267 research outputs found
Uncertainty Relations and Sparse Signal Recovery for Pairs of General Signal Sets
We present an uncertainty relation for the representation of signals in two
different general (possibly redundant or incomplete) signal sets. This
uncertainty relation is relevant for the analysis of signals containing two
distinct features each of which can be described sparsely in a suitable general
signal set. Furthermore, the new uncertainty relation is shown to lead to
improved sparsity thresholds for recovery of signals that are sparse in general
dictionaries. Specifically, our results improve on the well-known
-threshold for dictionaries with coherence by up to a factor of
two. Furthermore, we provide probabilistic recovery guarantees for pairs of
general dictionaries that also allow us to understand which parts of a general
dictionary one needs to randomize over to "weed out" the sparsity patterns that
prohibit breaking the square-root bottleneck.Comment: submitted to IEEE Trans. Inf. Theor
Almost Lossless Analog Signal Separation
We propose an information-theoretic framework for analog signal separation.
Specifically, we consider the problem of recovering two analog signals from a
noiseless sum of linear measurements of the signals. Our framework is inspired
by the groundbreaking work of Wu and Verd\'u (2010) on almost lossless analog
compression. The main results of the present paper are a general achievability
bound for the compression rate in the analog signal separation problem, an
exact expression for the optimal compression rate in the case of signals that
have mixed discrete-continuous distributions, and a new technique for showing
that the intersection of generic subspaces with subsets of sufficiently small
Minkowski dimension is empty. This technique can also be applied to obtain a
simplified proof of a key result in Wu and Verd\'u (2010).Comment: To be presented at IEEE Int. Symp. Inf. Theory 2013, Istanbul, Turke
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