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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sg-ntu-dr.10356-1736842024-02-23T15:43:33Z Design of deep learning based receiver for downlink NOMA system Chen, Shuheng Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Computer and Information Science 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. Master's degree 2024-02-23T01:23:18Z 2024-02-23T01:23:18Z 2024 Thesis-Master by Coursework Chen, S. (2024). Design of deep learning based receiver for downlink NOMA system. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173684 https://hdl.handle.net/10356/173684 en application/pdf Nanyang Technological University |
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Computer and Information Science Chen, Shuheng Design of deep learning based receiver for downlink NOMA system |
description |
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. |
author2 |
Teh Kah Chan |
author_facet |
Teh Kah Chan Chen, Shuheng |
format |
Thesis-Master by Coursework |
author |
Chen, Shuheng |
author_sort |
Chen, Shuheng |
title |
Design of deep learning based receiver for downlink NOMA system |
title_short |
Design of deep learning based receiver for downlink NOMA system |
title_full |
Design of deep learning based receiver for downlink NOMA system |
title_fullStr |
Design of deep learning based receiver for downlink NOMA system |
title_full_unstemmed |
Design of deep learning based receiver for downlink NOMA system |
title_sort |
design of deep learning based receiver for downlink noma system |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/173684 |
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1794549288960786432 |