Ensemble learning of deep learning-based receiver for 5G communication system implementation

This project explores the application of ensemble learning techniques for implementing deep learning (DL) based receivers in the fifth generation (5G) communication systems. Deep learning has shown promising results in handling the complexity of 5G channels; however, the performance of individual DL...

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Main Author: Xu, Ziang
Other Authors: Teh Kah Chan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/181868
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1818682024-12-27T15:46:04Z Ensemble learning of deep learning-based receiver for 5G communication system implementation Xu, Ziang Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering Ensemble deep learning This project explores the application of ensemble learning techniques for implementing deep learning (DL) based receivers in the fifth generation (5G) communication systems. Deep learning has shown promising results in handling the complexity of 5G channels; however, the performance of individual DL models can be limited due to the inherent variability of wireless channels. Ensemble learning offers a solution by combining multiple DL models to improve overall receiver performance. This project investigates different ensemble learning approaches, such as bagging, boosting, and stacking, and explores their effectiveness in enhancing the accuracy and robustness of DL-based receivers. Experimental evaluations will be conducted using realistic 5G channel models and performance metrics, comparing the ensemble learning approach with individual models. The findings of this research contribute to the practical implementation of DL-based receivers in 5G systems, providing insights into the benefits of ensemble learning for improved system performance and reliability. Master's degree 2024-12-27T12:11:45Z 2024-12-27T12:11:45Z 2024 Thesis-Master by Coursework Xu, Z. (2024). Ensemble learning of deep learning-based receiver for 5G communication system implementation. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181868 https://hdl.handle.net/10356/181868 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 Engineering
Ensemble deep learning
spellingShingle Engineering
Ensemble deep learning
Xu, Ziang
Ensemble learning of deep learning-based receiver for 5G communication system implementation
description This project explores the application of ensemble learning techniques for implementing deep learning (DL) based receivers in the fifth generation (5G) communication systems. Deep learning has shown promising results in handling the complexity of 5G channels; however, the performance of individual DL models can be limited due to the inherent variability of wireless channels. Ensemble learning offers a solution by combining multiple DL models to improve overall receiver performance. This project investigates different ensemble learning approaches, such as bagging, boosting, and stacking, and explores their effectiveness in enhancing the accuracy and robustness of DL-based receivers. Experimental evaluations will be conducted using realistic 5G channel models and performance metrics, comparing the ensemble learning approach with individual models. The findings of this research contribute to the practical implementation of DL-based receivers in 5G systems, providing insights into the benefits of ensemble learning for improved system performance and reliability.
author2 Teh Kah Chan
author_facet Teh Kah Chan
Xu, Ziang
format Thesis-Master by Coursework
author Xu, Ziang
author_sort Xu, Ziang
title Ensemble learning of deep learning-based receiver for 5G communication system implementation
title_short Ensemble learning of deep learning-based receiver for 5G communication system implementation
title_full Ensemble learning of deep learning-based receiver for 5G communication system implementation
title_fullStr Ensemble learning of deep learning-based receiver for 5G communication system implementation
title_full_unstemmed Ensemble learning of deep learning-based receiver for 5G communication system implementation
title_sort ensemble learning of deep learning-based receiver for 5g communication system implementation
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181868
_version_ 1820027770359513088