摘要翻译：
分布匹配是从均匀分布的比特序列到赋形振幅的定长可逆映射，在概率幅度赋形框架中占有重要地位。在传统的constantcomposition分布匹配(CCDM)中，所有输出序列具有相同的成分。在本文中，我们提出了多分区分布匹配(MPDM)，其中在所有输出序列上的组成是恒定的。当把期望的分布看作一个多集时，MPDM对应于将这个多集划分成大小相等的子集。我们证明了MPDM允许处理更多的输出序列，因此在所有非平凡情况下比CCDM具有更低的速率损失。提出了一种构造性的MPDM算法，该算法通过对划分进行约束，分为两个部分。二进制数据字的可变长度前缀确定要使用的组合，输入字的其余部分根据所选择的组合用常规的CCDM算法映射，例如算术编码。对加性高斯白噪声信道上64元正交幅度调制的仿真表明，在中高信噪比(SNRs)下，MPDM比CCDM的块长度节省约为2.5～5倍，在固定的间隙容量下，MPDM比CCDM的块长度节省约为2.5～5倍。
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英文标题：
《Multiset-Partition Distribution Matching》
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作者：
Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino, Keisuke
  Kojima and Kieran Parsons
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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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英文摘要：
  Distribution matching is a fixed-length invertible mapping from a uniformly distributed bit sequence to shaped amplitudes and plays an important role in the probabilistic amplitude shaping framework. With conventional constantcomposition distribution matching (CCDM), all output sequences have identical composition. In this paper, we propose multisetpartition distribution matching (MPDM) where the composition is constant over all output sequences. When considering the desired distribution as a multiset, MPDM corresponds to partitioning this multiset into equal-size subsets. We show that MPDM allows to address more output sequences and thus has lower rate loss than CCDM in all nontrivial cases. By imposing some constraints on the partitioning, a constructive MPDM algorithm is proposed which comprises two parts. A variable-length prefix of the binary data word determines the composition to be used, and the remainder of the input word is mapped with a conventional CCDM algorithm, such as arithmetic coding, according to the chosen composition. Simulations of 64-ary quadrature amplitude modulation over the additive white Gaussian noise channel demonstrate that the block-length saving of MPDM over CCDM for a fixed gap to capacity is approximately a factor of 2.5 to 5 at medium to high signal-to-noise ratios (SNRs).
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PDF链接：
https://arxiv.org/pdf/1801.08445