive recursive least-square (RLS) algorithm using optimum training sequences for channel estimation to improve the performance and reduce the complexity. Instead of tracking a large matrix inversion, we exploit the information of channel parameters that we have calculated to estimate the channel. Simulation results prove that the mean square error (MSE) performance of channel estimation and bit error rate (BER) are better with low signal- to-noise ratio (SNR) and low Doppler frequency.
