極化碼是當前5G通訊的熱門主題，傳統的置信傳播演算法用在碼長長的極化碼時往往收斂速度很慢因此需要許多迭代來收斂，如此會限制了此演算法的吞吐量。為了解決這個問題，有一項被稱作是可靠度推進的技巧被提出了，能夠利用可靠度較高的訊息來改善置信傳播演算法的收斂速度，然而此技巧使用的參數是非最佳化的，在這篇論文中我們將用深度學習的優化技巧來進一步改善此可靠度推進演算法，能達到更快的收斂速度以及更好的錯誤率表現。之後基於此優化過的演算法，我們提出了循環冗餘校閱輔助的極化碼循序列表可靠性推進置信傳播解碼演算法，與之前提出的列表置信傳播解碼演算法相比，可以擁有更低的解碼複雜度與達到更好的錯誤率表現。

Polar codes hava been a hot topic in the 5G communication senarios. The traditional belief propagation (BP) decoding for polar codes is known to converge slowly for large code lengths and therefore needs many iterations to converge, which makes it hard to achieve high throughput. To address this problem, a technique called reliability-boosting (RB-) decoding scheme was proposed, which improves the converging speed using the more reliable information in the belief propagation decoding process. However, the parameters used in this decoding scheme is sub-optimal. In this thesis, we further improved this decoding scheme using deep learning optimization, which achieved better converging speed and better error-rate performance. Later on top of that, we proposed a cyclic redundancy check (CRC)-aided sequential neural reliability-boosting belief propagation list decoding scheme, which can improve the bit error-rate performance and decoding complexity compared with the previous belief propagation list decoding scheme.

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