Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing
Conference: WSA 2020 - 24th International ITG Workshop on Smart Antennas
02/18/2020 - 02/20/2020 at Hamburg, Germany
Proceedings: ITG-Fb. 291: WSA 2020
Pages: 6Language: englishTyp: PDF
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Authors:
Goertz, Norbert; Birgmeier, Stefan (Technische Universität Wien, Institute of Telecommunications, Gusshausstr. 25 / E389, 1040 Wien, Austria)
Abstract:
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.