去尾迴旋碼結合解碼前環狀位移之解碼演算法

Abstract

由於去尾迴旋碼的網格具有環狀迴旋不改變結構的特性，因此解碼前作環狀位移(搭配解碼後的環狀位移回覆)並不會影響其解碼結果。初步模擬結果顯示，適當的解碼前環狀位移確實能夠改善解碼效能以及解碼複雜度。因此在本論文中，我們提出位移維特比演算法(shifting Viterbi algorithm)和位移環狀解碼演算法(shifting circular decoding algorithm)搭配「等權重」以及「不等權重」之位移量搜尋法。經由模擬比較不進行解碼前環狀位移之環狀解碼演算法(circular decoding algorithm)，使用位移環狀解碼演算法可以明顯縮減可達到接近最大概率(maximum-likelihood)效能的前訓練窗(forward training window)與後訓練窗(backward training window)的大小。最後，我們提出可達最大概率效能的訓練窗大小的理論預測方法。

By noting that the convolutional tail-biting code (CTBC) can be represented by a circular-free trellis structure, pre-decoding circular shift (together with the post-decoding shift back) will not change its decoding procedure. Simulations in the literature have already shown that the decoding performance as well as decoding complexity can be apparently improved by a proper pre-decoding circular shift. In this thesis, we proposed the shifting Viterbi algorithm and the shifting circular decoding algorithm using equal-weight and unequal-weight pre-decoding shift methods. We then show empirically that our methods can reduce the forward and backward training window sizes required for near maximum-likelihood performance. We also provide an intuitive analytical approach to determine the training window sizes required for near optimal performance.