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Optimal Sub-Nyquist Nonuniform Sampling and Reconstruction for Multiband Signals

机译:多频段信号的最佳亚奈奎斯特非均匀采样和重建

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摘要

We study the problem of optimal sub-Nyquist sampling for perfect reconstruction of multiband signals. The signals are assumed to have a known spectral support F that does not tile under translation. Such signals admit perfect reconstruction from periodic nonuniform sampling at rates approaching Landau's lower bound equal to the measure of F. For signals with sparse F, this rate can be much smaller than the Nyquist rate. Unfortunately, the reduced sampling rates afforded by this scheme can be accompanied by increased error sensitivity. In a recent study, we derived bounds on the error due to mismodeling and sample additive noise. Adopting these bounds as performance measures, we consider the problems of optimizing the reconstruction sections of the system, choosing the optimal base sampling rate, and designing the nonuniform sampling pattern. We find that optimizing these parameters can improve system performance significantly. Furthermore, uniform sampling is optimal for signals with F that tiles under translation. For signals with nontiling F, which are not amenable to efficient uniform sampling, the results reveal increased error sensitivities with sub-Nyquist sampling. However, these can be controlled by optimal design, demonstrating the potential for practical multifold reductions in sampling rate.
机译:我们研究了多频段信号完美重建的最优亚奈奎斯特采样问题。假设信号具有已知的频谱支持F,该F在平移时不会平铺。这些信号允许在接近 Landau 下界等于 F 度量的速率下从周期性非均匀采样中完美重建。对于具有稀疏 F 的信号,该速率可能远小于奈奎斯特速率。不幸的是,该方案提供的采样率降低可能伴随着误差灵敏度的提高。在最近的一项研究中,我们推导了由于错误建模和样品加性噪声引起的误差的边界。采用这些边界作为性能度量,我们考虑了优化系统的重建部分、选择最佳基础采样率和设计非均匀采样模式的问题。我们发现,优化这些参数可以显著提高系统性能。此外,对于具有 F 且平移平铺的信号,均匀采样是最佳选择。对于非平铺F的信号,这些信号不适合进行有效的均匀采样,结果显示亚奈奎斯特采样的误差灵敏度增加。然而,这些可以通过优化设计来控制,这表明采样率有可能降低数倍。

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