Design of deep learning based receiver for downlink NOMA system
Non-Orthogonal Multiple Access (NOMA) is a key technique that enables fifthgeneration mobile communication systems to function effectively. NOMA has received high attention in wireless communication. NOMA serves users simultaneously while enhancing frequency and spectral efficiency. The main NOMA...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173684 |
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| Summary: | Non-Orthogonal Multiple Access (NOMA) is a key technique that enables fifthgeneration
mobile communication systems to function effectively. NOMA has
received high attention in wireless communication. NOMA serves users simultaneously
while enhancing frequency and spectral efficiency. The main NOMA
detection approach is successive interference cancellation (SIC), which decodes
signals at each user equipment (UE). However, SIC has issues with error propagation
and power order constraints. Deep learning (DL) makes the system computationally
simpler and mitigates the problems of error propagation encountered
with traditional SIC schemes. In this dissertation, we explore the DL-based
method using a convolutional neural network (CNN) and long short-term memory
(LSTM) model for the downlink of the NOMA system and compare the
results with the conventional SIC method. Results demonstrate the effectiveness
and superior detection performance of the deep learning method. |
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