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...

Full description

Saved in:
Bibliographic Details
Main Author: Chen, Shuheng
Other Authors: Teh Kah Chan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/173684
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-173684
record_format dspace
spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle 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
_version_ 1794549288960786432