Data-Driven OTFS Channel Estimation Based on Gated Recurrent Convolutional Autoencoder
Considering the traffic environment with highmoving vehicles, orthogonal time frequency space (OTFS) has become an emerging technology to handle the rapid timevarying channels via vehicular communications. Due to sparse representation of the delay-Doppler (DD) domain, the related channel information...
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| Vydané v: | International Symposium on Communications and Information Technologies (Online) s. 7 - 12 |
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| Jazyk: | English |
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16.10.2023
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| ISSN: | 2643-6175 |
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| Abstract | Considering the traffic environment with highmoving vehicles, orthogonal time frequency space (OTFS) has become an emerging technology to handle the rapid timevarying channels via vehicular communications. Due to sparse representation of the delay-Doppler (DD) domain, the related channel information can be estimated by means of the embedded pilot technique. However, the uncertainties of unknown and burst noise can incur system performance degradation issues. To tackle this problem, in this paper, we propose a novel gated recurrent convolutional autoencoder (GRCAE) model to denoise the complex noise for channel estimation in OTFS systems. Specifically, the proposed model can distinguish and retain the significant features of the signal during the denoising process through the gated recurrent unit (GRU) network. Meanwhile, the convolutional autoencoder can better capture the local spatial features of the signal and reconstruct them to obtain a denoised signal. The parallel procedure further improves the denoising accuracy and robustness. Our simulation results demonstrate that the proposed GRCAEbased approach present satisfactory performance in various noise scenarios. |
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| AbstractList | Considering the traffic environment with highmoving vehicles, orthogonal time frequency space (OTFS) has become an emerging technology to handle the rapid timevarying channels via vehicular communications. Due to sparse representation of the delay-Doppler (DD) domain, the related channel information can be estimated by means of the embedded pilot technique. However, the uncertainties of unknown and burst noise can incur system performance degradation issues. To tackle this problem, in this paper, we propose a novel gated recurrent convolutional autoencoder (GRCAE) model to denoise the complex noise for channel estimation in OTFS systems. Specifically, the proposed model can distinguish and retain the significant features of the signal during the denoising process through the gated recurrent unit (GRU) network. Meanwhile, the convolutional autoencoder can better capture the local spatial features of the signal and reconstruct them to obtain a denoised signal. The parallel procedure further improves the denoising accuracy and robustness. Our simulation results demonstrate that the proposed GRCAEbased approach present satisfactory performance in various noise scenarios. |
| Author | Chen, Junshen Zhang, Shiyao Yuan, Qihao Liu, Chang |
| Author_xml | – sequence: 1 givenname: Junshen surname: Chen fullname: Chen, Junshen email: 2021280156@email.szu.edu.cn organization: Shenzhen University,Shenzhen,China – sequence: 2 givenname: Qihao surname: Yuan fullname: Yuan, Qihao email: 2021190035@email.szu.edu.cn organization: Shenzhen University,Shenzhen,China – sequence: 3 givenname: Shiyao surname: Zhang fullname: Zhang, Shiyao email: zhangsy@sustech.edu.cn organization: Southern University of Science and Technology,Shenzhen,China – sequence: 4 givenname: Chang surname: Liu fullname: Liu, Chang email: changwcom.liu@polyu.edu.hk organization: The Hong Kong Polytechnic University,Hong Kong SAR,China |
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| Snippet | Considering the traffic environment with highmoving vehicles, orthogonal time frequency space (OTFS) has become an emerging technology to handle the rapid... |
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| SubjectTerms | Channel estimation Convolution gated recurrent convolutional autoencoder Noise reduction orthogonal time frequency space Simulation System performance Time-frequency analysis Uncertainty |
| Title | Data-Driven OTFS Channel Estimation Based on Gated Recurrent Convolutional Autoencoder |
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