Channel Estimation Based on Deep Learning for OCDM Communications
Orthogonal chirp division multiplexing (OCDM) takes advantage of double spreading in both time and frequency domains for high rate communications. Multipath channels often deteriorate communication performance. Thus, channel estimation is preliminary for equalization to combat multipath. In OCDM sys...
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| Published in | 2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 286 - 290 |
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| Main Authors | , , , |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
11.08.2022
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| Subjects | |
| Online Access | Get full text |
| DOI | 10.1109/ICCC55456.2022.9880853 |
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| Abstract | Orthogonal chirp division multiplexing (OCDM) takes advantage of double spreading in both time and frequency domains for high rate communications. Multipath channels often deteriorate communication performance. Thus, channel estimation is preliminary for equalization to combat multipath. In OCDM systems, either independent pilot blocks are employed or extra null symbols have to be applied to accommodate inter-chirp interference by the conventional channel estimation methods. The bandwidth efficiency is reduced. In this paper, we develop a deep learning (DL) based channel estimation method for OCDM systems. Making use of the power of DL, the requirements of pilot symbols are relaxed and the bandwidth efficiency is increased. Simulation results corroborate the efficiency of the DL-based method compared with the conventional ones. |
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| AbstractList | Orthogonal chirp division multiplexing (OCDM) takes advantage of double spreading in both time and frequency domains for high rate communications. Multipath channels often deteriorate communication performance. Thus, channel estimation is preliminary for equalization to combat multipath. In OCDM systems, either independent pilot blocks are employed or extra null symbols have to be applied to accommodate inter-chirp interference by the conventional channel estimation methods. The bandwidth efficiency is reduced. In this paper, we develop a deep learning (DL) based channel estimation method for OCDM systems. Making use of the power of DL, the requirements of pilot symbols are relaxed and the bandwidth efficiency is increased. Simulation results corroborate the efficiency of the DL-based method compared with the conventional ones. |
| Author | Liu, Lingya Yang, Daoyong Wang, Yiyin Huang, Peishuo |
| Author_xml | – sequence: 1 givenname: Daoyong surname: Yang fullname: Yang, Daoyong email: ydyhll@sjtu.edu.cn organization: Shanghai Jiao Tong University,Department of Automation,Shanghai,China – sequence: 2 givenname: Yiyin surname: Wang fullname: Wang, Yiyin email: yiyinwang@sjtu.edu.cn organization: Shanghai Jiao Tong University,Department of Automation,Shanghai,China – sequence: 3 givenname: Lingya surname: Liu fullname: Liu, Lingya email: lyliu@cee.ecnu.edu.cn organization: School of Communication and Electronic Engineering, East China Normal University,Shanghai,China – sequence: 4 givenname: Peishuo surname: Huang fullname: Huang, Peishuo email: huangpeishuo@163.com organization: School of Information and Communication Engineering, Hainan University,Haikou,China |
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| Snippet | Orthogonal chirp division multiplexing (OCDM) takes advantage of double spreading in both time and frequency domains for high rate communications. Multipath... |
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| StartPage | 286 |
| SubjectTerms | Channel estimation Code division multiplexing Deep learning Interference OCDM Simulation Spectral efficiency Symbols |
| Title | Channel Estimation Based on Deep Learning for OCDM Communications |
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