Wireless communication receiver design based on machine learning

In digital wireless communications systems, equalizers are needed to reduce the effect of inter-symbol interference (ISI) due to multipath fading channel. Recent works have demonstrated that machine learning approaches are suitable to solve different tasks in mobile communication systems. In this fi...

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Main Author: Too, Marcus Xuanli
Other Authors: Guan Yong Liang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157703
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1577032023-07-07T19:01:49Z Wireless communication receiver design based on machine learning Too, Marcus Xuanli Guan Yong Liang School of Electrical and Electronic Engineering EYLGuan@ntu.edu.sg Engineering::Electrical and electronic engineering::Wireless communication systems In digital wireless communications systems, equalizers are needed to reduce the effect of inter-symbol interference (ISI) due to multipath fading channel. Recent works have demonstrated that machine learning approaches are suitable to solve different tasks in mobile communication systems. In this final year project, we study how to apply machine learning to wireless communication receiver design, and the task we focus on is equalization. Two types of artificial neural network (ANN), i.e., Long Short-Term Memory (LSTM) and Gated Recurring Units (GRU), are considered in our study. Extensive analysis has been done to find the optimal structure of the two ANNs, as well as the optimal setting of the training parameters. The performance of the two ANNs has been tested under different scenarios, which includes different modulation types (4QAM, 16QAM and 64QAM), channel types (time-invariant and time-varying), and waveforms (single-carrier and multicarrier, i.e., OFDM). In addition, the performance of the ANNs are compared with some well-known conventional equalization techniques, i.e., decision feedback equalization (DFE) in single-carrier, and least square (LS) or minimum mean square error (MMSE) channel estimation plus single-step equalization in OFDM. Bachelor of Engineering (Information Engineering and Media) 2022-05-19T05:27:55Z 2022-05-19T05:27:55Z 2022 Final Year Project (FYP) Too, M. X. (2022). Wireless communication receiver design based on machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157703 https://hdl.handle.net/10356/157703 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::Wireless communication systems
spellingShingle Engineering::Electrical and electronic engineering::Wireless communication systems
Too, Marcus Xuanli
Wireless communication receiver design based on machine learning
description In digital wireless communications systems, equalizers are needed to reduce the effect of inter-symbol interference (ISI) due to multipath fading channel. Recent works have demonstrated that machine learning approaches are suitable to solve different tasks in mobile communication systems. In this final year project, we study how to apply machine learning to wireless communication receiver design, and the task we focus on is equalization. Two types of artificial neural network (ANN), i.e., Long Short-Term Memory (LSTM) and Gated Recurring Units (GRU), are considered in our study. Extensive analysis has been done to find the optimal structure of the two ANNs, as well as the optimal setting of the training parameters. The performance of the two ANNs has been tested under different scenarios, which includes different modulation types (4QAM, 16QAM and 64QAM), channel types (time-invariant and time-varying), and waveforms (single-carrier and multicarrier, i.e., OFDM). In addition, the performance of the ANNs are compared with some well-known conventional equalization techniques, i.e., decision feedback equalization (DFE) in single-carrier, and least square (LS) or minimum mean square error (MMSE) channel estimation plus single-step equalization in OFDM.
author2 Guan Yong Liang
author_facet Guan Yong Liang
Too, Marcus Xuanli
format Final Year Project
author Too, Marcus Xuanli
author_sort Too, Marcus Xuanli
title Wireless communication receiver design based on machine learning
title_short Wireless communication receiver design based on machine learning
title_full Wireless communication receiver design based on machine learning
title_fullStr Wireless communication receiver design based on machine learning
title_full_unstemmed Wireless communication receiver design based on machine learning
title_sort wireless communication receiver design based on machine learning
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157703
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