1,094 research outputs found
Distributed Quantization for Compressed Sensing
We study distributed coding of compressed sensing (CS) measurements using
vector quantizer (VQ). We develop a distributed framework for realizing
optimized quantizer that enables encoding CS measurements of correlated sparse
sources followed by joint decoding at a fusion center. The optimality of VQ
encoder-decoder pairs is addressed by minimizing the sum of mean-square errors
between the sparse sources and their reconstruction vectors at the fusion
center. We derive a lower-bound on the end-to-end performance of the studied
distributed system, and propose a practical encoder-decoder design through an
iterative algorithm.Comment: 5 Pages, Accepted for presentation in ICASSP 201
Exact Performance Analysis of the Oracle Receiver for Compressed Sensing Reconstruction
A sparse or compressible signal can be recovered from a certain number of
noisy random projections, smaller than what dictated by classic Shannon/Nyquist
theory. In this paper, we derive the closed-form expression of the mean square
error performance of the oracle receiver, knowing the sparsity pattern of the
signal. With respect to existing bounds, our result is exact and does not
depend on a particular realization of the sensing matrix. Moreover, our result
holds irrespective of whether the noise affecting the measurements is white or
correlated. Numerical results show a perfect match between equations and
simulations, confirming the validity of the result.Comment: To be published in ICASSP 2014 proceeding
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