OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario
In order to solve the problem of integrated sensing and communication (ISAC) signal transmission channel estimation in commercial B5G/6G urban rail train-infrastructure scenario, a channel estimation method based on deep learning was proposed. An ISAC signal transmission system model based on orthog...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025032/ |
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| author | YANG Qian SU Hongsheng TAO Wanglin LIU Dawei |
| author_facet | YANG Qian SU Hongsheng TAO Wanglin LIU Dawei |
| author_sort | YANG Qian |
| collection | DOAJ |
| description | In order to solve the problem of integrated sensing and communication (ISAC) signal transmission channel estimation in commercial B5G/6G urban rail train-infrastructure scenario, a channel estimation method based on deep learning was proposed. An ISAC signal transmission system model based on orthogonal time frequency space (OTFS) modulation was established, the OTFS pilot was introduced, with OTFS pilot introduced to aid, CGAN-LSTM combining conditional generative adversarial network (CGAN) and long short-term memory (LSTM) network was designed. Chaos game optimization (CGO) algorithm was combined with classical Adam optimizer to optimize the network parameters, and the optimized network was used to complete the channel estimation. Simulation results show that the proposed method is superior to traditional channel estimation methods in normalized mean square error and bit error rate, and provides necessary data basis for ISAC signal detection and recovery. |
| format | Article |
| id | doaj-art-d644294fe4294e8580db30d1079eb822 |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-d644294fe4294e8580db30d1079eb8222025-08-20T03:01:35ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-02-0146597185454836OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenarioYANG QianSU HongshengTAO WanglinLIU DaweiIn order to solve the problem of integrated sensing and communication (ISAC) signal transmission channel estimation in commercial B5G/6G urban rail train-infrastructure scenario, a channel estimation method based on deep learning was proposed. An ISAC signal transmission system model based on orthogonal time frequency space (OTFS) modulation was established, the OTFS pilot was introduced, with OTFS pilot introduced to aid, CGAN-LSTM combining conditional generative adversarial network (CGAN) and long short-term memory (LSTM) network was designed. Chaos game optimization (CGO) algorithm was combined with classical Adam optimizer to optimize the network parameters, and the optimized network was used to complete the channel estimation. Simulation results show that the proposed method is superior to traditional channel estimation methods in normalized mean square error and bit error rate, and provides necessary data basis for ISAC signal detection and recovery.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025032/ISACOTFSCGANLSTMCGO |
| spellingShingle | YANG Qian SU Hongsheng TAO Wanglin LIU Dawei OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario Tongxin xuebao ISAC OTFS CGAN LSTM CGO |
| title | OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario |
| title_full | OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario |
| title_fullStr | OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario |
| title_full_unstemmed | OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario |
| title_short | OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario |
| title_sort | otfs isac system channel estimation based on gan lstm network in urban rail train infrastructure scenario |
| topic | ISAC OTFS CGAN LSTM CGO |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025032/ |
| work_keys_str_mv | AT yangqian otfsisacsystemchannelestimationbasedonganlstmnetworkinurbanrailtraininfrastructurescenario AT suhongsheng otfsisacsystemchannelestimationbasedonganlstmnetworkinurbanrailtraininfrastructurescenario AT taowanglin otfsisacsystemchannelestimationbasedonganlstmnetworkinurbanrailtraininfrastructurescenario AT liudawei otfsisacsystemchannelestimationbasedonganlstmnetworkinurbanrailtraininfrastructurescenario |