AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 65; H. 16; S. 4293 - 4308
Hauptverfasser:	Borgerding, Mark, Schniter, Philip, Rangan, Sundeep
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 15.08.2017
Schlagworte:	approximate message passing Approximation algorithms compressive sensing Deep learning Inverse problems Machine learning massive MIMO Message passing Probability density function random access Signal processing algorithms Transforms
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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