Publication

Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Michael Christoph Gastpar
2013
Journal Articles

Abstract

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear mea- surements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the measurement rate and per-sample signal-to-noise ratio (SNR) are finite constants independent of the length of the vector, then the optimal sparsity pattern estimate will have a constant fraction of errors. Lower bounds on the measurement rate needed to attain a desired fraction of errors are given in terms of the SNR and various key parameters of the unknown vector. The tightness of the bounds in a scaling sense, as a function of the SNR and the fraction of er- rors, is established by comparison with existing achievable bounds. Near optimality is shown for a wide variety of practically motivated signal models.

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https://infoscience.epfl.ch/entities/publication/334118e0-3422-4b78-9fcc-728543ddcef0

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Michael Christoph Gastpar
2013
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/334118e0-3422-4b78-9fcc-728543ddcef0

About this result

Related concepts (22)

Khinchin's constant

In number theory, Aleksandr Yakovlevich Khinchin proved that for almost all real numbers x, coefficients ai of the continued fraction expansion of x have a finite geometric mean that is independent of the value of x and is known as Khinchin's constant. That is, for it is almost always true that where is Khinchin's constant (with denoting the product over all sequence terms). Although almost all numbers satisfy this property, it has not been proven for any real number not specifically constructed for the purpose.

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In algebra, the partial fraction decomposition or partial fraction expansion of a rational fraction (that is, a fraction such that the numerator and the denominator are both polynomials) is an operation that consists of expressing the fraction as a sum of a polynomial (possibly zero) and one or several fractions with a simpler denominator. The importance of the partial fraction decomposition lies in the fact that it provides algorithms for various computations with rational functions, including the explicit computation of antiderivatives, Taylor series expansions, inverse Z-transforms, and inverse Laplace transforms.

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Signal-to-noise ratio (SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise. SNR is an important parameter that affects the performance and quality of systems that process or transmit signals, such as communication systems, audio systems, radar systems, imaging systems, and data acquisition systems.

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