基于压缩模数转换的随机解调原型硬件

T. Ragheb, J. Laska, H. Nejati, S. Kirolos, Richard Baraniuk, Y. Massoud
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引用次数: 109

摘要

在本文中,我们利用压缩感知理论的最新进展来实现超越奈奎斯特采样约束的信号采集。通过利用除带宽限制以外的附加结构,我们成功地恢复了以亚奈奎斯特采样率采样的信号。我们提出了一种基于随机解调架构的压缩模数转换器(CADC)的工作原型。该结构特别适用于时频面稀疏的宽带信号。在相同的带宽下,通过提高传输速率来提高通信和多媒体系统的性能。我们报告了在采样率低至奈奎斯特率的1/8的情况下成功重建调幅调制信号,这意味着带宽和存储内存节省高达87.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A prototype hardware for random demodulation based compressive analog-to-digital conversion
In this paper, we utilize recent advances in compressive sensing theory to enable signal acquisition beyond Nyquist sampling constraints. We successfully recover signals sampled at sub-Nyquist sampling rates by exploiting additional structure other than bandlimitedness. We present a working prototype of compressive analog-to-digital converter (CADC) based on a random demodulation architecture. The architecture is particularly suitable for wideband signals that are sparse in the time-frequency plane. CADC has the advantage of enhancing the performance of communication and multimedia systems by increasing the transmission rate for the same bandwidth. We report successful reconstruction of AM modulated signals at sampling rates down to 1/8 of the Nyquist-rate, which represents an up to 87.5% savings in the bandwidth and the storage memory.
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