臺灣博碩士論文加值系統

目前現有之離散多重音調系統之等化器參數求取演算法具接近最佳的表現，但是其計算量相當複雜。以頻域適應性演算法求取參數有收斂不穩定的情形，而時域適應性演算法的收斂速度有待提升。本論文即針對這些問題提出解決的方法。首先，對於時域的適應性演算法我們提出一種能加速運算時間的架構。主要是在接收端加入一前置濾波器以縮短通道的長度，而前置濾波器的係數則由已知通道利用最佳化的方法求出。接著我們提出一種新的演算法能快速計算等化器參數的理論值。我們實地以計算機模擬了兩者的表現並與原來的演算法比較。結果發現我們所提的方法之效能和傳統的做法很接近，但運算量卻能大幅的減低。

Existing algorithms for the computation of DMT equalizer coefficients require, though have near-optimum performance, high computation load. Frequency-domain equalizer training lacks the stability of convergence, and time-domain equalizer training suffers low speed of convergence. We proposed in this paper two alternative algorithms, one of which is to speed up the convergence of time-domain euqalizaer training, the other is to reduced the computation load of coefficients calculation. The performance is simulated and compared to that of the original algorithms.

1. 簡介7
2. DMT系統9
2.1. 系統介紹9
Constellation Encoder11
訓練訊號（Training Signal）11
2.2. 等化器架構12
3. 現有演算法19
3.1. MMSE最佳解19
3.2. 時域適應性演算法21
3.3. 頻域適應性演算法22
3.4. 最佳通道響應縮減24
4. 所提出的演算法29
4.1. 前置率波29
濾波器模型29
狀態空間IIR濾波器模型30
縱合通道響應縮減34
4.2. 即時修正之演算法34
新的Criterion34
The Power Method36
Generalized Levinson Algorithm37
5. 計算機模擬42
5.1. 模擬環境42
5.2. 模擬結果42
通道估計42
MMSE參數決定43
頻域適應性演算法43
最佳通道響應縮減44
前置濾波44
即時之通道響應縮減45
綜合比較46
6.結論72
參考文獻73

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