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Sensors 2018, 18(11), 4057; https://doi.org/10.3390/s18114057

NLOS Identification in WLANs Using Deep LSTM with CNN Features

1
Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea
2
Intel Labs, Intel Corporation, Santa Clara, CA 95054, USA
*
Author to whom correspondence should be addressed.
Received: 12 October 2018 / Revised: 3 November 2018 / Accepted: 13 November 2018 / Published: 20 November 2018
(This article belongs to the Section Sensor Networks)
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Abstract

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model. View Full-Text
Keywords: line-of-sight identification; channel state information; deep learning; convolutional neural network; long-short term memory model line-of-sight identification; channel state information; deep learning; convolutional neural network; long-short term memory model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Nguyen, V.-H.; Nguyen, M.-T.; Choi, J.; Kim, Y.-H. NLOS Identification in WLANs Using Deep LSTM with CNN Features. Sensors 2018, 18, 4057.

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