High-Dimensional Signal Compression: Lattice Point Bounds and Metric Entropy
Abstract
We study worst-case signal compression under an $\ell^2$ energy constraint, with coordinate-dependent quantization precisions. The compression problem is reduced to counting lattice points in a diagonal ellipsoid. Under balanced precision profiles, we obtain explicit, dimension-dependent upper bounds on the logarithmic codebook size. The analysis refines Landau's classical lattice point estimates using uniform Bessel bounds due to Olenko and explicit Abel summation. --- Source: arXiv:2604.03178v...
Description / Details
We study worst-case signal compression under an energy constraint, with coordinate-dependent quantization precisions. The compression problem is reduced to counting lattice points in a diagonal ellipsoid. Under balanced precision profiles, we obtain explicit, dimension-dependent upper bounds on the logarithmic codebook size. The analysis refines Landau's classical lattice point estimates using uniform Bessel bounds due to Olenko and explicit Abel summation.
Source: arXiv:2604.03178v1 - http://arxiv.org/abs/2604.03178v1 PDF: https://arxiv.org/pdf/2604.03178v1 Original Link: http://arxiv.org/abs/2604.03178v1
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Apr 6, 2026
Mathematics
Mathematics
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