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Convolutional Polar Codes: LLR-based Successive Cancellation Decoder and List Decoding Performance

机译:卷积型极码:基于LLR的连续取消解码器和列表解码性能

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Recently convolutional polar (cpolar) codes have been proposed. A tensor-network-based successive cancellation (SC) decoding was proposed for them under which cpolar codes were shown to outperform polar codes. In this paper we present the notion of m-bit-channels for cpolar codes and give the recursive construction of m-bit-channels for m = 3. Then a log likelihood ratio(LLR)-based SC decoding of complexity order O(N log(N)) for cpolar codes is presented. We also present the numerical results for performance evaluation of cpolar codes under SC list (SCL) decoding. Our simulation results show that cpolar codes can achieve the performance of polar codes with a list size reduced by a factor of 4.
机译:最近已经提出了卷积极性(CPOLAR)代码。提出了一种基于张量网络的连续消除(SC)解码,在其中计算CPOLAR代码以优于极性代码。在本文中,我们向CPOLAR码呈现M比特通道的概念,并为M = 3提供M位通道的递归结构。然后对复杂度顺序的基于SC解码的对数似然比(LLR)(n提出了CPOLAR代码的日志(n))。我们还提出了SC列表(SCL)解码下的CPOLAR码的性能评估的数值结果。我们的仿真结果表明,CPOLAR代码可以实现极性代码的性能,列表大小减少了4倍。

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