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研究生:王聖賢
研究生(外文):Sheng-Shian Wang
論文名稱:針對大限制長度迴旋碼的低狀態數班特比解碼器
論文名稱(外文):A state-reduction Viterbi decoder for convolutional code with large constraint length
指導教授:陳伯寧
指導教授(外文):Prof.~Po-Ning Chen
學位類別:博士
校院名稱:國立交通大學
系所名稱:電信工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:40
中文關鍵詞:大限制長度迴旋碼
外文關鍵詞:convolutional code with large constraint length
相關次數:
  • 被引用被引用:0
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迴旋碼編碼器和班特比解碼器是現今非常普遍的一種編碼系統的組合。這是因為在適當的設計下,這樣的組合可以提供可實現的複雜度和可接受的性能。在這樣的系統下,複雜度和性能的權衡取決於限制長度的選取。值得注意的是,班特比解碼器的效能隨著限制長度的增加而成指數性的降低,而複雜度卻同樣地成指數性的成長。現今的實踐能力僅僅能提供到不超過9的限制長度,這同時也限制了所能達到的效能。
另一方面,非常大限制長度的迴旋碼架構在理論上及實作上都已經不是問題,雖然蒙地卡羅在技術上仍然無法模擬最大近似的效能。不過,一個新的技巧─重要取樣技術被提出來可以正確地完成模擬大限制長度迴旋碼 (24或更高) 的最大近似的效能。而這模擬結果也顯示了大限制長度迴旋碼的最大近似的效能相當接近雪農的限制雖然並沒有碼器可以將之解碼。
在這篇論文中,我們提出了具有固定複雜度針對大限制長度迴旋碼的低狀態數班特比解調器,基於大限制長度迴旋碼的最大近似的效能非常好的原因,我們認為狀態降低的次佳解碼器依舊可以提供可接受的效能。最大近似的效能還受到一些其他參數選擇的影響,例如狀態規模和滑動視窗的大小,我們也會在論文中加以檢驗。
A popular combination in modern coding system is the
convolutional encoder and the Viterbi decoder. With a proper design, they can jointly provide an acceptable performance with feasible decoding complexity. In such a combination, a tradeoff on the error performance and the decoding complexity resides on the choice of the code constraint length. Specifically, the probability of Viterbi decoding failure decreases exponentially as the code constraint length increases. However, an increment of code constraint lengths also exponentially increases the computational effort of the Viterbi decoder. Nowadays, the implementation technology on the Viterbi decoder can only accommodate convolutional codes with a constraint length no greater than nine, which somehow limits the achievable error performance.
On the other hand, the construction of convolutional codes with very large constraint lengths are now possible in both theory and practice, yet Monte Carlo simulations of their resultant maximum-likelihood performance is technically infeasible. The author of "An efficient new technique for accurate bit error probability estimation of ZJ decoders" presented a new simulation technique called Important Sampling, which can accurately estimate the maximum-likelihood error performance of convolutional codes with constraint length up to 24 or higher. The authors proved by Important Sampling simulations that the error performance of convolutional codes with certain constraint length can actually be close to the Shannon limit although no feasible decoder can decode such codes.
In this thesis, we propose a reduced-state Viterbi decoder with fixed decoding complexity for use of codes with large constraint lengths. Since, by "An efficient new technique for accurate bit error probability estimation of ZJ decoders", the maximum-likelihood error performance of codes with large constraint length is very good, a degradation due to the sub-optimal state reduction at the decoder still provides an acceptably good performance. Performance impact from choosing different decoder parameters, such as state size and sliding window size, are also examined in this thesis.

Abstract
Content
1 Introduction
2 Background
2.1 Adaptive Viterbi algorithm
2.2 Randomized algorithm
2.3 M-algorithm
3 A state-reduction Viterbi decoding algorithm and its performance
3.1 A state-reduction Viterbi decoding algorithm
3.2 Simulation model
3.2.1 Simulation model
3.2.2 Degradetion due to reduction of mained state, M
3.2.3 Performance impact of sliding window
3.2.4 Performance impact of constraint length
4 Conclusions

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