Cargando…
Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks
The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adap...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038564/ https://www.ncbi.nlm.nih.gov/pubmed/33916361 http://dx.doi.org/10.3390/s21072538 |
_version_ | 1783677404645949440 |
---|---|
author | Zhang, Shuang Liu, Feng Huang, Yuang Meng, Xuedong |
author_facet | Zhang, Shuang Liu, Feng Huang, Yuang Meng, Xuedong |
author_sort | Zhang, Shuang |
collection | PubMed |
description | The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels. |
format | Online Article Text |
id | pubmed-8038564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80385642021-04-12 Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks Zhang, Shuang Liu, Feng Huang, Yuang Meng, Xuedong Sensors (Basel) Article The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels. MDPI 2021-04-05 /pmc/articles/PMC8038564/ /pubmed/33916361 http://dx.doi.org/10.3390/s21072538 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Shuang Liu, Feng Huang, Yuang Meng, Xuedong Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title | Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title_full | Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title_fullStr | Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title_full_unstemmed | Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title_short | Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks |
title_sort | adaptive detection of direct-sequence spread-spectrum signals based on knowledge-enhanced compressive measurements and artificial neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038564/ https://www.ncbi.nlm.nih.gov/pubmed/33916361 http://dx.doi.org/10.3390/s21072538 |
work_keys_str_mv | AT zhangshuang adaptivedetectionofdirectsequencespreadspectrumsignalsbasedonknowledgeenhancedcompressivemeasurementsandartificialneuralnetworks AT liufeng adaptivedetectionofdirectsequencespreadspectrumsignalsbasedonknowledgeenhancedcompressivemeasurementsandartificialneuralnetworks AT huangyuang adaptivedetectionofdirectsequencespreadspectrumsignalsbasedonknowledgeenhancedcompressivemeasurementsandartificialneuralnetworks AT mengxuedong adaptivedetectionofdirectsequencespreadspectrumsignalsbasedonknowledgeenhancedcompressivemeasurementsandartificialneuralnetworks |