Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems
the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01078.pdf |
| _version_ | 1817976760872992768 |
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| author | Belhabib Abdelfettah Amadid Jamal Khabba Asma Zeroual Abdelouhab |
| author_facet | Belhabib Abdelfettah Amadid Jamal Khabba Asma Zeroual Abdelouhab |
| author_sort | Belhabib Abdelfettah |
| collection | DOAJ |
| description | the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems. |
| first_indexed | 2024-04-13T22:07:52Z |
| format | Article |
| id | doaj.art-356c3e1e2a184d4ebe4288bef35c0325 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-13T22:07:52Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-356c3e1e2a184d4ebe4288bef35c03252022-12-22T02:27:53ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013510107810.1051/e3sconf/202235101078e3sconf_icies2022_01078Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systemsBelhabib Abdelfettah0Amadid Jamal1Khabba Asma2Zeroual Abdelouhab3I2SP team Group, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP team Group, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP team Group, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP team Group, Faculty of Sciences Semlalia, Cadi Ayyad Universitythe pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01078.pdf |
| spellingShingle | Belhabib Abdelfettah Amadid Jamal Khabba Asma Zeroual Abdelouhab Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems E3S Web of Conferences |
| title | Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems |
| title_full | Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems |
| title_fullStr | Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems |
| title_full_unstemmed | Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems |
| title_short | Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems |
| title_sort | ant colony based optimization algorithm to overcome the pilot contamination issue within multi cell massive mimo systems |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01078.pdf |
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