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
Hauptverfasser: Xu, Chendong, Wang, Weigang, Zhang, Yunwei, Qin, Jie, Yu, Shujuan, Zhang, Yun
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container_issue 5
container_start_page 574
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creator Xu, Chendong
Wang, Weigang
Zhang, Yunwei
Qin, Jie
Yu, Shujuan
Zhang, Yun
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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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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