Performance Evaluation of LMS and CM Algorithms for Beamforming
In this paper, we compare the performances of the least mean square (LMS) and constant modulus (CM) algorithms for beamforming. Our interest in these algorithms finds its origins in their reliability as a source-receiver pair. In addition, their use brings a great frequency of diversity even to resp...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7744625 |
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| author | Mossaab Atzemourt Abdelmajid Farchi Younes Chihab Zakaria Hachkar |
| author_facet | Mossaab Atzemourt Abdelmajid Farchi Younes Chihab Zakaria Hachkar |
| author_sort | Mossaab Atzemourt |
| collection | DOAJ |
| description | In this paper, we compare the performances of the least mean square (LMS) and constant modulus (CM) algorithms for beamforming. Our interest in these algorithms finds its origins in their reliability as a source-receiver pair. In addition, their use brings a great frequency of diversity even to respond quickly to the increasing spectral demand. The results suggest that the greater the number of elements in the antenna, the better the directivity for both LMS and CM. We also note that a judicious choice of the control parameter mu leads to a better speed of convergence for the two algorithms. Let us note, however, that LMS is more efficient. Our simulations show that in an environment affected by white Gaussian noise, LMS is more robust than CM. This confirms the theoretical result due to the fact that LMS uses a sequence for learning. Performance analyses of the two techniques are simulated in the MATLAB environment. |
| format | Article |
| id | doaj-art-89af75134fcd45d5a902cfebdb179a6f |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-89af75134fcd45d5a902cfebdb179a6f2025-02-03T06:05:55ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/7744625Performance Evaluation of LMS and CM Algorithms for BeamformingMossaab Atzemourt0Abdelmajid Farchi1Younes Chihab2Zakaria Hachkar3Fundamental and Applied Physics LaboratoryMechanical EngineeringLaboratory of Computer SciencesFundamental and Applied Physics LaboratoryIn this paper, we compare the performances of the least mean square (LMS) and constant modulus (CM) algorithms for beamforming. Our interest in these algorithms finds its origins in their reliability as a source-receiver pair. In addition, their use brings a great frequency of diversity even to respond quickly to the increasing spectral demand. The results suggest that the greater the number of elements in the antenna, the better the directivity for both LMS and CM. We also note that a judicious choice of the control parameter mu leads to a better speed of convergence for the two algorithms. Let us note, however, that LMS is more efficient. Our simulations show that in an environment affected by white Gaussian noise, LMS is more robust than CM. This confirms the theoretical result due to the fact that LMS uses a sequence for learning. Performance analyses of the two techniques are simulated in the MATLAB environment.http://dx.doi.org/10.1155/2022/7744625 |
| spellingShingle | Mossaab Atzemourt Abdelmajid Farchi Younes Chihab Zakaria Hachkar Performance Evaluation of LMS and CM Algorithms for Beamforming Advances in Materials Science and Engineering |
| title | Performance Evaluation of LMS and CM Algorithms for Beamforming |
| title_full | Performance Evaluation of LMS and CM Algorithms for Beamforming |
| title_fullStr | Performance Evaluation of LMS and CM Algorithms for Beamforming |
| title_full_unstemmed | Performance Evaluation of LMS and CM Algorithms for Beamforming |
| title_short | Performance Evaluation of LMS and CM Algorithms for Beamforming |
| title_sort | performance evaluation of lms and cm algorithms for beamforming |
| url | http://dx.doi.org/10.1155/2022/7744625 |
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