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Recovery under Side Constraints

Ardah, Khaled ; Haardt, Martin ; Liu, Tianyi ; Matter, Frederic ; Pesavento, Marius ; Pfetsch, Marc E. (2021)
Recovery under Side Constraints.
doi: 10.48550/arXiv.2106.09375
Report, Bibliographie

Kurzbeschreibung (Abstract)

This paper addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representationvector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using L1-minimization. Furthermore, for constant modulus signals, signals with row-, block- and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning.

Typ des Eintrags: Report
Erschienen: 2021
Autor(en): Ardah, Khaled ; Haardt, Martin ; Liu, Tianyi ; Matter, Frederic ; Pesavento, Marius ; Pfetsch, Marc E.
Art des Eintrags: Bibliographie
Titel: Recovery under Side Constraints
Sprache: Englisch
Publikationsjahr: 30 Juni 2021
Verlag: arXiv
Reihe: Computer Science
Kollation: 30 Seiten
DOI: 10.48550/arXiv.2106.09375
URL / URN: https://arxiv.org/abs/2106.09375
Kurzbeschreibung (Abstract):

This paper addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representationvector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using L1-minimization. Furthermore, for constant modulus signals, signals with row-, block- and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning.

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Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme
Hinterlegungsdatum: 01 Jul 2021 09:38
Letzte Änderung: 19 Dez 2024 10:26
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