Name
Abstract | ||
|---|---|---|
Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider additive white Gaussian noise (AWGN). To solve this problem, we employ the highly efficient generalized approximate message passing (GAMP) algorithm and provide closed-form expressions for the nonlinear steps. We demonstrate superiority of this approach in terms of the mean squared error (MSE)-performance compared to a similar state-of-the-art algorithm from the literature. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | Generalized approximate message passing, 1-bit quantization, Bernoulli-Gaussian mixture, noisy channel, compressed sensing |
Field | DocType | ISSN |
Approximation algorithm,Mathematical optimization,Noise measurement,Computer science,Signal-to-noise ratio,Algorithm,Mean squared error,Quantization (signal processing),Additive white Gaussian noise,Message passing,Compressed sensing | Conference | 2376-4066 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osman Musa | 1 | 16 | 2.68 |
| Gabor Hannak | 2 | 14 | 4.83 |
| Norbert Goertz | 3 | 316 | 28.94 |