摘要

多輸入多輸出(Multiple Input Multiple Output, MIMO)技術可提高無線通訊系統資料傳輸量；正交分頻多工調變(Orthogonal Frequency Division Modulation, OFDM)技術具有高頻譜效率和對抗多重路徑衰減通道的效應；而空時碼(Space-Time Coding, STC)則利用空間分集特性編碼來增加傳送資料量。因此，結合STC和MIMO-OFDM技術已成為第四代寬頻行動通訊的標準技術之ㄧ。本論文針對此MIMO STC-OFDM無線傳輸系統，發展以子空間分解法為基礎的半盲蔽式通道估測技術。
近年來，因高階統計特性的子空間分解法上較為複雜，故利用訊號二階統計特性的子空間分解法進行盲蔽式通道估測受到相當矚目。 此方法是利用雜訊子空間和通道參數矩陣的正交特性來估測通道參數。
本論文首先提出正反向平均法來提升盲蔽式通道估測的效能。此法結合正向和反向的取樣資料，因此，不需額外增加天線數目或者要求傳輸端再傳送一次資料，就能多出一倍的資料量，可以改善原本僅使用正向資料估測法的通道估測誤差，並減少傳送資料的位元錯誤率
。接著，我們針對正反向平均法，其自相關矩陣結構具有置換不變之特性，以及特徵向量對稱的性質，提出快速正反向法(FFB)，此法利用對兩個維度減半的矩陣進行特徵分解，可對應求出正反向平均法的雜訊子空間。此法的估測效能和正反向平均法相同，但計算量僅為正反向平均法的25%。
為了使上述以子空間為基礎的通道估測法能適用於時變的行動通訊環境中，本論文提出適應性雜訊子空間估測法，此方法可以即時調整特徵向量，並找出對應的雜訊子空間，故可達即時性的估測並抵抗時變環境。我們將此適應性雜訊子空間估測法和先前提出的正反向平均法和快速正反向平均法結合，提出適應性正反向估測法和適應性快速正反向估測法。此適應性估測法除了具有正反向平均法的優點，並且可藉由自我調適的能力，提升時變通訊環境下通道估測的效能。模擬結果驗證了本論文所提的正反向平均法和快速正反向平均法的效能優於原來僅使用正向資料的估測法。此外，也証實所提適應性正反向和適應性快速正反向通道估測法，具有即時追蹤時變通道參數的能力。

Multiple-input multiple-output (MIMO) technique can improve the data rate of wireless communication. Orthogonal Frequency Division Modulation (OFDM) technique can alleviate the performance degradation due to multipath interference. Space-Time Code (STC) uses the diversity in space and time domains to increase the transmission data rate. Therefore, the combination of MIMO, STC and OFDM techniques, called the MIMO STC-OFDM system, has become one of standards in the fourth-generation (4G) mobile wideband communication. In the study, we proposed a semiblind channel estimation method based on subspace decomposition for the MIMO STC-OFDM system.
Since the complexity of channel estimation based on high order statistics (HOS), the subspace decomposition method which uses the second order statistics (SOS) has become more popular recently. The method estimates channel parameters by utilizing the orthogonal property that the range space of channel parameter matrix is orthogonal to the noise subspace.
In the study, we first proposed the Forward Backward averaging Estimation (FBE) method to improve the accuracy of channel estimation. We obtained doubled samples by combining the forward and backward sample data. By the way, the FBE can reduce channel estimation error and bit error rate (BER) without setting up extra antennas. Then, we examined the structure of correlation matrix corresponding to the FBE and found the symmetric property of the associated eigenvector. Utilizing the property, we proposed the Fast Forward Backward averaging estimation (FFB) method. The FFB can calculate the noise subspace corresponding to the correlation matrix of FBE by performing eigendecomposition on two half-dimensionality matrices which obtained from the correlation matrix of FBE, individually. The FFB has the same performance as the FBE, but the computation complexity of FFB is one-fourth of FBE.
Next, we proposed adaptive noise subspace estimation method for time-varying channel environment. The adaptive subspace estimation method could adjust eigenvectors in real-time and find out the noise subspace corresponding to the time-varying environment. The adaptive estimation method can alleviate the performance degradation caused by the interference in time-varying environment and reduce the computation complexity in calculating the eigenvectors.
Finally, we proposed Adaptive Forward Backward averaging Estimation (AFBE) method and Adaptive Fast Forward Backward averaging Estimation (AFFBE) method by combining the adaptive noise subspace estimation method with the FBE and FFB methods, respectively. Simulation results validate that the proposed AFBE and AFFBE methods perform better than Adaptive Forward Only Estimation (AFOE) method which combines the adaptive noise subspace estimation method with the Forward Only Estimation method [1]. Furthermore, the AFBE and AFFBE have the capability of tracking the channel parameters of time-varying environments.

中文摘要 ………………………………………………...........................i
英文摘要 ……………………………………………….........................iii
目錄 ………………………………………………………………..……v
圖目錄 ………………………………………………………………....vii
表目錄 ……………………………………………………………….…ix
第一章 序論 ……………………………………………………………1
1.1研究重要性 …………………………...………………….. 1
1.2 研究背景 ………………………………...………………..2
1.2.1 MIMO簡介 ………………………...………………2
1.2.2 OFDM簡介 …………….…………………………..2
1.2.3 STC 簡介 ………….....................................……….3
1.2.4 盲蔽式通道估測 …...……..…………...…………..4
1.3 研究目標 ………………….…..…..……………………..4
1.4 各章節內容概述 ………………...……………………….. 6
第二章 半盲蔽式通道估測與等化器 ……………………………….. 7
2.1 盲蔽式通道估測簡介 …………………………..…………7
2.2 MIMO STC-OFD架構 .……………………………………8
2.3 以子空間分解法為基礎之盲蔽式通道估測 …………….11
2.3.1 盲蔽式通道估測之定義 ……….….……………...12
2.3.2 使用盲蔽式通道估測法 …………..……………...15
2.4 等化器 ..…….……………………………………………..17
第三章 正反向平均法 ………………………………………………..20
3.1 正向取樣資料 ..…………………………….…………….21
3.2 反向取樣資料 …………….……………………………...24
3.3 正反向平均法 …………….……………………………...25
3.4 特徵分析 ………………….……………………………...27
3.5 快速正反向平均法 ………………………….…………...35
3.5.1 快速正反向平均法……….………………………...35
3.6 舉例說明 …………….…………………………………...38
3.7 小結 ………………….…………………………………...44
第四章 適應性雜訊子空間估測法 …………………………………..46
4.1 適應性正向雜訊子空間估測法 ……………………….…47
4.2 適應性正反向雜訊子空間估測法 …………….…………54
4.3 適應性快速正反向雜訊子空間估測法 ………………….58
第五章 電腦模擬 ……..………………………………………………62
第六章 結論與未來發展 ……………………………………………..89
參考文獻 ………………………………………………………………92

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