Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system

In this dissertation, we build a deep learning (DL)-based orthogonal frequency division multiplexing (OFDM) receiver. This receiver replaces the traditional two processes, channel estimation and signal detection with one network deployment process, which can reduce the system complexity. DL method,...

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Main Author: Guo, Tianci
Other Authors: Teh Kah Chan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/163217
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1632172022-11-29T03:02:52Z Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system Guo, Tianci Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering::Electrical and electronic engineering In this dissertation, we build a deep learning (DL)-based orthogonal frequency division multiplexing (OFDM) receiver. This receiver replaces the traditional two processes, channel estimation and signal detection with one network deployment process, which can reduce the system complexity. DL method, rather than first calculate the channel state information (CSI) then recover the detected symbols, allows the received signal to be decoded. This approach can be divided into two parts, offline training and online deployment. Firstly, we train a network using data generated on multi-path Rayleigh fading channel. Then, this trained network is used in online deployment to recover the received data symbols directly. In this dissertation, two different kinds of DL network, DNN and LSTM are designed. The simulation is run on MATLAB platform and the simulation results demonstrate that both DNN and LSTM methods can achieve BER performance as good as minimum mean-square error (MMSE) method does when pilot information is sufficient. And in deficient pilot circumstance, DL method demonstrates better robustness. Further more, LSTM can learn channel statics quicker than DNN and can achieve better BER performance than DNN when SNR is low. In conclusion, combining traditional communication system with DL method has shown its advantages and is very promising. Master of Science (Communications Engineering) 2022-11-29T03:02:52Z 2022-11-29T03:02:52Z 2022 Thesis-Master by Coursework Guo, T. (2022). Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163217 https://hdl.handle.net/10356/163217 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
Guo, Tianci
Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
description In this dissertation, we build a deep learning (DL)-based orthogonal frequency division multiplexing (OFDM) receiver. This receiver replaces the traditional two processes, channel estimation and signal detection with one network deployment process, which can reduce the system complexity. DL method, rather than first calculate the channel state information (CSI) then recover the detected symbols, allows the received signal to be decoded. This approach can be divided into two parts, offline training and online deployment. Firstly, we train a network using data generated on multi-path Rayleigh fading channel. Then, this trained network is used in online deployment to recover the received data symbols directly. In this dissertation, two different kinds of DL network, DNN and LSTM are designed. The simulation is run on MATLAB platform and the simulation results demonstrate that both DNN and LSTM methods can achieve BER performance as good as minimum mean-square error (MMSE) method does when pilot information is sufficient. And in deficient pilot circumstance, DL method demonstrates better robustness. Further more, LSTM can learn channel statics quicker than DNN and can achieve better BER performance than DNN when SNR is low. In conclusion, combining traditional communication system with DL method has shown its advantages and is very promising.
author2 Teh Kah Chan
author_facet Teh Kah Chan
Guo, Tianci
format Thesis-Master by Coursework
author Guo, Tianci
author_sort Guo, Tianci
title Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
title_short Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
title_full Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
title_fullStr Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
title_full_unstemmed Deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) system
title_sort deep learning-based receiver for orthogonal frequency-division multiplexing (ofdm) system
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/163217
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