Collect Channel State Information Research Articles

Wi-TCG: A WiFi gesture recognition method based on transfer learning and conditional generative adversarial networks

Abstract With the rapid progress of WiFi technology, WiFi-based wireless sensing technology has opened up new ways for contactless human-computer interaction. However, hand gesture recognition technology faces low-quality data sets, insufficient model robustness, poor universality when the application scenario changes, high training costs, and weak generalization ability. To this end, this study innovatively proposes the Wi-TCG method, which combines transfer learning and conditional generative adversarial network (CGAN) to optimize WiFi gesture recognition. This method uses commercial Wi-Fi devices to collect channel state information (CSI) of gesture actions. It innovatively extracts Doppler shift image data as the input of CGAN to generate virtual data with similar characteristics to expand the training sample set. The network is fine-tuned using transfer learning techniques to recognize multiple gesture action categories in different scenarios accurately. In tests of two new natural scenes and six new gesture categories, the Wi-TCG method achieved a high recognition accuracy of 93.1%, providing strong support for applying WiFi-based wireless sensing technology in contactless human-computer interaction.

Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study.

Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. This study aims to examine the feasibility of developing a novel channel state information-based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing-based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults' Wi-Fi sensing technology acceptance. This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. DERR1-10.2196/53447.

Gesture recognition method based on misalignment mean absolute deviation and KL divergence

At present, it has become very convenient to collect channel state information (CSI) from ubiquitous commercial WiFi network cards, and the location or activity of a human who affects the CSI can be recognized by analyzing the change of the CSI. Therefore, wireless sensing technology based on the CSI has received widespread attention. However, the existing CSI-based gesture recognition methods still have some problems, which include that subcarrier selection is not optimized and motion interval extraction is not accurate enough, so the accuracy of gesture recognition methods still needs to be further improved. In response to the above problems, a gesture recognition method based on misalignment mean absolute deviation (MMAD) and KL divergence is proposed in the paper, which is called MMAD-KL-GR method. This method uses the proposed MMAD algorithm to extract the CSI amplitude intervals containing gesture information, then selects subcarriers by comparing the KL divergence of the CSI amplitude, and finally uses the subspace K-nearest neighbor (KNN) algorithm to recognize the gestures. Several experiments show that the MMAD-KL-GR method can effectively improve the accuracy of the gesture recognition.

Routing the Messages to Grid Channel Based Model in Wireless Mesh Networks

Earlier the work has analyzed and implemented through IPV4 based traffic. The researchers have address the source IPv4 based routing in MRMC method. The router configures with a standard IPV4 based network extracts the source IPV4 address from the packet Header. This paper has been defined the network topology which was work on IPV6 protocol and it may also support IPV4 protocol. This paper implements the equalization that takes observation of channel state. The stations collect channel state information to their neighboring node, later on it was transfer to Gateway. The collected information was only possible through equalization method. In general, the equalization divided into two ways, per symbol and Sequence based which are according to receiver Theory. We are using sequence based theory under equalization that take data from maximum likelihood neighbors. We were used proposed Hybrid that take the existing method and combine with dynamic channel method. The proposed model avoids the multipath propagation problem and that problem only arise when we changes the channel from one wavelength to another wavelength. This paper showed proposed results that would be analysis the position of the packets in cluster head (CH). The router is taking as the cluster head which is being deployed on the number of nodes and these nodes randomly moves from one location to another. The MATLAB Simulator has been used in this research paper that helps to solve the complex mathematical equation. Network Simulator (NS2) has used to implement the Network Model.

Contactless Body Movement Recognition During Sleep via WiFi Signals

Body movement is one of the most important indicators of sleep quality for elderly people living alone. Body movement is crucial for sleep staging and can be combined with other indicators, such as breathing and heart rate to monitor sleep quality. Nevertheless, traditional sleep monitoring methods are inconvenient and may invade users' privacy. To solve these problems, we propose a contactless body movement recognition (CBMR) method via WiFi signals. First, CBMR uses commercial off-the-shelf WiFi devices to collect channel state information (CSI) data of body movement and segment the CSI data by sliding window. Then, the context information of the segmented CSI data is learned by a bidirectional recurrent neural network (Bi-RNN). Bi-RNN can fuse the forward and backward propagation information at some point, and input it into a deeper independently recurrent neural network (IndRNN) with residual mechanism to extract the deeper features and capture the time dependencies of CSI data. Finally, the type of body movement can be recognized and classified by the softmax function. CBMR can effectively reduce data preprocessing and the delay caused by manually extracting features. The results of an experiment conducted on a complex body movement data set show that our method gives desirable performance and achieves an average accuracy of greater than 93.5%, which implies a prospect application of CBMR.

AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems

With widely deployed WiFi network and the uniqueness feature (fingerprint) of wireless channel information, fingerprinting based WiFi positioning is currently the mainstream indoor positioning method, in which fingerprint database construction is crucial. However, for accuracy, this approach requires enough data to be sampled at many reference points, which consumes excessive efforts and time. In this paper, we collect Channel State Information (CSI) data at reference points by the method of device-free localization, then we convert collected CSI data into amplitude feature maps and extend the fingerprint database using the proposed Amplitude-Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model. The use of AF-DCGAN accelerates convergence during the training phase, and substantially increases the diversity of the CSI amplitude feature map. The extended fingerprint database both reduces the human effort involved in fingerprint database construction and the accuracy of an indoor localization system, as demonstrated in the experiments.

Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes

Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors, or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free, and privacy-preserving WiFi-enabled Internet of Things platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf WiFi routers to collect channel state information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition, respectively.

Consistently High MIMO Rates via Switched-Beam Antennas

The demand for wireless bandwidth is rising to unprecedented levels. The industry has responded with the inclusion of advanced PHY techniques, most notably multi-user (MU) MIMO, in the most recent Wi-Fi and LTE standards. However, despite the theoretical promise for large multiplexing gains, in practice the rate gains are modest due to a combination of large overhead to collect channel state information and not-so-well-conditioned channel matrices. In this paper, we propose to replace omni-directional antennas with inexpensive switched-beam antennas to produce well-conditioned channel matrices for MU-MIMO purposes with very low overhead. Remarkably, the experimental results with both software-defined radios and commercial Wi-Fi chipsets show that, when appropriate antenna modes are used, this leads to a $3.5\times-5\times $ average throughput improvement in indoor environments. What is more, our backward compatible protocol extension coupled with an efficient algorithm to select appropriate antenna modes, achieve the aforementioned gains with almost zero overhead.

Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning

With the unprecedented advancement of Internet of Things (IoT), automatic occupant activity recognition is becoming realizable for a myriad of emerging applications in smart buildings for energy efficiency and user experience enhancement. Existing activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose DeepHare, a deep learning-based human activity recognition scheme that can automatically identify common activities using only commodity WiFi-enabled IoT devices. We design a novel OpenWrt-based IoT platform to collect Channel State Information (CSI) measurements from commercial IoT devices. Moreover, an innovative deep learning framework, Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN), is proposed. We developed dedicated neural network architectures to sanitize the noise in raw CSI data, extract high-level salient features and reveal the inherent temporal dependencies among data for accurate human activity recognition. All the parameters in AE-LRCN are fine-tuned end-to-end automatically. Extensive experiments are conducted in typical three indoor environments and the experimental results demonstrate that DeepHare outperforms existing methods and achieves a 97.6% activity recognition accuracy without human intervention.

The Ergodic Capacity of the Multiple Access Channel Under Distributed Scheduling - Order Optimality of Linear Receivers

Consider the problem of a multiple-input multiple-output multiple-access channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect channel state information from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyze a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analyzed for both zero-forcing and minimum mean square error receivers, and its expected sum rate in the limit of large number of users is investigated. It is shown that for large number of users, it achieves the same scaling laws as the optimal centralized scheme.

MAC Protocols for Cooperative Diversity in Wireless LANs and Wireless Sensor Networks

In this paper, we present a survey of cooperative diversity-enabled Medium Access Control (MAC) protocols for Wireless LANs (WLANs) and Wireless Sensor Networks (WSNs). Cooperative diversity has emerged as a promising technique to combat fading and improve reliability in a wireless environment. In cooperative diversity protocols, neighboring nodes act as virtual multiple-input-multiple-output (VMIMO) systems, where they cooperate with the transmitter-receiver pair to deliver multiple copies of a packet to the receiver via independent fading channels. These multiple copies of the same packet can be combined at the receiver to recover the original packet, thus improving reliability by exploiting spatial diversity in the wireless channel. MAC protocols play an important part in realizing this concept by effectively coordinating transmissions between source, partner and destination nodes. Cooperative MAC protocols can also collect channel state information (CSI) between neighboring nodes and provide this information to the routing layer, where it can be exploited for partner selection. For this reason, the research community has proposed a large number of cooperative MAC protocols to realize this concept. We present an overview of these protocols and discuss their handshaking schemes, medium access schemes, results, strengths, and any observed shortcomings.

Radio resource management of self‐organizing OFDMA wireless mesh networks

AbstractMesh networks with universal frequency reuse is expected to play an important role for future wireless system architecture, to provide ultra high throughput and seamless access via cooperative base stations (BSs), relays, and users. Different from traditional networks with central control, BSs should operate in a self‐organizing way (i) to support stable high‐throughput green communications (ii) to avoid inter/intra cell interference, and (iii) to implement in an ease and backward compatible way; and resource management is thus a critical step to satisfy (i), (ii), and (iii). We first systematically present radio resource management based on extensive literature survey. To optimize system performance, we develop the cognitive resource management for wireless mesh networks with self‐organizing BSs to utilize entire available spectrum, while BSs collect channel state information of each user by spectrum sensing. From numerical results, our resource management can significantly improve network throughput over the benchmark system, which suggests good potential for many wireless systems such as LTE‐A and WiMAX 2 that are adopting mesh network architecture, to achieve universal frequency reuse. Copyright © 2010 John Wiley & Sons, Ltd.