TY - GEN
T1 - Nonlinear Functions in Learned Iterative Shrinkage-Thresholding Algorithm for Sparse Signal Recovery
AU - Marques, Elaine Crespo
AU - Maciel, Nilson
AU - Naviner, Lirida
AU - Cai, Hao
AU - Yang, Jun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Compressive sensing requires fewer measurements than Nyquist rate to recover sparse signals, leading to processing and energy saving. The efficiency of this technique strongly depends on the quality of the considered sparse recovery algorithm. This work focuses on a learned iterative shrinkage-Thresholding algorithm where iterations are related to layers of a neural network. We analyze the performance of this algorithm for different shrinkage functions. A decrease up to 9dB in the NMSE value is achieved by choosing appropriate shrinkage function. Moreover, the estimation performance can be close to the theoretical performance bound, showing deep learning as a promising tool for sparse signal estimation. This work can be applied in several areas such as image processing, Internet of Things (IoT), cognitive radio networks, and sparse channel estimation for wireless communications.
AB - Compressive sensing requires fewer measurements than Nyquist rate to recover sparse signals, leading to processing and energy saving. The efficiency of this technique strongly depends on the quality of the considered sparse recovery algorithm. This work focuses on a learned iterative shrinkage-Thresholding algorithm where iterations are related to layers of a neural network. We analyze the performance of this algorithm for different shrinkage functions. A decrease up to 9dB in the NMSE value is achieved by choosing appropriate shrinkage function. Moreover, the estimation performance can be close to the theoretical performance bound, showing deep learning as a promising tool for sparse signal estimation. This work can be applied in several areas such as image processing, Internet of Things (IoT), cognitive radio networks, and sparse channel estimation for wireless communications.
KW - Compressive sensing
KW - deep learning
KW - learned iterative shrinkage-Thresholding algorithm
KW - shrinkage functions
KW - sparse recovery algorithm
KW - sparse systems
U2 - 10.1109/SiPS47522.2019.9020469
DO - 10.1109/SiPS47522.2019.9020469
M3 - Conference contribution
AN - SCOPUS:85082382465
T3 - IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
SP - 324
EP - 329
BT - 2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019
Y2 - 20 October 2019 through 23 October 2019
ER -