Deep Learning Based NLOS Identification with Commodity WLAN Devices

TL;DR

This paper presents a deep learning approach using RNNs to identify LOS and NLOS conditions from CSI data in commodity WLAN devices, enhancing accuracy with limited sampling rates.

Contribution

It introduces a novel RNN-based method for NLOS identification that outperforms existing handcrafted feature schemes using CSI time series data.

Findings

High accuracy in NLOS detection with short CSI sequences

Effective learning of non-linear relationships in CSI data

Improved performance over traditional handcrafted feature methods

Abstract

Identifying line-of-sight (LOS) and non-LOS (NLOS) channel conditions can improve the performance of many wireless applications, such as signal strength-based localization algorithms. For this purpose, channel state information (CSI) obtained by commodity IEEE 802.11n devices can be used, because it contains information about channel impulse response (CIR). However, because of the limited sampling rate of the devices, a high-resolution CIR is not available, and it is difficult to detect the existence of an LOS path from a single CSI measurement, but it can be inferred from the variation pattern of CSI over time. To this end, we propose a recurrent neural network (RNN) model, which takes a series of CSI to identify the corresponding channel condition. We collect numerous measurement data under an indoor office environment, train the proposed RNN model, and compare the performance with those of existing schemes that use handcrafted features. The proposed method efficiently learns a non-linear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.