Robust machine-learning based algorithm for detection of signal under noise floor

Spectrum sensing plays an important role in cognitive radio. In wireless communication systems, due to severe transmission environment of interference, the received signals may be very weak as compared to the background noise. In this project, first, the existing schemes of detection of signals belo...

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Main Author: Wang, Wenbo
Other Authors: Teh Kah Chan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/158127
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1581272023-07-07T19:32:48Z Robust machine-learning based algorithm for detection of signal under noise floor Wang, Wenbo Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering::Electrical and electronic engineering::Wireless communication systems Spectrum sensing plays an important role in cognitive radio. In wireless communication systems, due to severe transmission environment of interference, the received signals may be very weak as compared to the background noise. In this project, first, the existing schemes of detection of signals below the noise floor are studied. Following that, a machine-learning based algorithm using one-dimensional convolution neural network is developed and applied to detect the presence of signals below the noise floor. By testing on various cases and comparing with existing methods, it shows better performance and higher accuracy. It also brings out potential study subjects concerning real life application and signal enhancement. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-30T11:50:16Z 2022-05-30T11:50:16Z 2022 Final Year Project (FYP) Wang, W. (2022). Robust machine-learning based algorithm for detection of signal under noise floor. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158127 https://hdl.handle.net/10356/158127 en W3360-212 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
Wang, Wenbo
Robust machine-learning based algorithm for detection of signal under noise floor
description Spectrum sensing plays an important role in cognitive radio. In wireless communication systems, due to severe transmission environment of interference, the received signals may be very weak as compared to the background noise. In this project, first, the existing schemes of detection of signals below the noise floor are studied. Following that, a machine-learning based algorithm using one-dimensional convolution neural network is developed and applied to detect the presence of signals below the noise floor. By testing on various cases and comparing with existing methods, it shows better performance and higher accuracy. It also brings out potential study subjects concerning real life application and signal enhancement.
author2 Teh Kah Chan
author_facet Teh Kah Chan
Wang, Wenbo
format Final Year Project
author Wang, Wenbo
author_sort Wang, Wenbo
title Robust machine-learning based algorithm for detection of signal under noise floor
title_short Robust machine-learning based algorithm for detection of signal under noise floor
title_full Robust machine-learning based algorithm for detection of signal under noise floor
title_fullStr Robust machine-learning based algorithm for detection of signal under noise floor
title_full_unstemmed Robust machine-learning based algorithm for detection of signal under noise floor
title_sort robust machine-learning based algorithm for detection of signal under noise floor
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158127
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