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.
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
Copyright Date
2012
Language
en
File Format
application/pdf
Recommended Citation
Qiu, Kun and Dogandžić, Aleksandar, "Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding" (2012). Electrical and Computer Engineering Publications. 128.
https://lib.dr.iastate.edu/ece_pubs/128
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.