Transfer learning for 5G communication scenarios under different mobile speed

The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change. Compared with traditiona...

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Main Author: Liu, Haozhong
Other Authors: Teh Kah Chan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/161193
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1611932022-08-19T02:52:00Z Transfer learning for 5G communication scenarios under different mobile speed Liu, Haozhong Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering::Electrical and electronic engineering The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change. Compared with traditional machine learning, domain adaptation, a case in transfer learning, can be a more appropriate approach to reduce the influence caused by the Doppler effect and multi-path propagation to increase the reliability of communication. In this case, although the source data and target data might not be independent and identically distributed because of the changes in communication scenarios, a model could be implemented to recover the received signals with a low symbol-error rate (SER), and the training time and the computation complexity can be reduced to satisfy the requirement of low processing duration compared with learning from scratch. Master of Science (Communications Engineering) 2022-08-19T02:52:00Z 2022-08-19T02:52:00Z 2022 Thesis-Master by Coursework Liu, H. (2022). Transfer learning for 5G communication scenarios under different mobile speed. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161193 https://hdl.handle.net/10356/161193 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::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Liu, Haozhong
Transfer learning for 5G communication scenarios under different mobile speed
description The cellular network has become a cutting-edge topic due to the rapid advance of intelligent applications and the increasing user demand. With the help of machine learning, the result of message recovery could be acceptable when the wireless channel does not widely change. Compared with traditional machine learning, domain adaptation, a case in transfer learning, can be a more appropriate approach to reduce the influence caused by the Doppler effect and multi-path propagation to increase the reliability of communication. In this case, although the source data and target data might not be independent and identically distributed because of the changes in communication scenarios, a model could be implemented to recover the received signals with a low symbol-error rate (SER), and the training time and the computation complexity can be reduced to satisfy the requirement of low processing duration compared with learning from scratch.
author2 Teh Kah Chan
author_facet Teh Kah Chan
Liu, Haozhong
format Thesis-Master by Coursework
author Liu, Haozhong
author_sort Liu, Haozhong
title Transfer learning for 5G communication scenarios under different mobile speed
title_short Transfer learning for 5G communication scenarios under different mobile speed
title_full Transfer learning for 5G communication scenarios under different mobile speed
title_fullStr Transfer learning for 5G communication scenarios under different mobile speed
title_full_unstemmed Transfer learning for 5G communication scenarios under different mobile speed
title_sort transfer learning for 5g communication scenarios under different mobile speed
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/161193
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