NLOS Identification in WLANs Using Deep LSTM with CNN Features.

Yong-Hwa Kim,Minh-Tuan Nguyen,Viet-Hung Nguyen,Jeongsik Choi

doi:10.3390/s18114057

Yong-Hwa Kim, Minh-Tuan Nguyen + Show 2 more

Open Access

https://doi.org/10.3390/s18114057

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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.

Highlights

Location-based services such as real-time tracking, security alerts, informational services, and entertainment applications are becoming important in wireless communication infrastructures
We propose a new LOS and NLOS classification method for wireless local area network (WLAN) based on Received signal strength indicator (RSSI) and channel state information (CSI)
In the proposed convolutional neural network LSTM (CNNLSTM) model, the CNN captured the feature from frequency-domain characteristics of CSIs and long-short term memory (LSTM) extracted the temporal feature from RSSI

Summary

Introduction

Location-based services such as real-time tracking, security alerts, informational services, and entertainment applications are becoming important in wireless communication infrastructures. To improve WLAN localization performance, several studies have, over time, investigated how to distinguish between LOS and NLOS using handcrafted features by a series of CSI [11,12,13,14,15]. A recurrent neural network (RNN) model with long-short term memory (LSTM) one of a deep models using RSSI and CSI has been proposed to improve the classification performance of LOS and NLOS classification [16]. The RNN model with LSTMs in [16] only focuses on the temporal structure of CSI data and does not contain the frequency characteristics of CSIs. In this paper, we propose a new LOS and NLOS classification method for WLANs based on RSSI and CSI. Compared to the LSTM model [16], the proposed CNNLSTM exploits the non-temporal structure from the input by using the CNN before.

Related Works

System Model

Experimental Data

Proposed CNNLSTM Model

CNN Part

RNN Part

Performance Evaluation

Findings

Conclusions