On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices

Junjie Ma; Xiaojun Yuan; Li Ping

doi:10.1109/LSP.2015.2414951

On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices

Junjie Ma, Xiaojun Yuan, Li Ping

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

50 Citations (Scopus)

Abstract

This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

Original language	English
Article number	7065244
Pages (from-to)	1580-1584
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	10
Online published	23 Mar 2015
DOIs	https://doi.org/10.1109/LSP.2015.2414951
Publication status	Published - Oct 2015

Research Keywords

AMP
partial DFT
signal recovery
state evolution
turbo compressed sensing

Access Output

10.1109/LSP.2015.2414951

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{55f84b7d99c345f9985240d65e4ae99e,

title = "On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices",

abstract = "This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.",

keywords = "AMP, partial DFT, signal recovery, state evolution, turbo compressed sensing",

author = "Junjie Ma and Xiaojun Yuan and Li Ping",

year = "2015",

month = oct,

doi = "10.1109/LSP.2015.2414951",

language = "English",

volume = "22",

pages = "1580--1584",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "IEEE",

number = "10",

}

TY - JOUR

T1 - On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices

AU - Ma, Junjie

AU - Yuan, Xiaojun

AU - Ping, Li

PY - 2015/10

Y1 - 2015/10

N2 - This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

AB - This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

KW - AMP

KW - partial DFT

KW - signal recovery

KW - state evolution

KW - turbo compressed sensing

UR - http://www.scopus.com/inward/record.url?scp=84926435300&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84926435300&origin=recordpage

U2 - 10.1109/LSP.2015.2414951

DO - 10.1109/LSP.2015.2414951

M3 - RGC 21 - Publication in refereed journal

SN - 1070-9908

VL - 22

SP - 1580

EP - 1584

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 10

M1 - 7065244

ER -

On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices

Abstract

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this