Exploiting General Multi-Dimensional Priors in Compressed-Sensing Reconstruction

Konferenz: SCC 2019 - 12th International ITG Conference on Systems, Communications and Coding
11.02.2019 - 14.02.2019 in Rostock, Germany


Tagungsband: SCC 2019

Seiten: 6Sprache: EnglischTyp: PDF

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Birgmeier, Stefan C.; Goertz, Norbert (TU Wien, Institute of Telecommunications, Austria)

Message passing based algorithms have been shown to perform well in terms of minimum mean-squared error for high-dimensional signals composed of independent and identically distributed one-dimensional and sparse components. These conditions limit the applicability and performance of these algorithms since dependencies among components are not used during recovery. A detailed derivation is given that, as a novelty, extends the known derivation of the conventional Bayesian Approximate Message Passing scheme (BAMP) to general multi-dimensional priors. The proposed algorithms significantly reduce the number of samples required for reconstruction compared to methods which do not exploit dependencies. Applications include multiple-measurement vector (MMV) problems, group sparsity as well as symbol recovery in MIMO systems and reconstruction in the case of general, non-sparse dependencies between components.