Transfer learning for 5G communication scenarios under different mobile speed
The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change. Compared with traditiona...
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| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/161193 |
| _version_ | 1811693215533760512 |
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| author | Liu, Haozhong |
| author2 | Teh Kah Chan |
| author_facet | Teh Kah Chan Liu, Haozhong |
| author_sort | Liu, Haozhong |
| collection | NTU |
| description | The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change.
Compared with traditional machine learning, domain adaptation, a case in transfer learning, can be a more appropriate approach to reduce the influence caused by the Doppler effect and multi-path propagation to increase the reliability of communication. In this case, although the source data and target data might not be independent and identically distributed because of the changes in communication scenarios, a model could be implemented to recover the received signals with a low symbol-error rate (SER), and the training time and the computation complexity can be reduced to satisfy the requirement of low processing duration compared with learning from scratch. |
| first_indexed | 2024-10-01T06:48:08Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/161193 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:48:08Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1611932022-08-19T02:52:00Z Transfer learning for 5G communication scenarios under different mobile speed Liu, Haozhong Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering::Electrical and electronic engineering The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change. Compared with traditional machine learning, domain adaptation, a case in transfer learning, can be a more appropriate approach to reduce the influence caused by the Doppler effect and multi-path propagation to increase the reliability of communication. In this case, although the source data and target data might not be independent and identically distributed because of the changes in communication scenarios, a model could be implemented to recover the received signals with a low symbol-error rate (SER), and the training time and the computation complexity can be reduced to satisfy the requirement of low processing duration compared with learning from scratch. Master of Science (Communications Engineering) 2022-08-19T02:52:00Z 2022-08-19T02:52:00Z 2022 Thesis-Master by Coursework Liu, H. (2022). Transfer learning for 5G communication scenarios under different mobile speed. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161193 https://hdl.handle.net/10356/161193 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Liu, Haozhong Transfer learning for 5G communication scenarios under different mobile speed |
| title | Transfer learning for 5G communication scenarios under different mobile speed |
| title_full | Transfer learning for 5G communication scenarios under different mobile speed |
| title_fullStr | Transfer learning for 5G communication scenarios under different mobile speed |
| title_full_unstemmed | Transfer learning for 5G communication scenarios under different mobile speed |
| title_short | Transfer learning for 5G communication scenarios under different mobile speed |
| title_sort | transfer learning for 5g communication scenarios under different mobile speed |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/161193 |
| work_keys_str_mv | AT liuhaozhong transferlearningfor5gcommunicationscenariosunderdifferentmobilespeed |