TY - JOUR
T1 - Deep Learning Based NLOS Identification with Commodity WLAN Devices
AU - Choi, Jeong Sik
AU - Lee, Woong Hee
AU - Lee, Jae Hyun
AU - Lee, Jong Ho
AU - Kim, Seong Cheol
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - channel state information
KW - indoor localization
KW - Line-of-sight identification
KW - long short-term memory
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85037573836&partnerID=8YFLogxK
U2 - 10.1109/TVT.2017.2780121
DO - 10.1109/TVT.2017.2780121
M3 - Article
AN - SCOPUS:85037573836
SN - 0018-9545
VL - 67
SP - 3295
EP - 3303
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 4
ER -