With the continuous development of 5G network technology, its application fields are becoming increasingly widespread. However, in high-speed mobility scenarios, the network experience of 5G users will be greatly affected. The main reason for this is the discontinuity of network coverage in high-speed mobility scenarios. In addition, due to high-speed mobility, users will experience frequent handovers, which will greatly impact their network experience. Therefore, we propose a design method for a lens antenna that includes two sets of arrays directed in different directions, which can change the beam shape and enhance coverage. As a typical scenario of high-speed movement, we have for the first time verified the actual performance of this scheme within a commercial high-speed railway network to determine its feasibility. Compared with existing technologies, it can improve 18% of the coverage performance and enhance user experience in high-speed mobile scenarios.

Gesture recognition based on WiFi has recently attracted wide attention from academia and industry. However, the position-independent sensing is still a challenging problem. Existing work has made a breakthrough by extracting position-independent features through multiple transceiver pairs. We explore the position-independent gesture recognition methods that maintain accuracy and robustness while providing only one transceiver pair. Due to the limited information access and spatial resolution in that scenarios, noise cannot be effectively eliminated and gesture features are easily confused. Therefore, we propose a subcarrier selection method to select the subcarrier with less interference by noise. We extract dynamic phase as features for gesture recognition, which is position-independent. In addition, we split the dynamic phase variations of different gestures into a series of segments code based on the actions (traverse, approach and away). The easily confused gesture features are transformed into distinguishable gesture code. We developed a prototype on a Commercial Off-The-Shelf WiFi device. Extensive experimental results show that our system achieves position-independent gesture recognition using only one transceiver pair within an acceptable error range, achieving a maximum recognition accuracy of 94.33% and an average recognition accuracy of 87.25% in different positions.

Recent research has demonstrated the great potential of leveraging existing WiFi infrastructure for ubiquitous non-invasive respiration monitoring. Although this WiFi-based approach opens up a new direction for respiratory rate detection, existing studies are limited as only some simple scenarios have been considered. Consequently, the feasibility of using this technology in realistic scenarios needs to be further verified, especially for ensuring the detection performance in the following two cases: (1) long-distance and (2) different body postures. To address above two complex case studies, this paper presents several selection mechanisms to enable a robust WiFi-based respiration detection system. Firstly, a double-variance antenna links selection strategy is proposed to select the most sensitive link for breathing movements. Moreover, three subcarrier selection combining solutions are developed, where secondary selection is conducted to obtain the optimal respiration pattern in diverse situations. We conduct extensive experiments in two typical scenes. The evaluation results demonstrate that the detection error of our system is less than 0.7 bpm in each scene. More importantly, it outperforms compared with state-of-the-art systems.

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental human activity recognition system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network, which integrates an attention mechanism and dual loss function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets.

WiFi-based human activity recognition technology has attracted widespread attention for its prominent application value and theoretical significance. Existing approaches have made great achievements in the same domain sensing, which means the activity samples applied for training the model have a similar distribution with the testing data. However, in practical application, we hope that the same activity of different people with various states and habits in different locations can be accurately recognized and produce the same reaction. Therefore, cross-domain sensing technology is pretty important. Some studies explore the location-independent and environment-independent methods, but few attempts consider the influence of the initial states of the users, such as standing and sitting, which actually have very different effects on the transmission of the wireless signal. This paper presents a human activity recognition method adapted to different initial states. Meanwhile, we solve the accompanying issue of the small sample size sensing, obviating the need for the cumbersome wok resulting from the massive data collection. We take advantage of the idea of metric learning and few-shot learning to realize cross-domain sensing with very few samples. The experiments demonstrate the feasibility and excellent performance of our method, which could recognize human activities with different initial states as the training data.

Device-free human activity recognition based on WiFi signals has become a very popular research field. However, it still has one major problem that is activities of “unseen” humans cannot be accurately classified, which makes it infeasible in real-world application. To tackle this issue, in this paper, we present a human activity recognition (HAR) system based on identity (ID) transfer mechanism named CrossID, which can cross the boundaries of identity by taking the high-level personal characteristics of the source domain and target domain as IDs for training and transferring. Specifically, we employ the margin-based loss function to improve the training speed and accuracy. To fully evaluate the feasibility of the proposed approach for human activity recognition, a variety of the data samples have been taken at 16 locations conducted by six people performing four different types of activities. Through extensive experiments on our dataset, we verify the effectiveness, robustness, and generalization ability of proposed system. Our average recognition rate in the target domain is 95%, which is slightly lower than 98% in the source domain.

Device-free activity recognition is an indispensable technology in Human-Computer Interaction (HCI). The activity recognition system based on WiFi signals relying on the wide coverage of WiFi makes HCI more convenient. The previous research on WiFi-based activity recognition system has achieved high recognition accuracy. While the challenge that activity recognition is limited to fixed location and complex background, remains unresolved. In this paper, we propose a location-free activity recognition system which leverages fine-grained channel state information (CSI) to recognize same activities regardless of different locations and background. With CSI recorded in the Network Interface Card (NIC), Angle Difference of Arrival (ADoA) is reckoned to eliminate the location and background information, which is only consistent with the activity tendency. Then the Principal Component Analysis (PCA) method is utilized to reduce the dimension and followed by curve smoothing to make the signal more smoother. Furthermore, Bidirectional Long Short-Term Memory (BiLSTM) network is selected as ideal training machine to deal with issues that are highly correlated with time series. We use two commercial wireless network cards in the typical life scene, and finally achieve 93.7 % of recognition accuracy.