Practical construction of sensing matrices for a greedy sparse recovery algorithm over finite fields
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Compressed sensing aims to retrieve sparse signals from very few samples. It relies on dedicated reconstruction algorithms and well-chosen measurement matrices. In combination with network coding, which operates traditionally over finite fields, it leverages the benefits of both techniques. However, compressed sensing has been primarily investigated over the real field. F2OMP is one of the few recovery algorithms to reconstruct signals over finite fields. However, its use in practical cases is limited since its performance depends mainly on binary matrices for signal recovery. This paper reports results of extensive simulations enhancing the features of well-performing measurement matrices for F2OMP as well as methods to build them. Moreover, a modified version of the algorithm, F2OMP-loop, is proposed. It offers a compromise between performance, stability, and processing time. This allows to design a joint compressed sensing and network coding framework over finite fields.
Details
Original language | English |
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Title of host publication | Proceedings - DCC 2023 |
Editors | Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 120-129 |
Number of pages | 10 |
ISBN (electronic) | 9798350347951 |
Publication status | Published - 2023 |
Peer-reviewed | Yes |
Publication series
Series | Data Compression Conference Proceedings |
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Volume | 2023-March |
ISSN | 1068-0314 |
Conference
Title | 2023 Data Compression Conference, DCC 2023 |
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Duration | 21 - 24 March 2023 |
City | Snowbird |
Country | United States of America |
External IDs
ORCID | /0000-0001-7008-1537/work/158767451 |
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