Step-by-step classification detection algorithm of SPPM based on K-means clustering
In view of the high computational complexity in spatial pulse position modulation systems when using maximum likelihood detection algorithm, a step-by-step classification detection algorithm based on K-means clustering was proposed according to the characteristics of signal matrix with spatial pulse...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022010/ |
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| author | Huiqin WANG Wenbin HOU Qingbin PENG Minghua CAO Rui HUANG Ling LIU |
| author_facet | Huiqin WANG Wenbin HOU Qingbin PENG Minghua CAO Rui HUANG Ling LIU |
| author_sort | Huiqin WANG |
| collection | DOAJ |
| description | In view of the high computational complexity in spatial pulse position modulation systems when using maximum likelihood detection algorithm, a step-by-step classification detection algorithm based on K-means clustering was proposed according to the characteristics of signal matrix with spatial pulse position modulation.The signal vector detection algorithm was utilized to detect the index of light source in the training samples.The on K-means clustering algorithm was utilized to acquire the mapping rule between centroid of samples and modulated symbol by offline training.Subsequently, online detection of modulated symbols was achieved based on the mapping rule, and then the index of light sources was detected by exhaustive search.In addition, Monte Carlo method was used to investigate the effects of key parameters such as the number of clusters and initialization times on the system bit error rate (BER) performance.Simulation results demonstrate that the proposed algorithm can achieve an approximate BER performance as the maximum likelihood algorithm on the basis of greatly reducing the computational complexity.Compared with the linear decoding algorithms, the proposed algorithm is also applicable to scenarios where the number of detectors is less than the number of light sources. |
| format | Article |
| id | doaj-art-fb46ea19fca04498a5c7f2c27e753211 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-fb46ea19fca04498a5c7f2c27e7532112025-01-14T06:30:30ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-01-014316117159398612Step-by-step classification detection algorithm of SPPM based on K-means clusteringHuiqin WANGWenbin HOUQingbin PENGMinghua CAORui HUANGLing LIUIn view of the high computational complexity in spatial pulse position modulation systems when using maximum likelihood detection algorithm, a step-by-step classification detection algorithm based on K-means clustering was proposed according to the characteristics of signal matrix with spatial pulse position modulation.The signal vector detection algorithm was utilized to detect the index of light source in the training samples.The on K-means clustering algorithm was utilized to acquire the mapping rule between centroid of samples and modulated symbol by offline training.Subsequently, online detection of modulated symbols was achieved based on the mapping rule, and then the index of light sources was detected by exhaustive search.In addition, Monte Carlo method was used to investigate the effects of key parameters such as the number of clusters and initialization times on the system bit error rate (BER) performance.Simulation results demonstrate that the proposed algorithm can achieve an approximate BER performance as the maximum likelihood algorithm on the basis of greatly reducing the computational complexity.Compared with the linear decoding algorithms, the proposed algorithm is also applicable to scenarios where the number of detectors is less than the number of light sources.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022010/wireless optical communicationspatial pulse position modulationK-means clusteringstep-by-step classifi-cation detection |
| spellingShingle | Huiqin WANG Wenbin HOU Qingbin PENG Minghua CAO Rui HUANG Ling LIU Step-by-step classification detection algorithm of SPPM based on K-means clustering Tongxin xuebao wireless optical communication spatial pulse position modulation K-means clustering step-by-step classifi-cation detection |
| title | Step-by-step classification detection algorithm of SPPM based on K-means clustering |
| title_full | Step-by-step classification detection algorithm of SPPM based on K-means clustering |
| title_fullStr | Step-by-step classification detection algorithm of SPPM based on K-means clustering |
| title_full_unstemmed | Step-by-step classification detection algorithm of SPPM based on K-means clustering |
| title_short | Step-by-step classification detection algorithm of SPPM based on K-means clustering |
| title_sort | step by step classification detection algorithm of sppm based on k means clustering |
| topic | wireless optical communication spatial pulse position modulation K-means clustering step-by-step classifi-cation detection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022010/ |
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