Xuyu Wang

CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach

With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant interest due to its high accuracy. In this paper, we present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error, compared with three existing methods in two representative indoor environments.

DeepFi: Deep learning for indoor fingerprinting using channel state information

With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.

CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card by accessing the modified device driver. Then a linear transformation is applied to extract the calibrated phase information, which we prove to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ the weights of the deep network to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a subnetwork between two consecutive layers forms a restricted Boltzmann machine. In the online stage, we use a probabilistic method based on the radial basis function for online location estimation. The proposed PhaseFi scheme is implemented and validated with extensive experiments in two representation indoor environments. It is shown to outperform three benchmark schemes based on CSI or received signal strength in both scenarios.

PhaseFi: Phase Fingerprinting for Indoor Localization with a Deep Learning Approach

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card (NIC) by accessing the modified driver. Then a linear transform is used to extract the calibrated phase information, which is proven to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ weights to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a sub-network between two continuous layers forms a Restricted Boltzmann Machine (RBM). In the online stage, we use a probabilistic method based on the radial basis function (RBF) for online location estimation. The proposed PhaseFi scheme is implemented and validated with intensive experiments in two representation indoor environments. It outperforms other three benchmark schemes based on CSI or RSS in both scenarios.

CA2T: Cooperative Antenna Arrays Technique for Pinpoint Indoor Localization

Abstract Location-based service has a great potential in the indoor environment, making it important to develop accurate indoor localization techniques. In this paper, we consider AOA based indoor localization, which can generally achieve higher accuracy of localization than other approaches. We propose to use cooperative APs with antenna arrays for accurate indoor localization. With the proposed Cooperative Antenna Arrays Technique (CA 2 T), we first estimate the arriving angles for all the multipath components using the MUSIC algorithm, and then exploit the geometric relationship among the angles to identify the LOS angles. The user location can be computed with the LOS angles and the accurate, known distance between the two APs. The proposed scheme is validated with simulations and is shown to outperform an existing scheme with considerable gains.

TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi

Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.

A SURVEY OF LTE WI-FI COEXISTENCE IN UNLICENSED BANDS

With the rapid growth of mobile data, many LTE operators are interested in leveraging unlicensed bands to enhance data rates and user experience. Th is paper investigates the problem of the coexistence of LTE and Wi-Fi in 5 GHz unlicensed bands. We fi rst introduce the current rules for the 5 GHz unlicensed bands and the carrier aggregation technique. We then discuss four deployment scenarios and two LTE-unlicensed (LTE-U) coexistence scenarios. Further, we provide a feature comparison between LTE and Wi-Fi in the PHY/MAC layers, and review the coexistence methods for LTE-U and Wi-Fi without or with the Listen- Before-Talk (LBT) mechanism. Th is paper is concluded by an examination of Wi-Fi link aggregation and in-device coexistence issues.

PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices

Vital signs, such as respiration and heartbeat, are useful to health monitoring since such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this paper, we present PhaseBeat to exploit channel state information (CSI) phase difference data to monitor breathing and heartbeat with commodity WiFi devices. We provide a rigorous analysis of the CSI phase difference data with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices, and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.

Mobility improves LMI-based cooperative indoor localization

With the proliferation of mobile devices such as smartphones, an interesting problem is how to make use them to improve the accuracy of localization in indoor environments. In this paper, we develop a novel cooperative localization scheme exploiting mobility in the indoor environment. The problem is formulated as a semidefinite program (SDP) using Linear Matrix Inequality (LMI). With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. Simulation results confirm that both the mean error and variance can be effectively reduced by exploiting IMU data and Kalman filter.

BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi

In this paper, we study fingerprinting-based indoor localization in commodity 5-GHz WiFi networks. We first theoretically and experimentally validate three hypotheses on the channel state information (CSI) data of 5-GHz OFDM channels. We then propose a system termed BiLoc, which uses bi-modality deep learning for localization in the indoor environment using off-the-shelf WiFi devices. We develop a deep learning-based algorithm to exploit bi-modal data, i.e., estimated angle of arrivings and average amplitudes (which are calibrated CSI data using several proposed techniques), for both the off-line and online stages of indoor fingerprinting. The proposed BiLoc system is implemented using commodity WiFi devices. Its superior performance is validated with extensive experiments under three typical indoor environments and through comparison with three benchmark schemes.