We consider multi-variate signals spanned by the integer shifts of a set of generating functions with distinct frequency profiles and the problem of reconstructing them from samples taken on a random periodic set. We show that such a sampling strategy succeeds with high probability provided that the density of the sampling pattern exceeds the number of frequency profiles by a logarithmic factor. The signal model includes bandlimited functions with multi-band spectra. While in this well-studied setting delicate constructions provide sampling strategies that meet the information theoretic benchmark of Shannon and Landau, the sampling pattern that we consider provides, at the price of a logarithmic oversampling factor, a simple alternative that is accompanied by favorable a priori stability margins (snug frames). More generally, we also treat bandlimited functions with arbitrary compact spectra, and different measures of its complexity and approximation rates by integer tiles. At the technical level, we elaborate on recent work on relevant sampling, with the key difference that the reconstruction guarantees that we provide hold uniformly for all signals, rather than for a subset of well-concentrated ones. This is achieved by methods of concentration of measure formulated on the Zak domain.

翻译：我们考虑由一组不同频率分布的生成函数的整数平移所张成的多变量信号，以及从随机周期集上的样本中重建这些信号的问题。我们证明，只要采样模式的密度超过频率分布的数量一个对数因子，这种采样策略就能以高概率成功。该信号模型包括具有多频带频谱的带限函数。尽管在这个经过深入研究的设定中，精妙的构造提供了满足Shannon和Landau信息论基准的采样策略，但我们所考虑的采样模式以对数过采样因子为代价，提供了一种简单的替代方案，并伴有有利的先验稳定性边际（紧贴框架）。更一般地，我们还处理具有任意紧致频谱的带限函数，以及其复杂性和通过整数平铺获得的近似率的不同度量。在技术层面，我们详细阐述了近期关于相关采样工作的成果，关键区别在于我们提供的重建保证对所有信号均匀成立，而不仅仅是针对那些良好集中的信号子集。这是通过在Zak域上制定的测度集中方法实现的。