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This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on
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Kazunori HAYASHI, Masaaki NAGAHARA, Toshiyuki TANAKA, "A User's Guide to Compressed Sensing for Communications Systems" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 3, pp. 685-712, March 2013, doi: 10.1587/transcom.E96.B.685.
Abstract: This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.685/_p
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@ARTICLE{e96-b_3_685,
author={Kazunori HAYASHI, Masaaki NAGAHARA, Toshiyuki TANAKA, },
journal={IEICE TRANSACTIONS on Communications},
title={A User's Guide to Compressed Sensing for Communications Systems},
year={2013},
volume={E96-B},
number={3},
pages={685-712},
abstract={This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on
keywords={},
doi={10.1587/transcom.E96.B.685},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - A User's Guide to Compressed Sensing for Communications Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 685
EP - 712
AU - Kazunori HAYASHI
AU - Masaaki NAGAHARA
AU - Toshiyuki TANAKA
PY - 2013
DO - 10.1587/transcom.E96.B.685
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2013
AB - This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on
ER -