An Indoor Localization System Using Residual Learning with Channel State Information
With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indo...
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Veröffentlicht in: | Entropy (Basel, Switzerland) Switzerland), 2021-05, Vol.23 (5), p.574, Article 574 |
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description | With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment. |
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However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.</description><identifier>ISSN: 1099-4300</identifier><identifier>EISSN: 1099-4300</identifier><identifier>DOI: 10.3390/e23050574</identifier><identifier>PMID: 34067056</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>Accuracy ; Algorithms ; channel state information (CSI) ; Convolution ; Deep learning ; Degradation ; denoising neural network (NN) ; Indoor environments ; indoor localization ; Localization ; Location based services ; Methods ; Network interface cards ; Neural networks ; Noise ; Noise reduction ; Physical Sciences ; Physics ; Physics, Multidisciplinary ; Propagation ; residual network (ResNet) ; Science & Technology ; Wireless networks</subject><ispartof>Entropy (Basel, Switzerland), 2021-05, Vol.23 (5), p.574, Article 574</ispartof><rights>2021 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 (https://creativecommons.org/licenses/by/4.0/). 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Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>channel state information (CSI)</subject><subject>Convolution</subject><subject>Deep learning</subject><subject>Degradation</subject><subject>denoising neural network (NN)</subject><subject>Indoor environments</subject><subject>indoor localization</subject><subject>Localization</subject><subject>Location based services</subject><subject>Methods</subject><subject>Network interface cards</subject><subject>Neural networks</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics, Multidisciplinary</subject><subject>Propagation</subject><subject>residual network (ResNet)</subject><subject>Science & Technology</subject><subject>Wireless networks</subject><issn>1099-4300</issn><issn>1099-4300</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkV1rFDEUhgdRbK1e-A8GvFFkNd8zuRHKUHVhodCP65BJzuxmmUlqkrG0v97sblmsV17lcM6Th8N5q-o9Rl8olegrEIo44g17UZ1iJOWCUYRe_lWfVG9S2iJEKMHidXVCGRIN4uK0ujn39dLbEGK9CkaP7lFnF3x9_ZAyTPVtcn5dX0FydtZjvQId_a5z7_Km7jbaexjr66wzFMsQ4rT__bZ6Negxwbun96y6_X5x0_1crC5_LLvz1cIwJvLCWMYGQXpKgZctOeeaEm35QDTW0mDUMGRbKWlDKEhuBCDKG0wYFrS33NCzannw2qC36i66SccHFbRT-0aIa6VjdmYEJQW0rCFCG25ZPwy9JYQWF--1lNJCcX07uO7mfgJrwOeox2fS5xPvNmodfqsWcyw5KYKPT4IYfs2QsppcMjCO2kOYkyKcClaOznboh3_QbZijL6faUYQyJnlbqE8HysSQUoThuAxGape7OuZe2M8H9h76MCTjwBs48gghwanEgpQK4UK3_093Lu9D7cLsM_0D7PK9Ug</recordid><startdate>20210507</startdate><enddate>20210507</enddate><creator>Xu, Chendong</creator><creator>Wang, Weigang</creator><creator>Zhang, Yunwei</creator><creator>Qin, Jie</creator><creator>Yu, Shujuan</creator><creator>Zhang, Yun</creator><general>Mdpi</general><general>MDPI AG</general><general>MDPI</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1412-3282</orcidid></search><sort><creationdate>20210507</creationdate><title>An Indoor Localization System Using Residual Learning with Channel State Information</title><author>Xu, Chendong ; 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subjects | Accuracy Algorithms channel state information (CSI) Convolution Deep learning Degradation denoising neural network (NN) Indoor environments indoor localization Localization Location based services Methods Network interface cards Neural networks Noise Noise reduction Physical Sciences Physics Physics, Multidisciplinary Propagation residual network (ResNet) Science & Technology Wireless networks |
title | An Indoor Localization System Using Residual Learning with Channel State Information |
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