給定一組多訊號源經過瞬時多通道混合的量測資料，而盲蔽訊號源分離(blind source separation, BSS)演算法只需利用訊號源之統計特性以分離出所有的訊號源而不需傳送任何導引符號(training symbol)。因此，它具有較高的頻寬效益之優點。在現存的演算法中，每種演算法都有其特定的假設，因此我們在此篇論文中整理了一些常見之盲蔽訊號源分離演算法，並加以比較。而其中Chi和Chen的快速峰度最大值演算法(fast kurtosis maximization algorithm, FKMA)係利用峰度最大化之準則，有效地利用空間抽取及分離訊號源，然而訊號源之正規化峰度絕對值越小，FKMA的效能就會越差。因此Chi等人提出了同樣使用峰度最大化的渦輪式訊號源分離演算法(turbo source extraction algorithm, TSEA)，其抽取訊號源的過程中，循環地經過了一個空間處理(訊號源抽取)和一個時間處理(將欲抽出的訊號源轉換成具有較大正規化峰度值的濾後訊號源)。而在此論文中，我們更完整地分析TSEA之效能及模擬。另外，FKMA與TSEA一次只能抽取一個訊號源，因此需要搭配一個多級連續消除(multistage successive cancellation, MSC)程序來達成分離所有訊號源。然而MSC程序會造成錯誤累積，使得越後抽取的訊號源正確性越低，因此Chi等人再度提出兩個非消除多級(non-cancellation multistage)演算法，不需要經過任何消除程序即能抽取出所有的訊號源。而此篇論文中，我們對此演算法加以分析其效能及簡化其數學式子。
Chi和Chen的FKMA已成功地被應用至訊號源分離、波束成型(beamforming)及等化(equalization)。而在此論文中，我們將FKMA應用至傳統的串接式空時接收器(cascade space-time receiver, CSTR)，此架構包含一個空間處理器，其目的在於消弭同通道干擾(co-channel interference, CCI)，和一個時間處理器，其目的在於消弭符碼間干擾(inter-symbol interference, ISI)。然而，此接收器使用FKMA之效能受限於ISI失真的訊號源之正規化峰度絕對值大小。因此，我們提出一個新的架構，稱為渦輪式空時接收器(turbo space-time receiver, TSTR)。提出之接收器效能較不受限於ISI失真的訊號源之正規化峰度絕對值大小，因此提出的TSTR比CSTR有較好之效能。最後，我們呈現一些模擬結果去支持提出的TSTR之效能。

With a given set of measurements of instantaneous channel mixture of multiple sources, blind source separation (BSS) algorithms require only statistic of sources rather than training symbol to separate all source signals. Therefore, those blind algorithms are better spectrum efficient than those non-blind algorithms. Because there are specific assumptions for existing BSS algorithms, in this thesis, we make some arrangements and comparisons for some common BSS algorithms. Chi and Chen’s fast kurtosis maximization algorithm (FKMA), which is one of the BSS algorithms, by kurtosis maximization effectively extracts source signals using only spatial processing. By empirical studies we found that the smaller the normalized kurtosis magnitude of the extracted source signal, the worse the performance of the FKMA. Therefore, Chi et al. proposed turbo source separation algorithm (TSEA) also by kurtosis maximization which extracts one source signal through a spatial processing (for source extraction), and a temporal processing (for conversion of the extracted source into a filtered source with larger normalized kurtosis magnitude) cyclically. In this thesis, we analyse the performance of the TSEA and show some simulation results to support our analyses. For the extraction of all the unknown sources using either FKMA or TSEA, the widely used multistage successive cancellation (MSC) procedure has been an effective approach in spite of error propagation effects accumulated from stage to stage. Furthermore Chi et al. proposed two non-cancellation multistage (NCMS) BSS algorithms which extract all the unknown sources without any cancellation procedure. In this thesis, we analyse the performance of the two algorithms and simplify the mathematical equations.
Chi and Chen’s FKMA has been successively applied to blind source separation, blind beamforming, and blind equalization. In this thesis, we apply the FKMA to a conventional cascade space-time receiver (CSTR), which involves a spatial processor to suppress co-channel interference (CCI) and a temporal processor to reduce inter-symbol interference (ISI). However, the performance of the receiver using FKMA is limited by the normalized kurtosis magnitude of the ISI distorted source signal. For this reason, we propose a novel receiver structure, referred to as turbo space-time receiver (TSTR). The proposed receiver is not sensitive to the normalized kurtosis magnitude of the ISI distorted source signal, so the proposed TSTR outperforms the CSTR. Finally, some simulation results are provided to support the performance of the proposed TSTR.

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