Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures
Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Fi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9060948/ |
| _version_ | 1818566841489948672 |
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| author | Peiyang Song Fengkui Gong Qiang Li Guo Li Haiyang Ding |
| author_facet | Peiyang Song Fengkui Gong Qiang Li Guo Li Haiyang Ding |
| author_sort | Peiyang Song |
| collection | DOAJ |
| description | Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design. |
| first_indexed | 2024-12-14T01:58:47Z |
| format | Article |
| id | doaj.art-cf7b7146ee5141b4bd871d8870861d1f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T01:58:47Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cf7b7146ee5141b4bd871d8870861d1f2022-12-21T23:21:06ZengIEEEIEEE Access2169-35362020-01-018688666887310.1109/ACCESS.2020.29866799060948Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based ArchitecturesPeiyang Song0https://orcid.org/0000-0001-6605-6056Fengkui Gong1https://orcid.org/0000-0002-4211-0959Qiang Li2Guo Li3https://orcid.org/0000-0002-7661-6691Haiyang Ding4State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaSchool of Information and Communications, National University of Defense Technology, Xi’an, ChinaFaster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design.https://ieeexplore.ieee.org/document/9060948/Faster-than-Nyquistreceiver designsignal detectiondeep learningintersymbol interferencechannel coding |
| spellingShingle | Peiyang Song Fengkui Gong Qiang Li Guo Li Haiyang Ding Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures IEEE Access Faster-than-Nyquist receiver design signal detection deep learning intersymbol interference channel coding |
| title | Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures |
| title_full | Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures |
| title_fullStr | Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures |
| title_full_unstemmed | Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures |
| title_short | Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures |
| title_sort | receiver design for faster than nyquist signaling deep learning based architectures |
| topic | Faster-than-Nyquist receiver design signal detection deep learning intersymbol interference channel coding |
| url | https://ieeexplore.ieee.org/document/9060948/ |
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