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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Nanyang Technological University
2024
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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 |
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Engineering Ensemble deep learning Xu, Ziang Ensemble learning of deep learning-based receiver for 5G communication system implementation |
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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. |
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Teh Kah Chan |
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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 |
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1820027770359513088 |