臺灣博碩士論文加值系統

在這篇論文中，首先介紹最大概度之軟性決策序列旋積碼解碼演算法(MLSDA)，這是由韓永祥和陳伯寧老師在1998年發明的解碼演算法。再來我們針對MLSDA依照堆疊空間和時間的限制做模擬，在這裡所有的模擬都是在接收端將所收到的資訊用八階量化處理。還有為了要解決實際的碼比(code rate)所造成在效能上的損失，而得送出非常長的訊息，所以在解碼方面，我們設計一種用區塊解碼方式的MLSDA，來解決此問題。
我們採用(2,1,6), (2,1,8)和(2,1,10)的迴旋碼經過白色高斯雜訊通道(AWGN)的環境做模擬。由模擬結果中，我們發現在信號功率對雜訊功率的比值(SNR)大於5dB時，其平均的運算複雜度，比傳統的Viterbi演算法快非常的多。而且在針對區塊解碼的方法在時間的限制上，我們研究發現在相同的位元錯誤比值(BER)上MLSDA可以比傳統的Viterbi演算法好十倍。

Maximum-likelihood soft-decision sequential decoding algorithm (MLSDA) was presented by Han and Chen. In this thesis, first of all, we present the MLSDA, then study and simulate the MLSDA imposed with stack size limitation, and time limitation. All simulations are based on 8 levels quantization receiver. In order to compensate the loss on signal-to-noise ratio due to reduction on the effective code rate of a convolutional code, a block decoding type MLSDA is designed. Simulation results of (2, 1, 6), (2, 1, 8) and (2, 1, 10) convolutional codes antipodally transmitted over the AWGN channel show that the average of computational effort required by MLSDA is several orders of magnitude less than the Viterbi algorithm when
signal-to-noise ratio is greater than 5 dB. Furthermore,
investigation on the block decoding type MLSDA restricted on time limitation indicates that proposed decoding algorithm has about ten times advantage on average complexity of the Viterbi algorithm at similar bit error rate performance.

1 Introduction
2 Introduction to MLSDA
3 Implementation Considerations and Simulation Results of the MLSDA
4 Conclusions

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