NLOS Identification in WLANs Using Deep LSTM with CNN Features

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 syst...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-11, Vol.18 (11), p.4057
Main Authors: Nguyen, Viet-Hung, Nguyen, Minh-Tuan, Choi, Jeongsik, Kim, Yong-Hwa
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Algorithms
channel state information
Classification
convolutional neural network
deep learning
Indoor environments
Infrastructure
International conferences
Line of sight communication
line-of-sight identification
Local area networks
Localization
Location based services
long-short term memory model
Natural language processing
Neural networks
Orthogonal Frequency Division Multiplexing
Recurrent neural networks
Signal processing
Signal strength
Voice recognition
Wireless communications
Wireless networks
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18114057