In this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. While existing methods either rely on the sparsity property, the finite-alphabet property, or none of those properties to solve the under-determined system of linear equations, we capitalize on both the sparsity and the finite-alphabet features of the signal. The problem is first formulated in a Bayesian framework to incorporate the prior knowledge of sparsity, which is then shown to be solvable using sphere decoding (SD) or semi-definite relaxation (SDR) for efficient Boolean programming. A few toy simulations show how our method can outperform existing works.

Detection of Sparse Signals under Finite-Alphabet Constraints

LOTTICI, VINCENZO
Co-primo
Writing – Review & Editing
2009-01-01

Abstract

In this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. While existing methods either rely on the sparsity property, the finite-alphabet property, or none of those properties to solve the under-determined system of linear equations, we capitalize on both the sparsity and the finite-alphabet features of the signal. The problem is first formulated in a Bayesian framework to incorporate the prior knowledge of sparsity, which is then shown to be solvable using sphere decoding (SD) or semi-definite relaxation (SDR) for efficient Boolean programming. A few toy simulations show how our method can outperform existing works.
2009
978-1-4244-2354-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/132652
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