摘要翻译：
在大多数纠错编码(ECC)框架中，典型的错误度量是比特错误率(BER)，它度量比特错误的数量。对于这个度量，比特的位置与解码无关，在许多噪声模型中，也与误码率无关。在许多应用中，这是不令人满意的，因为通常所有比特都不相等并且具有不同的意义。我们考虑了位在不同位置具有不同重要性时的纠错和缓解问题。为了纠错，我们从贝叶斯的角度来看ECC，并引入了具有一般损失函数的贝叶斯估计来考虑比特的重要性。我们提出了优化该误差度量的ECC方案。由于该问题具有高度非线性，传统的ECC施工技术已不适用。使用穷举搜索成本很低，因此我们使用迭代改进搜索技术来找到好的码书。我们对一般码本和线性码都进行了优化。我们给出了数值实验，表明它们可以优于经典的线性分组码（如汉明码）和译码方法（如最小距离译码）。为了减少错误，我们研究了ECC不可能或不可取的情况，但显著性感知信息编码仍然有利于减少平均错误。本文提出了一种新的数字表示格式，适用于未知噪声和可能较大噪声的新兴存储介质，并表明它比传统的数字表示格式具有更低的平均误差。
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英文标题：
《Designing communication systems via iterative improvement: error
  correction coding with Bayes decoder and codebook optimized for source symbol
  error》
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作者：
Chai Wah Wu
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最新提交年份：
2021
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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：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In most error correction coding (ECC) frameworks, the typical error metric is the bit error rate (BER) which measures the number of bit errors. For this metric, the positions of the bits are not relevant to the decoding, and in many noise models, not relevant to the BER either. In many applications this is unsatisfactory as typically all bits are not equal and have different significance. We consider the problem of bit error correction and mitigation where bits in different positions have different importance. For error correction, we look at ECC from a Bayesian perspective and introduce Bayes estimators with general loss functions to take into account the bit significance. We propose ECC schemes that optimize this error metric. As the problem is highly nonlinear, traditional ECC construction techniques are not applicable. Using exhaustive search is cost prohibitive, and thus we use iterative improvement search techniques to find good codebooks. We optimize both general codebooks and linear codes. We provide numerical experiments to show that they can be superior to classical linear block codes such as Hamming codes and decoding methods such as minimum distance decoding.   For error mitigation, we study the case where ECC is not possible or not desirable, but significance aware encoding of information is still beneficial in reducing the average error. We propose a novel number presentation format suitable for emerging storage media where the noise magnitude is unknown and possibly large and show that it has lower mean error than the traditional number format.
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PDF链接：
https://arxiv.org/pdf/1805.07429