Approximate message passing under finite alphabet constraints
In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the...
| Main Authors: | , , |
|---|---|
| Format: | Conference item |
| Published: |
2012
|
| _version_ | 1826272101547376640 |
|---|---|
| author | Müller, A Sejdinovic, D Piechocki, R |
| author_facet | Müller, A Sejdinovic, D Piechocki, R |
| author_sort | Müller, A |
| collection | OXFORD |
| description | In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement. © 2012 IEEE. |
| first_indexed | 2024-03-06T22:07:13Z |
| format | Conference item |
| id | oxford-uuid:509235b1-f6b9-4271-b500-e260b27a7202 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:07:13Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:509235b1-f6b9-4271-b500-e260b27a72022022-03-26T16:14:20ZApproximate message passing under finite alphabet constraintsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:509235b1-f6b9-4271-b500-e260b27a7202Symplectic Elements at Oxford2012Müller, ASejdinovic, DPiechocki, RIn this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement. © 2012 IEEE. |
| spellingShingle | Müller, A Sejdinovic, D Piechocki, R Approximate message passing under finite alphabet constraints |
| title | Approximate message passing under finite alphabet constraints |
| title_full | Approximate message passing under finite alphabet constraints |
| title_fullStr | Approximate message passing under finite alphabet constraints |
| title_full_unstemmed | Approximate message passing under finite alphabet constraints |
| title_short | Approximate message passing under finite alphabet constraints |
| title_sort | approximate message passing under finite alphabet constraints |
| work_keys_str_mv | AT mullera approximatemessagepassingunderfinitealphabetconstraints AT sejdinovicd approximatemessagepassingunderfinitealphabetconstraints AT piechockir approximatemessagepassingunderfinitealphabetconstraints |