摘要翻译：
极性码作为第一个可证明能达到二进制输入离散无内存信道（B-DMCs）对称容量的纠错码，最近被3GPP用于eMBB控制信道。在现有的算法中，CRC辅助连续抵消列表（CA-SCL）译码由于其良好的性能而受到青睐，它将CRC置于译码的最后，有助于在最终选择之前消除无效候选项。然而，良好的性能是在复杂度与列表大小呈线性增长的情况下获得的。本文提出了一种量身定制的CRC辅助SCL（TCA-SCL）译码方法，以平衡性能和复杂度。利用\emph{虚变换}和\emph{虚长度}分析了如何对给定的段选择合适的CRC。为了进一步提高性能，引入了混合自动重传请求(HARQ)方案。数值结果表明，TCA-SCL和HARQ-TCA-SCL方案在误码率$textrm{FER}=10^-2}$时，在复杂度与现有技术相当的情况下，分别获得$0.1$dB和$0.25$dB的性能增益。最后，用FPGA实现了一个高效的TCA-SCL译码器，证明了其优于CA-SCL译码器的优点。
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英文标题：
《Segmented Successive Cancellation List Polar Decoding with Tailored CRC》
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作者：
Huayi Zhou (1 and 2 and 3), Xiao Liang (1 and 2 and 3), Liping Li (4),
  Zaichen Zhang (2 and 3), Xiaohu You (2), Chuan Zhang (1 and 2 and 3) ((1) Lab
  of Efficient Architectures for Digital-communication and Signal-processing
  (LEADS), (2) National Mobile Communications Research Laboratory, (3) Quantum
  Information Center, Southeast University, China, (4) Key Laboratory of
  Intelligent Computing and Signal Processing of the Ministry of Education,
  Anhui University, China)
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  As the first error correction codes provably achieving the symmetric capacity of binary-input discrete memory-less channels (B-DMCs), polar codes have been recently chosen by 3GPP for eMBB control channel. Among existing algorithms, CRC-aided successive cancellation list (CA-SCL) decoding is favorable due to its good performance, where CRC is placed at the end of the decoding and helps to eliminate the invalid candidates before final selection. However, the good performance is obtained with a complexity increase that is linear in list size $L$. In this paper, the tailored CRC-aided SCL (TCA-SCL) decoding is proposed to balance performance and complexity. Analysis on how to choose the proper CRC for a given segment is proposed with the help of \emph{virtual transform} and \emph{virtual length}. For further performance improvement, hybrid automatic repeat request (HARQ) scheme is incorporated. Numerical results have shown that, with the similar complexity as the state-of-the-art, the proposed TCA-SCL and HARQ-TCA-SCL schemes achieve $0.1$ dB and $0.25$ dB performance gain at frame error rate $\textrm{FER}=10^{-2}$, respectively. Finally, an efficient TCA-SCL decoder is implemented with FPGA demonstrating its advantages over CA-SCL decoder.
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PDF链接：
https://arxiv.org/pdf/1803.00521