Campus Units

Electrical and Computer Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

5-2012

Journal or Book Title

IEEE Transactions on Signal Processing

Volume

60

Issue

5

First Page

2628

Last Page

2634

DOI

10.1109/TSP.2012.2185231

Abstract

We develop a generalized expectation-maximization (GEM) algorithm for sparse signal reconstruction from quantized noisy measurements. The measurements follow an underdetermined linear model with sparse regression coefficients, corrupted by additive white Gaussian noise having unknown variance. These measurements are quantized into bins and only the bin indices are used for reconstruction. We treat the unquantized measurements as the missing data and propose a GEM iteration that aims at maximizing the likelihood function with respect to the unknown parameters. Under mild conditions, our GEM iteration yields a convergent monotonically nondecreasing likelihood function sequence and the Euclidean distance between two consecutive GEM signal iterates goes to zero as the number of iterations grows. We compare the proposed scheme with the state-of-the-art convex relaxation method for quantized compressed sensing via numerical simulations.

Comments

This is a manuscript of an article from IEEE Transactions on Signal Processing 60 (2012): 2628, doi:10.1109/TSP.2012.2185231. Posted with permission.

Rights

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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