Work

Optimal quantization for compressive sensing under message passing reconstruction

Year: 2011

Type: conference-paper

Abstract: We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.

Source: IEEE International Symposium on Information Theory (ISIT)

DOI: 10.1109/isit.2011.6034168

Authors: Ulugbek Kamilov, Vivek K Goyal, Sundeep Rangan

Institutions: École Polytechnique Fédérale de Lausanne, Massachusetts Institute of Technology, New York University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2109236251

FWCI: 8.8

Cites: 26

Cited by: 28

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