Channel State Information Amplitude Research Articles

A Handwriting Recognition System With WiFi

Handwriting recognition systems are a convenient and alternative way of writing in the air with fingers rather than typing on keyboards. However, existing recognition systems are limited by their low accuracy and the requirement to wear dedicated devices. To address these issues, we propose WiWrite, an accurate contactless handwriting recognition system that allows users to write in the air without wearing any wearable devices. Specifically, we employ a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSI division scheme</i> to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces noise in CSI amplitude. To automatically retain low noise data for identification in the LOS scenario, we propose a self-paced dense convolutional network (SPDCN), which is a self-paced loss function based on a modified convolutional neural network coupled with a dense convolutional network. Furthermore, to achieve accurate handwriting recognition in the NLOS scenario, we combine ADOA and PCA algorithms to remove location-induced interference and extract action features. Comprehensive experiments show the merits of WiWrite, revealing that the recognition accuracy for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve accurate recognition regardless of environment and target diversity in LOS and NLOS scenarios.

A channel state information and geomagnetic fused fingerprint localisation algorithm based on multi‐input convolutional neural network

AbstractNumerous applications require indoor localisation, and one of the current research areas is how to leverage low‐cost ubiquitous signals for indoor localisation. This research designs a multi‐input convolutional neural network (Multi‐CNN) localisation approach to combine natural geomagnetic signals and universal 5G communication signals. To create the location fingerprint data, the geomagnetic three‐component data and channel state information (CSI) must first undergo independent preprocessing. Subsequently, the rebuilt CSI amplitude and geomagnetic intensity are employed for separate offline training to efficiently extract the corresponding data features. Lastly, Multi‐CNN is used to estimate the user's location online. The localisation outcomes for the conference room and hall demonstrate that the Multi‐CNN algorithm can achieve average localisation accuracies of 1.41 and 2.66 m, respectively. These are higher than the single‐input CNN algorithms by 21% and 15%, and higher than backpropagation network (BPNN) algorithm by 24% and 17%, and higher than the weighted K‐nearest neighbour algorithm by 34% and 28%. The Multi‐CNN‐based localisation approach successfully fuses the diverse data, potentially satisfying most indoor localisation applications.

Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization

In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods.

Texture features-based lightweight passive multi-state crowd counting algorithm

Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.

CeHAR: CSI-Based Channel-Exchanging Human Activity Recognition

Despite the intense effort from the research community, state-of-the-art WiFi-based human activity recognition performance remains unsatisfactory. Current approaches usually use individual characteristics of CSI, i.e., amplitude or phase measurements, to model the relationship between the changes of channel state information (CSI) and human activities, which lead to their failure to achieve satisfactory accuracy due to information loss. To deal with this issue, this paper proposes CeHAR, a CSI-based Human Activity Recognition using a channel-exchanging fusion network to deep fuse the CSI amplitude and phase features to obtain the informative features for human activity recognition. The proposed CeHAR is a parameter-free dual-characteristic fusion framework that dynamically exchanges channels between sub-networks of two kinds of characteristics to comprehensively learn informative features from both. Specifically, the proposed approach employs two sub-network using convolutional neural networks to learn features from each characteristic of CSI. The magnitude of Batch-Normalization (BN) scaling factor is used to determine the channel importance of each characteristic, and then guides the exchange process. The proposed CeHAR also shares convolutional filters, but keeps private BNs layers in different characteristics, which, as an added benefit, allows our characteristic fusion network to be nearly as compact as a single-characteristic network. Extensive real-world experiments have been conducted to evaluate the performance of our proposed CeHAR, and the experimental results illustrate that our proposed approach outperforms baselines.

PEACH: Proactive and Environment-Aware Channel State Information Prediction with Depth Images

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation, up to 6 dB, when compared to traditional approaches.

Device-Free Indoor Localization Based on Multidimensional CSI Features Classification

With the rapid development of location-based service applications in recent years, indoor localization based on Wi-Fi channel state information (CSI) has attracted extensive attention. Although spatial variations are explicitly reflected in CSI measurements, representation differences caused by small contextual changes tend to be submerged by overall multipath fluctuations, especially in device-free localization. Most existing model-based solutions either underutilize the spatial and time-frequency information carried by CSI, or the employed models fail to capture small representational changes in CSI well, which makes them struggle to get satisfactory expectations. To address these problems, this paper proposes a progressive device-free localization scheme using multidimensional CSI features classification, named ProLoc. Different from previous works that directly extract the features of raw measurements, ProLoc constructs a model input containing multi-category information of CSI to perform better location inference. First, a time-frequency correlation matrix with low rank is created based on the CSI amplitude, and a 3-order CSI tensor is planned combined with spatial diversity. Second, using a novel low-rank matrix factorization algorithm and the time-frequency gradients of CSI, a 4-order tensor containing multidimensional information is generated as input data. Finally, employing 3D convolutional neural networks and gated recurrent unit, we build a progressive dual-model system to realize the mapping from CSI to target locations in various spatial contexts. Extensive self-evaluations and comparisons with several state-of-the-art methods highlight the superiority of the proposed ProLoc.

Deep-fading hole avoidance for secure region detection using channel state information

One of the critical challenges in many wireless systems is the deep fading hole problem where signals interfere destructively to create an abrupt change in signal amplitude due to multipath fading. In this work, we tackle this challenge in the context of secure region detection (SRD) problem. Specifically, we propose SHARD, a novel hole avoidance technique that analyzes channel state information (CSI) to significantly improve the accuracy of CSI-based SRD. SHARD identifies potential fading holes in CSI amplitude, and removes an unreliable portion of data while utilizing the remaining unaffected part. To compensate for the loss of information, we define phase-distance for reliable use of time-varying CSI phase, and neighboring reference points are utilized for accurate matching. Our real-world experiments show that SHARD can achieve a near-perfect 99.96% true-negative ratio (successfully rejecting devices not in the secure region) and an excellent true-positive of 98.01% for practical usage, significantly better than state-of-the-art prior work. We believe our ideas can be generalized to many RF-based localization systems to mitigate the deep fading hole problem and improve their accuracy.

Hybrid Subcarrier Selection Method for Vital Sign Monitoring With Long-Term and Short-Term Data Considerations

Tracking vital signs during sleeping is important because the breathing and heart rates can be used to detect breathing diseases and heart disorders. However, existing work for breathing and heart rate estimations only considered long-term data, while ignoring the fact that people could turn over frequently in practice, which results in different data lengths of breathing and heart rate signals. In addition, most systems based on channel state information (CSI) only adopted either amplitude or phase information to select subcarrier, which could degrade the performance of breathing and heart rate estimations. In this article, in order to improve the performance, we first propose a new subcarrier selection method by jointly considering the CSI amplitude and phase information. Then, based on the proposed subcarrier selection scheme, a new breathing and heart rate estimations scheme is proposed under the consideration of different data lengths, where the peak detection and the FFT-based methods are, respectively, used for long-term and short-term breathing signals, while the FFT-based method is used to estimate the heart rate for both long-term and short-term signals. Experiment results demonstrate that our work can achieve accuracies of 96.1% and 94.3% for breathing and heart rate estimations.

Fusion Learning for 1-Bit CS-Based Superimposed CSI Feedback With Bi-Directional Channel Reciprocity

Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay. To overcome these drawbacks, this letter proposes a fusion learning scheme by exploiting the bi-directional channel reciprocity. Specifically, a simplified version of the conventional downlink CSI reconstruction is utilized to extract the initial feature of downlink CSI, and a single hidden layer-based amplitude-learning network (AMPL-NET) is designed to learn the auxiliary feature of the downlink CSI amplitude. Then, based on the extracted and learned amplitude features, a simple but effective amplitude-fusion network (AMPF-NET) is developed to perform the amplitude fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit CS-based superimposed CSI feedback while reducing the processing delay. Simulation results show the effectiveness of the proposed feedback scheme and the robustness against parameter variations.

Utilizing deep learning models in CSI-based human activity recognition.

In recent years, channel state information (CSI) in WiFi 802.11n has been increasingly used to collect data pertaining to human activity. Such raw data are then used to enhance human activity recognition. Activities such as lying down, falling, walking, running, sitting down, and standing up can now be detected with the use of information collected through CSI. Human activity recognition has a multitude of applications, such as home monitoring of patients. Four deep learning models are presented in this paper, namely: a convolution neural network (CNN) with a Gated Recurrent Unit (GRU); a CNN with a GRU and attention; a CNN with a GRU and a second CNN, and a CNN with Long Short-Term Memory (LSTM) and a second CNN. Those models were trained to perform Human Activity Recognition (HAR) using CSI amplitude data collected by a CSI tool. Experiments conducted to test the efficacy of these models showed superior results compared with other recent approaches. This enhanced performance of our models may be attributable the ability of our models to make full use of available data and to extract all data features, including high dimensionality and time sequence. The highest average recognition accuracy reached by the proposed models was achieved by the CNN-GRU, and the CNN-GRU with attention models, standing at 99.31% and 99.16%, respectively. In addition, the performance of the models was evaluated for unseen CSI data by training our models using a random split-of-dataset method (70% training and 30% testing). Our models achieved impressive results with accuracies reaching 100% for nearly all activities. For the lying down activity, accuracy obtained from the CNN-GRU model stood at 99.46%; slightly higher than the 99.05% achieved by the CNN-GRU with attention model. This confirmed the robustness of our models against environmental changes.

Monitoring Respiratory Motion With Wi-Fi CSI: Characterizing Performance and the BreatheSmart Algorithm.

Respiratory motion (i.e., motion pattern and rate) can provide valuable information for many medical situations. This information may help in the diagnosis of different health disorders and diseases. Wi-Fi-based respiratory monitoring schemes utilizing commercial off-the-shelf (COTS) devices can provide contactless, low-cost, simple, and scalable respiratory monitoring without requiring specialized hardware. Despite intense research efforts, an in-depth investigation on how to evaluate this type of technology is missing. We demonstrated and assessed the feasibility of monitoring and extracting human respiratory motion from Wi-Fi channel state information (CSI) data. This demonstration involves implementing an end-to-end system for a COTS-based hardware platform, control software, data acquisition, and a proposed processing algorithm. The processing algorithm is a novel deep-learning-based approach that exploits small changes in both CSI amplitude and phase information to learn high-level abstractions of breathing-induced chest movements and to reveal the unique characteristics of their difference. We also conducted extensive laboratory experiments demonstrating an assessment technique that can be replicated when quantifying the performance of similar systems. The results indicate that the proposed scheme can classify respiratory patterns and rates with an accuracy of 99.54% and 98.69%, respectively, in moderately degraded RF channels. Comprehensive data acquisition revealed the capability of the proposed system in detecting and classifying respiratory motions. Understanding the feasible limits and potential failure factors of Wi-Fi CSI-based respiratory monitoring scheme - and how to evaluate them - is an essential step toward the practical deployment of this technology. This study discusses ideas for further expansion of this technology.

A Semi-Supervised Ladder Network-Based Indoor Localization Using Channel State Information

We propose a ladder network-based fingerprinting (LadderNetFi) method, which combines unsupervised learning with supervised learning in the neural network model for channel state information (CSI)-based indoor localization. The unsupervised part of LadderNetFi, which serves as a denoising autoencoder with skip connections from its encoder to the decoder, focuses on detailed features related to supervised learning. By dealing with the measurement uncertainty in the architecture design, a better generalization of fingerprints from the pre-processing of CSI amplitude leads to performance improvement compared to other deep learning-based methods. As semi-supervised learning, we verify that LadderNet trained using only a small portion of labeled measurements with the unlabeled measurements, e.g., 2,000 versus 20,000, and only a part of reference points, e.g., 19 versus 31, provides better performance than state-of-art methods using all labeled measurements. By reducing human labor from labeling, the deployment for LadderNetFi is scalable.

Area human sensing via ambient Wi‐Fi signals

Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi-Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi-dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two-level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one-person and two-person is 99.6% and 94.6% respectively.

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

WiPhone

Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.

CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction

In order to improve the performance of Wi-Fi fingerprint indoor positioning technology, a method based on Convolutional Neural Networks (CNN) for Channel State Information (CSI) fingerprint indoor positioning is first proposed. This method combines the CSI amplitude difference and phase difference information to train the CNN model in the offline stage. Positioning experiments are carried out in two different indoor positioning scenarios in the gallery and the laboratory, and the average positioning errors of 25 cm and 48 cm are obtained respectively; then, on this basis, the focus is on improving the timeliness of CNN-based CSI indoor positioning, and the volume is introduced. The Convolutional AutoEncoder (CAE) realizes the dimensionality reduction processing of CSI. Under the premise of ensuring the accuracy of the original positioning method, the positioning time is increased by 40% and the memory consumption is reduced to 1/15 of the original algorithm. The experimental results verify the effectiveness of the proposed algorithm.

MultiSense

In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. Existing approaches mainly rely on spectral analysis of the CSI amplitude to obtain respiration rate information, leading to multiple limitations: (1) spectral analysis works when multiple persons exhibit dramatically different respiration rates, however, it fails to resolve similar rates; (2) spectral analysis can only obtain the average respiration rate over a period of time, and it is unable to capture the detailed rate change over time; (3) they fail to sense the respiration when a target is located at the "blind spots" even the target is close to the sensing devices. To overcome these limitations, we propose MultiSense, the first WiFi-based system that can robustly and continuously sense the detailed respiration patterns of multiple persons even they have very similar respiration rates and are physically closely located. The key insight of our solution is that the commodity WiFi hardware nowadays is usually equipped with multiple antennas. Thus, each individual antenna can receive a different mix copy of signals reflected from multiple persons. We successfully prove that the reflected signals are linearly mixed at each antenna and propose to model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to separate the mixed signal and obtain the reparation information of each person. Extensive experiments show that with only one pair of transceivers, each equipped with three antennas, MultiSense is able to accurately monitor respiration even in the presence of four persons, with the mean absolute respiration rate error of 0.73 bpm (breaths per minute).

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

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.

Secure Single-Input-Multiple-Output Media-Based Modulation

The media-based modulation (MBM) system, which varies its channel state by controlling the on/off status of the radio frequency (RF) mirrors, and embeds additional information bits into the mirror activation pattern, is capable of achieving significant throughput and desirable bit-error-rate (BER) performance. Hence, it has attracted increasing attention from researchers of wireless communication community. In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , the information secrecy issue for MBM is investigated, and a secure single-input-multiple-output MBM (SIMO-MBM) system is proposed utilizing physical-layer security techniques. By considering the time-division duplexing (TDD) mode with channel reciprocity property, the legitimate channel state information (CSI) is applied to secure the data transmission of SIMO-MBM. More specifically, on one hand, adaptive phase rotation is imposed on the transmitted signals, where the rotation angle is determined by the phase of legitimate CSI; on the other hand, the mapping rule between the mirror activation pattern and binary bits is changed according to the amplitude of legitimate CSI. The BER performance of the eavesdropper is investigated via theoretical derivations, and the system secrecy mutual information is also derived, based on which the optimal design of the adaptive phase rotation strategy is provided. Theoretical and simulative results demonstrate that the proposed secure SIMO-MBM system is capable of enhancing the data confidentiality whilst keeping reliable data transmission.