In this paper, we investigate a millimeter-wave secure integrated sensing and communication system assisted by the sub-connected (SC) active reconfigurable intelligent surface (SC-ARIS), where the dual-function radar and communication station (DFBS) employs a hybrid precoding structure. We formulate an optimization problem to jointly design the DFBS hybrid precoding and SC-ARIS beamforming, aiming to maximize the secure energy efficiency while ensuring communication and sensing qualities. To address the above non-convex problem, we utilize alternating optimization technique to decouple it into two subproblems, where DFBS hybrid precoding and SC-ARIS beamforming are respectively optimized. For the first one, we first propose an iterative algorithm to solve the equivalent digital precoding based on constrained concave-convex procedure, Taylor expansion, semidefinite relaxation (SDR) and fractional programming techniques. Then, the hybrid precoding is obtained rely on the manifold optimization alternating minimization technique. For the later one, we propose an iterative algorithm based on the SDR. Considering a more realistic scenario, we extend to the imperfect eavesdropping channel, and propose a robust beamforming design scheme. Finally, simulation results show the effectiveness of the proposed schemes.

Recent years have witnessed the significant advancement of WiFi-based human gait identification. However, existing works require the human subjects to walk with a standard gait pattern, i.e., the normal walking speed and free limb swings. This significantly impedes the wide adoption of these technologies since humans walk with diverse gait patterns in reality. To this end, we present a Pattern-Independent Authentication System (PIAS), the first system that enables human identification across different gait patterns using commodity WiFi. First, we extract Doppler spectrograms as human gait signatures and standardize them to narrow the distribution discrepancies between different gait patterns. Then, we design a Class-level Unsupervised Domain Adaptive Network (CUDAN) to extract human identity features in the source and target domains while minimizing the feature domain discrepancies at the class-level to achieve pattern-independent human gait identification. Extensive experiments are conducted on different gait patterns and the results demonstrate that our system can achieve the highest performance compared to the baseline methods, demonstrating its effectiveness in human identification across diverse gait patterns.

In this paper, we investigate a millimeter wave integrated sensing and communication system with aid of the hybrid reconfigurable intelligent surface (HRIS), where the dual-function radar and communication station (DFBS) applies the hybrid precoding structure. On this basis, we consider the sensing and communication performance, respectively, and formulate two optimization problems. One is to maximize the worst-case illumination power while ensuring the communication quality, and another is to maximize the total achievable rate while satisfying the sensing performance. To solve them, we first decouple each nonconvex problem into three subproblems via the alternative optimization technique. For the former one, we transform DFBS and HRIS beamforming optimization subproblems into the convex ones by the quadratic constrained quadratic programming (QCQP) and semidefinite program relaxation (SDR) techniques, and obtain the solutions by standard convex optimization technique. For the later one, fractional programming is applied to decouple the objective function, and then we transform DFBS and HRIS beamforming design subproblems into the convex ones by QCQP and Taylor expansion techniques, and obtain the solutions by the alternating direction method of multipliers (ADMM). For the hybrid precoding design subproblems of DFBS in both problems, a manifold optimization-alternating minimization (MO-AltMin) algorithm based on minimizing the Euclidean distance is used to obtain the solutions. Simulation results show the effectiveness of the proposed schemes.

Wi-Fi-based continuous activity sensing is of great importance to personal healthcare, security monitoring, and healthy lifestyle assessment. However, it remains challenging to understand multiperson continuous activities as the Wi-Fi signals reflected by each person are mixed up in the Wi-Fi channel state information (CSI). To this end, we present MuSense, the first Wi-Fi-based system that enables multiperson continuous activity segmentation and recognition using commodity devices. In MuSense, we design a Wi-Fi network interface card (NIC) combination and calibration (NICCC) algorithm to construct a high-resolution receiving array and calibrate the CSI measurement noises of this array. On this basis, we propose a multiperson reflection signal separation (MPRSS) algorithm to completely and practically separate each person’s Wi-Fi reflection signals, obtaining the amplitude attenuation and phase shift corresponding to each person’s activities on the subcarrier dimension. Finally, we design an unsupervised adversarial continuous activity sensing network (UACAS-Net), with a two-stage adversarial training method to achieve generalized multiperson continuous activity sensing. Through two-stage adversarial training, UACAS-Net can capture distinguishing features of continuous activities from separated reflection signal streams in the source and target domains while minimizing the feature domain discrepancies to segment and recognize each person’s continuous activities in different domains. Intensive experiments are conducted under three different scenarios and the results demonstrate the effectiveness and practicality of MuSense for multiperson continuous activity sensing.

Previous works have verified the feasibility of WiFi-based human pose estimation (HPE). However, their crucial limitations lie in requiring favorable placement of Wi-Fi devices and only sensing human poses in a small area. To address these issues, we propose WiLink, a Wi-Fi-based 3D HPE system that selectively uses several existing Wi-Fi links to achieve accurate HPE everywhere indoors. We find that the Channel State Information (CSI) fluctuations caused by human pose changes over different Wi-Fi links are various. According to the effectiveness of Wi-Fi links for human pose sensing, we classify the links as Noise-Dominated Links, Most-Effective Links and Redundant Links. Then we propose a Dynamic Link Selection (DLS) mechanism to adaptively select Most-Effective Links for HPE. This process maximizes the importance and minimizes the redundancy of the selected links. Finally, we feed the CSI samples corresponding to Most-Effective Links into a neural network to estimate human poses. Intensive experiments are conducted, and the results show that WiLink achieves a good performance in the scenario with multiple available Wi-Fi links.

Daily activity monitoring is essential to healthy lifestyle assessment and personal healthcare, among which Wi-Fi-based solutions have attracted increasing attention due to their no-intrusive and privacy-protected characters. However, related researches are based on the assumption that there is an interval between two activities, during which the target is thought to be static. This assumption falls short of reality as human activities are performed continuously in daily life. Therefore, this article aims to design a nonintrusive and privacy-protected system, namely, Wi-Monitor, to monitor human activities in daily life. In Wi-Monitor, we first fragmentize Wi-Fi channel state information (CSI) streams into CSI bins and design a feature extraction network to extract activity fragmentation features (AFFs) from these CSI bins. From the extracted AFFs, a temporal convolutional network (TCN) is further used to capture activity continuity features (ACFs), which are used as distinguishing characteristics of continuous activities. Finally, Wi-Monitor utilizes these distinguishing characteristics to segment and recognize human activities in daily life simultaneously to achieve daily activity monitoring. In addition, we design an over-segmentation suppression mechanism with two training stages in Wi-Monitor to overcome the over-segmentation issue and enhance the activity monitoring accuracy. Intensive experiments are conducted in three different scenarios and the results demonstrate the effectiveness and practicality of Wi-Monitor for daily activity monitoring.

Recently, commodity Wi-Fi devices have been shown to be able to construct human pose images, i.e., human skeletons, as fine-grained as cameras. Existing papers achieve good results when constructing the images of subjects who are in the prior training samples. However, the performance drops when it comes to new subjects, i.e., the subjects who are not in the training samples. This paper focuses on solving the subject-generalization problem in human pose image construction. To this end, we define the subject as the domain. Then we design a Domain-Independent Neural Network (DINN) to extract subject-independent features and convert them into fine-grained human pose images. We also propose a novel training method to train the DINN and it has no re-training overhead comparing with the domain-adversarial approach. We build a prototype system and experimental results demonstrate that our system can construct fine-grained human pose images of new subjects with commodity Wi-Fi in both the visible and through-wall scenarios, which shows the effectiveness and the subject-generalization ability of our model.

In this work, we address the issue of Wi-Fi-based human activity recognition (HAR) system in densely deployed Wi-Fi environments. With the benefit of sufficient information provided by Wi-Fi channel state information (CSI), HAR based on Wi-Fi has become an active research area in recent years. Traditional Wi-Fi CSI-based HAR applications usually focus on utilizing one Wi-Fi transmitter and one or several Wi-Fi receivers to extract the activity-related features, ignoring the communication among multiple Wi-Fi devices in the real world. In this paper, we present a novel Wi-Fi link selection model on the basis of continuous state decision-making process in which CSI is modeled as a part of the state. The model, referred to as WiAgent, takes an action of selecting one Wi-Fi link according to current state, and then updates the state for the choice of the next action. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi transmitters exist.