Deep Learning Based NLOS Identification with Commodity WLAN Devices

Jeong Sik Choi, Woong Hee Lee, Jae Hyun Lee, Jong Ho Lee, Seong Cheol Kim

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

Identifying line-of-sight (LOS) and non-LOS 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 nonlinear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.

Original languageEnglish
Pages (from-to)3295-3303
Number of pages9
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number4
DOIs
StatePublished - Apr 2018

Keywords

  • channel state information
  • indoor localization
  • Line-of-sight identification
  • long short-term memory
  • recurrent neural network

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