Empowering wireless communications and sensing with deep learning technology
In recent years, deep learning (DL) technologies have witnessed dramatic progress due to their nonlinearity. Deep learning has brought many breakthroughs in various fields, such as computer vision, natural language processing and speech recognition, which motivate researchers from other fields to ex...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/169971 |
| _version_ | 1811696158068703232 |
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| author | Ji, Sijie |
| author2 | Mo Li |
| author_facet | Mo Li Ji, Sijie |
| author_sort | Ji, Sijie |
| collection | NTU |
| description | In recent years, deep learning (DL) technologies have witnessed dramatic progress due to their nonlinearity. Deep learning has brought many breakthroughs in various fields, such as computer vision, natural language processing and speech recognition, which motivate researchers from other fields to explore the possibility of adopting deep learning techniques. Many efforts have been made and much progress has been witnessed in bioinformatics, medicine, material science, civil engineering, etc. The computer network and communications field as well. Both physical layers like coding and modulation schemes and upper layers like communication network deployment report remarkable progress.
Since it is in the early stage, there are still many issues to be solved and there remains huge potential. Specifically, this thesis explores the feasibility of using deep learning techniques to enhance next-generation communication efficiency and broaden the ubiquitous radio frequency (RF) sensing boundary. |
| first_indexed | 2024-10-01T07:34:55Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/169971 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:34:55Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1699712023-09-04T07:32:08Z Empowering wireless communications and sensing with deep learning technology Ji, Sijie Mo Li School of Computer Science and Engineering limo@ntu.edu.sg Engineering::Computer science and engineering In recent years, deep learning (DL) technologies have witnessed dramatic progress due to their nonlinearity. Deep learning has brought many breakthroughs in various fields, such as computer vision, natural language processing and speech recognition, which motivate researchers from other fields to explore the possibility of adopting deep learning techniques. Many efforts have been made and much progress has been witnessed in bioinformatics, medicine, material science, civil engineering, etc. The computer network and communications field as well. Both physical layers like coding and modulation schemes and upper layers like communication network deployment report remarkable progress. Since it is in the early stage, there are still many issues to be solved and there remains huge potential. Specifically, this thesis explores the feasibility of using deep learning techniques to enhance next-generation communication efficiency and broaden the ubiquitous radio frequency (RF) sensing boundary. Doctor of Philosophy 2023-08-18T07:43:40Z 2023-08-18T07:43:40Z 2023 Thesis-Doctor of Philosophy Ji, S. (2023). Empowering wireless communications and sensing with deep learning technology. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/169971 https://hdl.handle.net/10356/169971 10.32657/10356/169971 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Ji, Sijie Empowering wireless communications and sensing with deep learning technology |
| title | Empowering wireless communications and sensing with deep learning technology |
| title_full | Empowering wireless communications and sensing with deep learning technology |
| title_fullStr | Empowering wireless communications and sensing with deep learning technology |
| title_full_unstemmed | Empowering wireless communications and sensing with deep learning technology |
| title_short | Empowering wireless communications and sensing with deep learning technology |
| title_sort | empowering wireless communications and sensing with deep learning technology |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/169971 |
| work_keys_str_mv | AT jisijie empoweringwirelesscommunicationsandsensingwithdeeplearningtechnology |