Constant Modulus Algorithms via Low-Rank Approximation
We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant...
| Main Authors: | , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM)
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/114672 |
| _version_ | 1826190154693345280 |
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| author | Adler, Amir Wax, Mati |
| author_facet | Adler, Amir Wax, Mati |
| author_sort | Adler, Amir |
| collection | MIT |
| description | We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant modulus. The usefulness of the proposed solutions is demonstrated for the tasks of blind beamforming and blind multiuser detection. The performance of these solutions, as we demonstrate by simulated data, is superior to existing methods. |
| first_indexed | 2024-09-23T08:35:55Z |
| format | Technical Report |
| id | mit-1721.1/114672 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:35:55Z |
| publishDate | 2018 |
| publisher | Center for Brains, Minds and Machines (CBMM) |
| record_format | dspace |
| spelling | mit-1721.1/1146722019-04-09T18:41:18Z Constant Modulus Algorithms via Low-Rank Approximation Adler, Amir Wax, Mati Constant modulus convex optimization trace norm We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant modulus. The usefulness of the proposed solutions is demonstrated for the tasks of blind beamforming and blind multiuser detection. The performance of these solutions, as we demonstrate by simulated data, is superior to existing methods. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. 2018-04-12T17:23:48Z 2018-04-12T17:23:48Z 2018-04-12 Technical Report Working Paper Other http://hdl.handle.net/1721.1/114672 en_US CBMM Memo Series;077 Attribution-NonCommercial-ShareAlike 3.0 United States http://creativecommons.org/licenses/by-nc-sa/3.0/us/ application/pdf Center for Brains, Minds and Machines (CBMM) |
| spellingShingle | Constant modulus convex optimization trace norm Adler, Amir Wax, Mati Constant Modulus Algorithms via Low-Rank Approximation |
| title | Constant Modulus Algorithms via Low-Rank Approximation |
| title_full | Constant Modulus Algorithms via Low-Rank Approximation |
| title_fullStr | Constant Modulus Algorithms via Low-Rank Approximation |
| title_full_unstemmed | Constant Modulus Algorithms via Low-Rank Approximation |
| title_short | Constant Modulus Algorithms via Low-Rank Approximation |
| title_sort | constant modulus algorithms via low rank approximation |
| topic | Constant modulus convex optimization trace norm |
| url | http://hdl.handle.net/1721.1/114672 |
| work_keys_str_mv | AT adleramir constantmodulusalgorithmsvialowrankapproximation AT waxmati constantmodulusalgorithmsvialowrankapproximation |