CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario
Orthogonal time frequency space (OTFS) is fully applied in high Doppler communication scenarios due to its good Doppler frequency bias and time delay adaptability.The channel estimation methods for OTFS systems have shortcomings such as high complexity and poor BER performance.A CNN-based channel es...
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| Format: | Article |
| Language: | zho |
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Post&Telecom Press Co.,LTD
2022-09-01
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| Series: | 天地一体化信息网络 |
| Subjects: | |
| Online Access: | http://www.j-sigin.com.cn/zh/article/doi/10.11959/j.issn.2096-8930.2022030/ |
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| _version_ | 1841534004570882048 |
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| author | Cheng GUO Le YU Lidong ZHU |
| author_facet | Cheng GUO Le YU Lidong ZHU |
| author_sort | Cheng GUO |
| collection | DOAJ |
| description | Orthogonal time frequency space (OTFS) is fully applied in high Doppler communication scenarios due to its good Doppler frequency bias and time delay adaptability.The channel estimation methods for OTFS systems have shortcomings such as high complexity and poor BER performance.A CNN-based channel estimation method for OTFS systems in the terrestrial-satellite scenario using a convolutional neural network (CNN) approach was proposed.Simulation results showed that the deep learning-based method outperformed the conventional method in terms of algorithm complexity and BER in the terrestrial-satellite scenario, thus demonstrating that deep learning is a promising tool for channel estimation in OTFS systems. |
| format | Article |
| id | doaj-art-7d9b4c2b36ba43e18f87b6dbea3849c3 |
| institution | Kabale University |
| issn | 2096-8930 |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | Post&Telecom Press Co.,LTD |
| record_format | Article |
| series | 天地一体化信息网络 |
| spelling | doaj-art-7d9b4c2b36ba43e18f87b6dbea3849c32025-01-15T02:48:07ZzhoPost&Telecom Press Co.,LTD天地一体化信息网络2096-89302022-09-013374559530836CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground ScenarioCheng GUOLe YULidong ZHUOrthogonal time frequency space (OTFS) is fully applied in high Doppler communication scenarios due to its good Doppler frequency bias and time delay adaptability.The channel estimation methods for OTFS systems have shortcomings such as high complexity and poor BER performance.A CNN-based channel estimation method for OTFS systems in the terrestrial-satellite scenario using a convolutional neural network (CNN) approach was proposed.Simulation results showed that the deep learning-based method outperformed the conventional method in terms of algorithm complexity and BER in the terrestrial-satellite scenario, thus demonstrating that deep learning is a promising tool for channel estimation in OTFS systems.http://www.j-sigin.com.cn/zh/article/doi/10.11959/j.issn.2096-8930.2022030/satellite to ground communicationOTFSdeep learningchannel estimation |
| spellingShingle | Cheng GUO Le YU Lidong ZHU CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario 天地一体化信息网络 satellite to ground communication OTFS deep learning channel estimation |
| title | CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario |
| title_full | CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario |
| title_fullStr | CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario |
| title_full_unstemmed | CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario |
| title_short | CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario |
| title_sort | cnn based channel estimation method for otfs system in satellite ground scenario |
| topic | satellite to ground communication OTFS deep learning channel estimation |
| url | http://www.j-sigin.com.cn/zh/article/doi/10.11959/j.issn.2096-8930.2022030/ |
| work_keys_str_mv | AT chengguo cnnbasedchannelestimationmethodforotfssysteminsatellitegroundscenario AT leyu cnnbasedchannelestimationmethodforotfssysteminsatellitegroundscenario AT lidongzhu cnnbasedchannelestimationmethodforotfssysteminsatellitegroundscenario |