Research on scenario recognition for THz channels based on mRMR-GA
To address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was construct...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2025-05-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025082 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136489562734592 |
|---|---|
| author | HAO Xinyu LIAO Xi WANG Yang LIN Feng LUO Jiao ZHANG Jie |
| author_facet | HAO Xinyu LIAO Xi WANG Yang LIN Feng LUO Jiao ZHANG Jie |
| author_sort | HAO Xinyu |
| collection | DOAJ |
| description | To address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was constructed based on feature selection theory and evolutionary computation principles. The crossover and mutation operations of channel characteristics were executed by the genetic algorithm (GA), and the optimal feature parameters with high scenario relevance were selected using the minimum redundancy maximum relevance (mRMR) criterion. These parameters were then inputed into a backpropagation neural network model. To validate the method, a dataset containing 12 channel features was constructed with 1 745 groups of terahertz channel simulation data collected from indoor scenarios, and the model was trained and rigorously validated based on this dataset. The results demonstrate that the proposed algorithm improves accuracy and efficiency by 8% and 38.8%, respectively, and outperforms traditional algorithms in terms of convergence and transfer generalization capabilities. |
| format | Article |
| id | doaj-art-5ef2ea1caed04f09a3bb1f16da662326 |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5ef2ea1caed04f09a3bb1f16da6623262025-08-20T02:31:08ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-05-014691102108584714Research on scenario recognition for THz channels based on mRMR-GAHAO XinyuLIAO XiWANG YangLIN FengLUO JiaoZHANG JieTo address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was constructed based on feature selection theory and evolutionary computation principles. The crossover and mutation operations of channel characteristics were executed by the genetic algorithm (GA), and the optimal feature parameters with high scenario relevance were selected using the minimum redundancy maximum relevance (mRMR) criterion. These parameters were then inputed into a backpropagation neural network model. To validate the method, a dataset containing 12 channel features was constructed with 1 745 groups of terahertz channel simulation data collected from indoor scenarios, and the model was trained and rigorously validated based on this dataset. The results demonstrate that the proposed algorithm improves accuracy and efficiency by 8% and 38.8%, respectively, and outperforms traditional algorithms in terms of convergence and transfer generalization capabilities.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025082THz channelscenario recognitionfeature selectiongenetic algorithmneural network |
| spellingShingle | HAO Xinyu LIAO Xi WANG Yang LIN Feng LUO Jiao ZHANG Jie Research on scenario recognition for THz channels based on mRMR-GA Tongxin xuebao THz channel scenario recognition feature selection genetic algorithm neural network |
| title | Research on scenario recognition for THz channels based on mRMR-GA |
| title_full | Research on scenario recognition for THz channels based on mRMR-GA |
| title_fullStr | Research on scenario recognition for THz channels based on mRMR-GA |
| title_full_unstemmed | Research on scenario recognition for THz channels based on mRMR-GA |
| title_short | Research on scenario recognition for THz channels based on mRMR-GA |
| title_sort | research on scenario recognition for thz channels based on mrmr ga |
| topic | THz channel scenario recognition feature selection genetic algorithm neural network |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025082 |
| work_keys_str_mv | AT haoxinyu researchonscenariorecognitionforthzchannelsbasedonmrmrga AT liaoxi researchonscenariorecognitionforthzchannelsbasedonmrmrga AT wangyang researchonscenariorecognitionforthzchannelsbasedonmrmrga AT linfeng researchonscenariorecognitionforthzchannelsbasedonmrmrga AT luojiao researchonscenariorecognitionforthzchannelsbasedonmrmrga AT zhangjie researchonscenariorecognitionforthzchannelsbasedonmrmrga |