SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection
In order to solve the problem of small capacity and high energy consumption in China’s 5G communication technology system, the research proposes that based on the segmented weakly orthogonal matching pursuit (SWOMP) algorithm, it is combined with the compressed sensing matching pursuit algorithm to...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2023/1374601 |
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| author | Tao Fu Yanfeng Yu Cheng Liu |
| author_facet | Tao Fu Yanfeng Yu Cheng Liu |
| author_sort | Tao Fu |
| collection | DOAJ |
| description | In order to solve the problem of small capacity and high energy consumption in China’s 5G communication technology system, the research proposes that based on the segmented weakly orthogonal matching pursuit (SWOMP) algorithm, it is combined with the compressed sensing matching pursuit algorithm to form a segmented backtracking weak selection positive algorithm and Cross Match Tracking (SCWOMP) algorithm. First, the sparseness of MIMO system technology and its transmission structure is analyzed. Then, the new model is built after comparing with other algorithms, and the problem of overestimating the low recovery probability in the calculation process is improved by the backtracking of the algorithm and the improvement of the angle of the atomic column selection, so as to reduce the number of iterations and improve the performance of the algorithm. The results show that, in the performance comparison of different sampling points under different compressed sensing recovery algorithms, the recovery probability of the SCWOMP algorithm is the best, and when the number of sampling points is 80, although the fixed step size of the SCWOMP algorithm is different, there is recovery. The probability has a maximum value, close to 1. Then, the improved compressed sensing recovery algorithm is simulated and analyzed. When the pruning coefficient is 0.5 and the number of sampling points is 80, the reconstruction rate has a maximum value, and when other algorithms reach the maximum reconstruction rate, the number of sampling points (M) is significantly greater than that of the SCWOMP algorithm. An increase in the rate of reduction of the reconstruction probability of the SCWOMP algorithm is significantly lower than that of other algorithms; when sparsity is equal to 70, the reconstruction probability becomes 0, indicating that SCWOMP has a wider reconfigurable range and has a significant performance effect. This shows that the proposed SCWOMP algorithm has the best detection performance for 5G communication symbol detection, which can effectively increase the capacity of the system and better promote technology. |
| format | Article |
| id | doaj-art-3db09405f26e4a39ae65580ec79dbeb9 |
| institution | Kabale University |
| issn | 2090-715X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-3db09405f26e4a39ae65580ec79dbeb92025-02-03T06:48:31ZengWileyJournal of Computer Networks and Communications2090-715X2023-01-01202310.1155/2023/1374601SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol DetectionTao Fu0Yanfeng Yu1Cheng Liu2College of Electronic EngineeringCollege of Electronic EngineeringCollege of Electronic EngineeringIn order to solve the problem of small capacity and high energy consumption in China’s 5G communication technology system, the research proposes that based on the segmented weakly orthogonal matching pursuit (SWOMP) algorithm, it is combined with the compressed sensing matching pursuit algorithm to form a segmented backtracking weak selection positive algorithm and Cross Match Tracking (SCWOMP) algorithm. First, the sparseness of MIMO system technology and its transmission structure is analyzed. Then, the new model is built after comparing with other algorithms, and the problem of overestimating the low recovery probability in the calculation process is improved by the backtracking of the algorithm and the improvement of the angle of the atomic column selection, so as to reduce the number of iterations and improve the performance of the algorithm. The results show that, in the performance comparison of different sampling points under different compressed sensing recovery algorithms, the recovery probability of the SCWOMP algorithm is the best, and when the number of sampling points is 80, although the fixed step size of the SCWOMP algorithm is different, there is recovery. The probability has a maximum value, close to 1. Then, the improved compressed sensing recovery algorithm is simulated and analyzed. When the pruning coefficient is 0.5 and the number of sampling points is 80, the reconstruction rate has a maximum value, and when other algorithms reach the maximum reconstruction rate, the number of sampling points (M) is significantly greater than that of the SCWOMP algorithm. An increase in the rate of reduction of the reconstruction probability of the SCWOMP algorithm is significantly lower than that of other algorithms; when sparsity is equal to 70, the reconstruction probability becomes 0, indicating that SCWOMP has a wider reconfigurable range and has a significant performance effect. This shows that the proposed SCWOMP algorithm has the best detection performance for 5G communication symbol detection, which can effectively increase the capacity of the system and better promote technology.http://dx.doi.org/10.1155/2023/1374601 |
| spellingShingle | Tao Fu Yanfeng Yu Cheng Liu SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection Journal of Computer Networks and Communications |
| title | SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection |
| title_full | SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection |
| title_fullStr | SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection |
| title_full_unstemmed | SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection |
| title_short | SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection |
| title_sort | scwomp recovery algorithm for 5g mimo communication symbol detection |
| url | http://dx.doi.org/10.1155/2023/1374601 |
| work_keys_str_mv | AT taofu scwomprecoveryalgorithmfor5gmimocommunicationsymboldetection AT yanfengyu scwomprecoveryalgorithmfor5gmimocommunicationsymboldetection AT chengliu scwomprecoveryalgorithmfor5gmimocommunicationsymboldetection |