TY - JOUR
T1 - Decentralized Expectation Consistent Signal Recovery for Phase Retrieval
AU - Wang, Chang Jen
AU - Wen, Chao Kai
AU - Tsai, Shang Ho
AU - Jin, Shi
PY - 2020
Y1 - 2020
N2 - In this study, we present a phase retrieval solution that aims to recover signals from noisy phaseless measurements. A recently proposed scheme known as generalized expectation consistent signal recovery (GEC-SR), has shown better accuracy, speed, and robustness than many existing methods. However, sensing high-resolution images with large transform matrices presents a computational burden for GEC-SR, thereby limiting its applications to areas, such as real-time implementation. Moreover, GEC-SR does not support distributed computing, which is an important requirement to modern computing. To address these issues, we propose a novel decentralized algorithm called 'deGEC-SR' by leveraging the core framework of GEC-SR. deGEC-SR exhibits excellent performance similar to GEC-SR but runs tens to hundreds of times faster than GEC-SR. We derive the theoretical state evolution for deGEC-SR and demonstrate its accuracy using numerical results. Analysis allows quick generation of performance predictions and enriches our understanding on the proposed algorithm.
AB - In this study, we present a phase retrieval solution that aims to recover signals from noisy phaseless measurements. A recently proposed scheme known as generalized expectation consistent signal recovery (GEC-SR), has shown better accuracy, speed, and robustness than many existing methods. However, sensing high-resolution images with large transform matrices presents a computational burden for GEC-SR, thereby limiting its applications to areas, such as real-time implementation. Moreover, GEC-SR does not support distributed computing, which is an important requirement to modern computing. To address these issues, we propose a novel decentralized algorithm called 'deGEC-SR' by leveraging the core framework of GEC-SR. deGEC-SR exhibits excellent performance similar to GEC-SR but runs tens to hundreds of times faster than GEC-SR. We derive the theoretical state evolution for deGEC-SR and demonstrate its accuracy using numerical results. Analysis allows quick generation of performance predictions and enriches our understanding on the proposed algorithm.
KW - Bayes-optimal inference
KW - Decentralized algorithm
KW - Distributed processing
KW - Expectation consistent
KW - Phase retrieval
UR - http://www.scopus.com/inward/record.url?scp=85082401577&partnerID=8YFLogxK
U2 - 10.1109/TSP.2020.2974711
DO - 10.1109/TSP.2020.2974711
M3 - Article
AN - SCOPUS:85082401577
SN - 1053-587X
VL - 68
SP - 1484
EP - 1499
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
M1 - 9006911
ER -