Deep learning-based receiver for 5G communication system under doubly selective fading channel
With the increasing research on the fifth generation (5G) communication systems, especially in doubly selective fading channels, receiver designs based on deep learning have attracted widespread attention. This thesis proposes a receiver design utilizing deep learning, combining Convolutional Neural...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/180310 |
| _version_ | 1826115464814657536 |
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| author | Wan, Yuxuan |
| author2 | Teh Kah Chan |
| author_facet | Teh Kah Chan Wan, Yuxuan |
| author_sort | Wan, Yuxuan |
| collection | NTU |
| description | With the increasing research on the fifth generation (5G) communication systems, especially in doubly selective fading channels, receiver designs based on deep learning have attracted widespread attention. This thesis proposes a receiver design utilizing deep learning, combining Convolutional Neural Networks (CNN) for spatiotemporal feature extraction and Recurrent Neural Networks (RNN) for capturing temporal dependencies and exploiting channel dynamics. Through joint optimization and parameter training, the receiver aims to improve the bit error rate (BER) and detection accuracy. Extensive simulations are conducted in Orthogonal Frequency Division multiplexing (OFDM) systems to evaluate the performance of the proposed receiver in comparison to traditional methods. The results indicate that deep learning-based receivers demonstrate excellent reliability and performance, providing an effective solution to enhance communication system performance in time and frequency-selective fading environments. |
| first_indexed | 2025-03-09T11:23:37Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/180310 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T11:23:37Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1803102024-10-04T15:43:47Z Deep learning-based receiver for 5G communication system under doubly selective fading channel Wan, Yuxuan Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering 5G communication OFDM Doubly selective fading channel Deep learning With the increasing research on the fifth generation (5G) communication systems, especially in doubly selective fading channels, receiver designs based on deep learning have attracted widespread attention. This thesis proposes a receiver design utilizing deep learning, combining Convolutional Neural Networks (CNN) for spatiotemporal feature extraction and Recurrent Neural Networks (RNN) for capturing temporal dependencies and exploiting channel dynamics. Through joint optimization and parameter training, the receiver aims to improve the bit error rate (BER) and detection accuracy. Extensive simulations are conducted in Orthogonal Frequency Division multiplexing (OFDM) systems to evaluate the performance of the proposed receiver in comparison to traditional methods. The results indicate that deep learning-based receivers demonstrate excellent reliability and performance, providing an effective solution to enhance communication system performance in time and frequency-selective fading environments. Master's degree 2024-10-01T11:22:04Z 2024-10-01T11:22:04Z 2024 Thesis-Master by Coursework Wan, Y. (2024). Deep learning-based receiver for 5G communication system under doubly selective fading channel. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180310 https://hdl.handle.net/10356/180310 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering 5G communication OFDM Doubly selective fading channel Deep learning Wan, Yuxuan Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title | Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title_full | Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title_fullStr | Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title_full_unstemmed | Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title_short | Deep learning-based receiver for 5G communication system under doubly selective fading channel |
| title_sort | deep learning based receiver for 5g communication system under doubly selective fading channel |
| topic | Engineering 5G communication OFDM Doubly selective fading channel Deep learning |
| url | https://hdl.handle.net/10356/180310 |
| work_keys_str_mv | AT wanyuxuan deeplearningbasedreceiverfor5gcommunicationsystemunderdoublyselectivefadingchannel |