A Parallel Algorithm for Phase Retrieval with Dictionary Learning

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

doi: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

Department of Computer Science and Applied Mathematics

Weizmann Institute of Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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
Title of host publication	ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages	5619-5623
Number of pages	5
Volume	2021-June
ISBN (Electronic)	9781728176055
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9413991
State	Published - 13 May 2021
Event	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, ON, Canada Duration: 6 Jun 2021 → 11 Jun 2021

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Period	6/06/21 → 11/06/21

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10.1109/ICASSP39728.2021.9413991

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@inproceedings{5aeabe3c3eff47c09eadab30545a4504,

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.",

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

year = "2021",

month = may,

day = "13",

doi = "10.1109/ICASSP39728.2021.9413991",

language = "الإنجليزيّة",

isbn = "978-1-7281-7606-2",

volume = "2021-June",

pages = "5619--5623",

booktitle = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

note = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 06-06-2021 Through 11-06-2021",

}

Liu, T, Tillmann, AM, Yang, Y, Eldar, YC & Pesavento, M 2021, A Parallel Algorithm for Phase Retrieval with Dictionary Learning. in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). vol. 2021-June, pp. 5619-5623, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6/06/21. https://doi.org/10.1109/ICASSP39728.2021.9413991

TY - GEN

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 - 2021/5/13

Y1 - 2021/5/13

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.

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

U2 - 10.1109/ICASSP39728.2021.9413991

DO - 10.1109/ICASSP39728.2021.9413991

M3 - منشور من مؤتمر

SN - 978-1-7281-7606-2

VL - 2021-June

SP - 5619

EP - 5623

BT - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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

Y2 - 6 June 2021 through 11 June 2021

ER -

A Parallel Algorithm for Phase Retrieval with Dictionary Learning

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