scholarly journals NLOS Identification in WLANs Using Deep LSTM with CNN Features

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4057 ◽  
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jeongsik Choi ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Channel State Information ◽  
Recurrent Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Channel State ◽  
State Information ◽  
Channel Conditions

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.

scholarly journals Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

2020 ◽  
Vol 12 (12) ◽  
pp. 1995
Author(s):  
David Sánchez-Rodríguez ◽  
Miguel A. Quintana-Suárez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Javier J. Sánchez-Medina
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Channel State ◽  
State Information ◽  
Single Access ◽  
5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

scholarly journals A Novel Indoor Ranging Algorithm Based on a Received Signal Strength Indicator and Channel State Information Using an Extended Kalman Filter

2020 ◽  
Vol 10 (11) ◽  
pp. 3687 ◽  
Author(s):  
Jingjing Wang ◽  
Joon Goo Park
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Channel State Information ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cumulative Distribution ◽  
Estimation Accuracy ◽  
Channel State ◽  
Received Signal Strength Indicator ◽  
State Information

With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.

scholarly journals Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3379 ◽  
Author(s):  
Jialin Liu ◽  
Lei Wang ◽  
Jian Fang ◽  
Linlin Guo ◽  
Bingxian Lu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Motion Detection ◽  
School Bullying ◽  
Human Motion ◽  
Line Of Sight ◽  
Human Motion Analysis ◽  
Channel State ◽  
Detection Scheme ◽  
State Information ◽  
Human Motion Detection

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

Non‐orthogonal multiple access without channel state information for similar channel conditions

2019 ◽  
Vol 55 (8) ◽  
pp. 493-495 ◽  
Author(s):  
Wenlong Xia ◽  
Qingdang Meng ◽  
Qingchuan Tao ◽  
Ray T. Chen
Keyword(s):  
Channel State Information ◽  
Multiple Access ◽  
Channel State ◽  
State Information ◽  
Channel Conditions
Sign in / Sign up

Export Citation Format

Share Document