改进亚奈奎斯特采样系统的有意混叠方法

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摘要翻译：
介绍了一种调制宽带变换器(MWC)作为利用一组快速交替伪随机(PR)信号的亚奈奎斯特采样器。MWC通过并行采样分支，将多频带谱与PR信号在时域内混合压缩，得到其亚奈奎斯特样本。以前，压缩比完全依赖于PR信号的规格。也就是说，为了进一步降低采样率而不丢失信息，需要更快、更长周期的PR信号。然而，这种PR信号发生器的实现导致高功耗和大制作面积。本文提出了一种新的混叠调制宽带变换器(AMWC)，该变换器可以在固定PR信号的情况下进一步降低MWC的采样率。其主要思想是在模数转换器(ADC)上引起有意的信号混叠。除了信号混频器的第一频谱压缩之外，有意混叠再次压缩混合频谱。我们证明了AMWC在不需要提高PR信号的速度或周期的情况下，减少了采样支路的数目和ADC的速率，从而实现了无损亚奈奎斯特采样。反之，对于给定的采样支路数和采样率，AMWC可以提高信号重构的性能。
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英文标题：
《Intentional Aliasing Method to Improve Sub-Nyquist Sampling System》
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作者：
Jehyuk Jang, Sanghun Im, and Heung-No Lee
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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：
  A modulated wideband converter (MWC) has been introduced as a sub-Nyquist sampler that exploits a set of fast alternating pseudo random (PR) signals. Through parallel sampling branches, an MWC compresses a multiband spectrum by mixing it with PR signals in the time domain, and acquires its sub-Nyquist samples. Previously, the ratio of compression was fully dependent on the specifications of PR signals. That is, to further reduce the sampling rate without information loss, faster and longer-period PR signals were needed. However, the implementation of such PR signal generators results in high power consumption and large fabrication area. In this paper, we propose a novel aliased modulated wideband converter (AMWC), which can further reduce the sampling rate of MWC with fixed PR signals. The main idea is to induce intentional signal aliasing at the analog-to-digital converter (ADC). In addition to the first spectral compression by the signal mixer, the intentional aliasing compresses the mixed spectrum once again. We demonstrate that AMWC reduces the number of sampling branches and the rate of ADC for lossless sub-Nyquist sampling without needing to upgrade the speed or period of PR signals. Conversely, for a given fixed number of sampling branches and sampling rate, AMWC improves the performance of signal reconstruction.
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PDF链接：
https://arxiv.org/pdf/1710.06142

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关键词：奎斯特 Applications Optimization Construction Application 降低 MWC sampling 频谱 利用