A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO
Despite the advantages of massive multi-input multi-output (MIMO) technology, traditional multi-antenna detection algorithms are not suitable for systems with large-scale antennas, and the use of this technology requires a significant increase in computational costs. In this paper, a low-complexity...
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University of Qom
2024-03-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_2789_953502ef91fbaf505bb37367d8be7994.pdf |
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| author | Hamid Amiriara Mohammadreza Zahabi |
| author_facet | Hamid Amiriara Mohammadreza Zahabi |
| author_sort | Hamid Amiriara |
| collection | DOAJ |
| description | Despite the advantages of massive multi-input multi-output (MIMO) technology, traditional multi-antenna detection algorithms are not suitable for systems with large-scale antennas, and the use of this technology requires a significant increase in computational costs. In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. The TLBO algorithm, as one of the advanced methods of intelligence, is very useful for large-scale problems. Therefore, we use this method to search for the optimal solution vector in the modulation alphabet. In order to prove the accuracy and efficiency of the detector, it was suggested to simulate the system with different dimensions from 64×64 to 1024×1024. The proposed TLBO detector, in a limited time, achieves a bit error rate (BER) 10^(-5) in the average signal-to-noise ratio of 12 dB, which is approximately equal to the optimal detector performance, and maximum likelihood. As a result, the proposed detector is very efficient for use in massive MIMO systems. |
| format | Article |
| id | doaj-art-2bd873e6c2e648f3a90f4efc8de6eed0 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2024-03-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-2bd873e6c2e648f3a90f4efc8de6eed02025-01-30T20:19:06ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752024-03-0192354910.22091/jemsc.2024.8730.11672789A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMOHamid Amiriara0Mohammadreza Zahabi1Electrical Engineering, Sharif University of Technology, Tehran, Iran.Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, IranDespite the advantages of massive multi-input multi-output (MIMO) technology, traditional multi-antenna detection algorithms are not suitable for systems with large-scale antennas, and the use of this technology requires a significant increase in computational costs. In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. The TLBO algorithm, as one of the advanced methods of intelligence, is very useful for large-scale problems. Therefore, we use this method to search for the optimal solution vector in the modulation alphabet. In order to prove the accuracy and efficiency of the detector, it was suggested to simulate the system with different dimensions from 64×64 to 1024×1024. The proposed TLBO detector, in a limited time, achieves a bit error rate (BER) 10^(-5) in the average signal-to-noise ratio of 12 dB, which is approximately equal to the optimal detector performance, and maximum likelihood. As a result, the proposed detector is very efficient for use in massive MIMO systems.https://jemsc.qom.ac.ir/article_2789_953502ef91fbaf505bb37367d8be7994.pdf5g wireless communicationdetection algorithmmassive multi input multi outputteaching-learning based optimization |
| spellingShingle | Hamid Amiriara Mohammadreza Zahabi A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO مدیریت مهندسی و رایانش نرم 5g wireless communication detection algorithm massive multi input multi output teaching-learning based optimization |
| title | A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO |
| title_full | A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO |
| title_fullStr | A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO |
| title_full_unstemmed | A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO |
| title_short | A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO |
| title_sort | low complexity near optimal detector based on teaching learning algorithm for massive mimo |
| topic | 5g wireless communication detection algorithm massive multi input multi output teaching-learning based optimization |
| url | https://jemsc.qom.ac.ir/article_2789_953502ef91fbaf505bb37367d8be7994.pdf |
| work_keys_str_mv | AT hamidamiriara alowcomplexitynearoptimaldetectorbasedonteachinglearningalgorithmformassivemimo AT mohammadrezazahabi alowcomplexitynearoptimaldetectorbasedonteachinglearningalgorithmformassivemimo AT hamidamiriara lowcomplexitynearoptimaldetectorbasedonteachinglearningalgorithmformassivemimo AT mohammadrezazahabi lowcomplexitynearoptimaldetectorbasedonteachinglearningalgorithmformassivemimo |