Deep learning-based algorithms for high capacity transmission of orthogonal multiple access and non-orthogonal multiple access systems
Over the past decade, with the massive spike in the use of data-hungry applications such as streaming Netflix in 4k, playing graphic-intensive PC games, and online learning, there is a need for a faster data transmission rate. In this project, we aim to apply deep learning techniques to current data...
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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/10356/172966 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Over the past decade, with the massive spike in the use of data-hungry applications such as streaming Netflix in 4k, playing graphic-intensive PC games, and online learning, there is a need for a faster data transmission rate. In this project, we aim to apply deep learning techniques to current data transmission methods such as orthogonal frequency-division multiple access (OFDMA) and upcoming non-orthogonal multiple access (NOMA) method to improve the capacity of a channel. The student will explore the advantage of deep-learning techniques to improve the system capacity and compare with existing conventional methods. Matlab or Python programming will be used to study the performance of the proposed scheme. |
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