碼際干擾(Intersymbol Interference, ISI)是影響通訊系統效能的一個重要因素，為了達到高速率數位傳輸與可靠的通訊，使用等化器可以有效地消除由通道頻寬或多重路徑所造成的碼際干擾。
在本論文中，我們介紹一適應性H∞等化演算法，和傳統的遞迴最小平方(Recursive Least Square, RLS)濾波演算法不同的是，適應性H∞濾波演算法是最差情況下的最佳化，它可以最小化最差擾動(包括輸入雜訊和模態誤差(Modeling Error))對等化誤差的影響。適應性H∞等化演算法已經被證實較RLS演算法對於模態誤差和任意雜訊環境具有較不敏感的優點，然而，H∞演算法的缺點是有非常大的計算量。因此我們利用子權重分割技術去降低H∞演算法的計算量，由電腦模擬中可發現H∞演算法也可以減少因子權重分割所產生的動態估測誤差(Dynamic Estimation Error)。
最後，為了解決多重接取干擾(Multiple Access Interference, MAI)、切片間的干擾(Interchip Interference, ICI)、通道時變效應所造成的影響及計算複雜度等問題，我們將經子權重分割的基於H∞演算法(H∞-based)應用於直序分碼多重接取(Direct Sequence-Code Division Multiple Access, DS-CDMA)的多使用者偵測系統中，電腦模擬將驗證基於H∞等化演算法的優點。

Intersymbol interference(ISI) is an important factor which affects the performance of communication systems. To achieve highspeed digital transmission and reliable communication, an equalizer can effectively eliminate ISI caused by band-limited channel or multipath.
In this paper, we introduce an adaptive H∞ equalizing algorithm. Different form conventional recursive least square filtering algorithm, the adaptive H∞ filtering algorithm is a worst case optimization. It can minimize the effect of the worst disturbances (including input noise and modeling error) on the equalization error. It has been proven that the adaptive H∞ filtering algorithm has the advantage of reduction of sensitivity to modeling error and suitability for arbitrary ambient noise over RLS algorithm. However, the computational burden of the H∞ algorithm is enormous. To reduce the computational complexity, the subweight partition technique is employed to the H∞ algorithm. Computer simulation also shows that the H∞ algorithm can reduce the dynamic estimation error resulting from subweight partition.
Finally, in order to overcome multiple access interference, interchip interference, time-varying effects from the channel and computational complexity, the H∞ algorithm with subweight partition (termed H∞-based) is then further extended to the multiuser detection in code division multiple access (CDMA) system. Simulation results are presented to demonstrate the advantage of the H∞-based equalizing algorithm.

感謝詞.................................I
中文摘要...............................II
英文摘要...............................III
目錄...................................IV
圖表目錄...............................VI
第一章 緒言............................1
1.1 文獻探討...........................1
1.2 研究動機...........................3
第二章 適應性等化器演算法..............5
2.1 適應性等化器.......................5
2.1.1 數位調變訊號、通道與碼際干擾.....6
2.1.2 線性等化器(LE)...................8
2.1.3 判斷回授等化器(DFE)..............9
2.2 等化演算法.........................10
2.2.1 逼零演算法(ZF)...................10
2.2.2 最小均方演算法(LMS)..............11
2.2.3 遞迴最小平方演算法(RLS)..........13
2.3 電腦模擬及分析...................14
第三章 適應性H∞等化演算法...........25
3.1 系統介紹...........................26
3.2 判斷回授等化器的適應性濾波演算法...26
3.2.1 濾波(RLS)演算法..................27
3.2.2 濾波演算法.......................28
3.2.3 適應性H∞濾波演算法..............30
3.3 電腦模擬及分析...................32
第四章 基於H∞適應性等化演算法及其應用.41
4.1 子權重分割技術 .....................41
4.2 應用子權重分割技術的適應性H∞等化演算法.43
4.2.1 基於RLS濾波演算法................44
4.2.2 基於H∞濾波演算法................46
4.2.3 計算複雜度分析...................48
4.3 應用基於H∞適應性等化演算法於多使用者偵測系統.....................................49
4.3.1 信號模型介紹.....................49
4.3.2 接收器結構.......................50
4.4 電腦模擬及分析...................54
第五章 討論與建議....................63
參考文獻...............................65
附錄 A.................................i
附錄 B.................................iv
縮寫...................................v

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