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- Title
NLOS Identification in WLANs Using Deep LSTM with CNN Features.
- Authors
Nguyen, Viet-Hung; Nguyen, Minh-Tuan; Choi, Jeongsik; Kim, Yong-Hwa
- 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.
- Subjects
WIRELESS LANs; LINE-of-sight radio links; MATHEMATICAL optimization; ARTIFICIAL neural networks; DEEP learning
- Publication
Sensors (14248220), 2018, Vol 18, Issue 11, p4057
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s18114057