N. Görtz, S. C. Birgmeier:

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

Talk: 24th International ITG Workshop on Smart Antennas (WSA 2020), Hamburg, Deutschland; 02-18-2020 - 02-20-2020; in: "24th International ITG Workshop on Smart Antennas", VDE, (2020), ISBN: 978-3-8007-5200-3; 1 - 6.

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.

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.

Signal Processing, Compressed Sensing, Approximate Message Passing

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