The rapid proliferation of emerging video services has substantially increased the demand for real-time transmission of high-definition video content. However, optimizing the trade-off between video transmission quality and communication bandwidth remains a significant challenge. To address this issue, we propose RAJSCC, a rate-adaptive video deep joint source-channel coding (DeepJSCC) framework. RAJSCC incorporates a spatio-temporal token merging mechanism to aggregate semantically similar tokens, effectively reducing redundancy both within and across frames. Additionally, an adaptive token merging predictor, designed based on simple statistical features of the input videos, enables dynamic rate control at the group of pictures (GoP) level, ensuring smooth and continuous variation in the overall video coding rate. Extensive experiments demonstrate that RAJSCC significantly outperforms traditional video transmission schemes, such as H.264 and H.265 with low-density parity-check (LDPC), as well as existing DeepJSCC methods, in terms of reconstruction quality. More importantly, the proposed adaptive spatio-temporal token merging mechanism reduces bandwidth consumption by 63.5% and computational cost by 13.0%, while incurring only a marginal 1–2 dB degradation in reconstruction quality. These findings highlight the effectiveness of RAJSCC in achieving a superior balance between transmission efficiency and video quality, making it a promising solution for real-time high-definition video communication in bandwidth-constrained environments.

Respiration serves as a critical indicator of human health, reflecting the well-being of various bodily organs. The potential of device-free WiFi signals for respiration detection has been shown in recent investigations. Nevertheless, there are several disadvantages to the current WiFi signal-based respiration detection methods. These include inadequate utilization of the respiratory component within the signal, and the high-quality subcarriers cannot be screened out effectively. In response to these issues, we propose a novel respiration detection system named Wi-Mapping. Firstly, to enhance the utilization of the respiratory component within the signal, we introduce a novel complex plane mapping approach. This method reconstructs a signal with a more noticeable respiratory component by integrating the original amplitude and phase of Channel State Information (CSI). Wi-Mapping then concentrates on utilizing features in the frequency domain and subcarrier dimension. Additionally, a subcarrier screening method is proposed, which combines Respiration Energy Ratio (RER) and correlation between subcarriers. This method can effectively screen out subcarriers with higher quality. Finally, a dataset containing various scenes was established to confirm Wi-Mapping’s respiration detecting capabilities. The detection error achieved by Wi-Mapping is 0.382, with a detection rate of 86.91%, surpassing that of state-of-the-art methods.

Current WiFi-based respiration detection algorithms may experience performance degradation due to variations in user location, as the relationship between user location and patterns of respiration has not been adequately considered. To overcome this limitation, this paper proposes a spatially directional respiration detection approach, named Wi-locind. Wi-locind employs antenna arrays on commercial WiFi receivers to achieve directional enhancement of respiration signals. Combined with post-filtering techniques, Wi-locind is capable of extracting respiration patterns that are independent of changes in the user's location. Specifically, the Minimum Variance Distortionless Response algorithm is used to identify the arrival angle of the target user and directionally enhance the received signal in the corresponding direction. The Empirical Mode Decomposition algorithm is subsequently utilized to suppress the environmental noise and time domain artifacts caused by the enhancement method, enabling the extraction of the target's respiration pat-tern. Our results show that the proposed approach consistently achieves an average absolute error of less than 0.3 breaths per minute across all positions, significantly outperforming the baseline approaches.

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.