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Channel Prediction Based on BP Neural Network for Backscatter Communication Networks
Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing metho...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982895/ https://www.ncbi.nlm.nih.gov/pubmed/31948085 http://dx.doi.org/10.3390/s20010300 |
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author | Zhao, Jumin Tian, Hao Li, Deng-ao |
author_facet | Zhao, Jumin Tian, Hao Li, Deng-ao |
author_sort | Zhao, Jumin |
collection | PubMed |
description | Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved. |
format | Online Article Text |
id | pubmed-6982895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69828952020-02-06 Channel Prediction Based on BP Neural Network for Backscatter Communication Networks Zhao, Jumin Tian, Hao Li, Deng-ao Sensors (Basel) Article Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved. MDPI 2020-01-05 /pmc/articles/PMC6982895/ /pubmed/31948085 http://dx.doi.org/10.3390/s20010300 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Jumin Tian, Hao Li, Deng-ao Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title | Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title_full | Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title_fullStr | Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title_full_unstemmed | Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title_short | Channel Prediction Based on BP Neural Network for Backscatter Communication Networks |
title_sort | channel prediction based on bp neural network for backscatter communication networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982895/ https://www.ncbi.nlm.nih.gov/pubmed/31948085 http://dx.doi.org/10.3390/s20010300 |
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