A parallel algorithm for phase retrieval with dictionary learning

Tianyi Liu; Andreas M. Tillmann; Yang Yang; Yonina C. Eldar; Marius Pesavento

doi:https://doi.org/10.1109/ICASSP39728.2021.9413991

A parallel algorithm for phase retrieval with dictionary learning

Tianyi Liu, Andreas M. Tillmann, Yang Yang, Yonina C. Eldar, Marius Pesavento

Research output: Contribution to journal › Conference article › peer-review

Abstract

We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.

Original language	English
Pages (from-to)	5619-5623
Number of pages	5
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2021-June
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9413991
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021

Keywords

Block successive convex approximation
Dictionary learning
Majorization-minimization
Phase retrieval

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

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https://doi.org/10.1109/ICASSP39728.2021.9413991

Cite this

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title = "A parallel algorithm for phase retrieval with dictionary learning",

abstract = "We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.",

keywords = "Block successive convex approximation, Dictionary learning, Majorization-minimization, Phase retrieval",

author = "Tianyi Liu and Tillmann, {Andreas M.} and Yang Yang and Eldar, {Yonina C.} and Marius Pesavento",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

doi = "https://doi.org/10.1109/ICASSP39728.2021.9413991",

language = "الإنجليزيّة",

volume = "2021-June",

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journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "1520-6149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - A parallel algorithm for phase retrieval with dictionary learning

AU - Liu, Tianyi

AU - Tillmann, Andreas M.

AU - Yang, Yang

AU - Eldar, Yonina C.

AU - Pesavento, Marius

PY - 2021

Y1 - 2021

N2 - We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.

AB - We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.

KW - Block successive convex approximation

KW - Dictionary learning

KW - Majorization-minimization

KW - Phase retrieval

UR - http://www.scopus.com/inward/record.url?scp=85109551303&partnerID=8YFLogxK

U2 - https://doi.org/10.1109/ICASSP39728.2021.9413991

DO - https://doi.org/10.1109/ICASSP39728.2021.9413991

M3 - مقالة من مؤنمر

SN - 1520-6149

VL - 2021-June

SP - 5619

EP - 5623

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021

Y2 - 6 June 2021 through 11 June 2021

ER -

A parallel algorithm for phase retrieval with dictionary learning

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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