Joint Design of Convolutional Code and CRC under Serial List Viterbi Decoding
Joint Design of Convolutional Code and CRC under Serial List Viterbi Decoding
复制标题
串行列表维特比译码下卷积码与CRC的联合设计
DOI：
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发表时间：
2018
期刊：
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通讯作者：
R. Wesel
中科院分区：
文献类型：
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作者：
Hengjie Yang;Ethan Liang;R. Wesel
本文研究了使用串行列表Viterbi算法（S-LVA）的最佳卷积代码（CCS）和CRC代码的联合设计，以达到目标框架错误率（FER）。我们首先对SNR和列表大小分析了S-LVA性能，并反复出现，并证明SNR到达极端时预期的解码尝试次数的收敛性。然后，我们提出编码的通道容量作为共同设计最佳CC-CRC对和最佳列表大小的标准，并证明S-LVA的最佳列表尺寸始终是所有可能CCS的基数。使用最大列表大小，我们选择最佳CC-CRC对的设计指标作为绑定到随机编码联合（RCU）结合的SNR差距，而最佳CC-CRC对是实现目标SNR差距最少复杂性的目标。最后，我们表明，具有强大最佳CRC代码的较弱的CC可能与没有CRC代码的强大CC一样强大。
This paper studies the joint design of optimal convolutional codes (CCs) and CRC codes when serial list Viterbi algorithm (S-LVA) is employed in order to achieve the target frame error rate (FER). We first analyze the S-LVA performance with respect to SNR and list size, repsectively, and prove the convergence of the expected number of decoding attempts when SNR goes to the extreme. We then propose the coded channel capacity as the criterion to jointly design optimal CC-CRC pair and optimal list size and show that the optimal list size of S-LVA is always the cardinality of all possible CCs. With the maximum list size, we choose the design metric of optimal CC-CRC pair as the SNR gap to random coding union (RCU) bound and the optimal CC-CRC pair is the one that achieves a target SNR gap with the least complexity. Finally, we show that a weaker CC with a strong optimal CRC code could be as powerful as a strong CC with no CRC code.