Paper Info
Title
Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing
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.
Year
Venue
DocType
2020
WSA 2020; 24th International ITG Workshop on Smart Antennas
Conference
ISBN
Citations 
PageRank 
978-3-8007-5200-3
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Norbert Goertz131628.94
Stefan C. Birgmeier200.68