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Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks
This paper investigates the security–reliability of simultaneous wireless information and power transfer (SWIPT)-assisted amplify-and-forward (AF) full-duplex (FD) relay networks. In practice, an AF-FD relay harvests energy from the source (S) using the power-splitting (PS) protocol. We propose an a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490820/ https://www.ncbi.nlm.nih.gov/pubmed/37688073 http://dx.doi.org/10.3390/s23177618 |
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author | Nguyen, Tan N. Minh, Bui Vu Tran, Dinh-Hieu Le, Thanh-Lanh Le, Anh-Tu Nguyen, Quang-Sang Lee, Byung Moo |
author_facet | Nguyen, Tan N. Minh, Bui Vu Tran, Dinh-Hieu Le, Thanh-Lanh Le, Anh-Tu Nguyen, Quang-Sang Lee, Byung Moo |
author_sort | Nguyen, Tan N. |
collection | PubMed |
description | This paper investigates the security–reliability of simultaneous wireless information and power transfer (SWIPT)-assisted amplify-and-forward (AF) full-duplex (FD) relay networks. In practice, an AF-FD relay harvests energy from the source (S) using the power-splitting (PS) protocol. We propose an analysis of the related reliability and security by deriving closed-form formulas for outage probability (OP) and intercept probability (IP). The next contribution of this research is an asymptotic analysis of OP and IP, which was generated to obtain more insight into important system parameters. We validate the analytical formulas and analyze the impact on the key system parameters using Monte Carlo simulations. Finally, we propose a deep learning network (DNN) with minimal computation complexity and great accuracy for OP and IP predictions. The effects of the system’s primary parameters on OP and IP are examined and described, along with the numerical data. |
format | Online Article Text |
id | pubmed-10490820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104908202023-09-09 Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks Nguyen, Tan N. Minh, Bui Vu Tran, Dinh-Hieu Le, Thanh-Lanh Le, Anh-Tu Nguyen, Quang-Sang Lee, Byung Moo Sensors (Basel) Article This paper investigates the security–reliability of simultaneous wireless information and power transfer (SWIPT)-assisted amplify-and-forward (AF) full-duplex (FD) relay networks. In practice, an AF-FD relay harvests energy from the source (S) using the power-splitting (PS) protocol. We propose an analysis of the related reliability and security by deriving closed-form formulas for outage probability (OP) and intercept probability (IP). The next contribution of this research is an asymptotic analysis of OP and IP, which was generated to obtain more insight into important system parameters. We validate the analytical formulas and analyze the impact on the key system parameters using Monte Carlo simulations. Finally, we propose a deep learning network (DNN) with minimal computation complexity and great accuracy for OP and IP predictions. The effects of the system’s primary parameters on OP and IP are examined and described, along with the numerical data. MDPI 2023-09-02 /pmc/articles/PMC10490820/ /pubmed/37688073 http://dx.doi.org/10.3390/s23177618 Text en © 2023 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 Nguyen, Tan N. Minh, Bui Vu Tran, Dinh-Hieu Le, Thanh-Lanh Le, Anh-Tu Nguyen, Quang-Sang Lee, Byung Moo Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title | Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title_full | Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title_fullStr | Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title_full_unstemmed | Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title_short | Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks |
title_sort | security–reliability analysis of af full-duplex relay networks using self-energy recycling and deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490820/ https://www.ncbi.nlm.nih.gov/pubmed/37688073 http://dx.doi.org/10.3390/s23177618 |
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