Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing

Konferenz: WSA 2020 - 24th International ITG Workshop on Smart Antennas
18.02.2020 - 20.02.2020 in Hamburg, Germany

Tagungsband: ITG-Fb. 291: WSA 2020

Seiten: 6Sprache: EnglischTyp: PDF

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Goertz, Norbert; Birgmeier, Stefan (Technische Universität Wien, Institute of Telecommunications, Gusshausstr. 25 / E389, 1040 Wien, Austria)

Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing recovery algorithm. Simulations show that such sparse matrices, which allow for large savings in storage and computation time, can achieve a recovery performance that is as good as the benchmark given by random Gaussian matrices.