Current end-to-end congestion-control feedback is too slow to track rapid wireless dynamics in cellular networks. We identify Grant-to-Buffer Ratio (GBR)—the ratio of base-station uplink grants to mobile-reported demand—as a millisecond-scale RAN signal of uplink resource scarcity. Measurements across AT&T, Verizon, and T-Mobile LTE/5G FDD/TDD deployments show that GBR tracks base-station uplink load and reveals congestion earlier than end-to-end feedback. Because GBR is derived from the mandatory BSR-grant exchange, it requires no base-station changes and captures scheduler decisions at their native timescale. We then design GBR-CC, a dual-loop controller that updates the sender rate on each GBR sample, using GBR for fast adaptation and end-to-end delay trends as a conservative fallback. This design lets the sender react before queues inflate while still handling non-radio bottlenecks through the outer loop. GBR-CC runs on commodity mobile devices without extra hardware or external tools. Experiments on commercial cellular networks show that GBR-CC improves average throughput over GCC by 50%, while reducing median playout latency by 32–53% and freeze rate by 60%; compared with BBR, it improves average throughput by 5% and halves median RTT.

Head orientation tracking has many potential applications in many fields,e.g., human-computer interaction, AR, and VR. In recent years, a large amount of existing work only focuses on the positioning of the user but ignore the tracking of the head orientation. Undoubtedly, with the information of the user's head orientation, many applications will have more opportunities for performance enhancement and better user experience. However, reviewing existing works regarding head tracking, the CV-based solutions have limited tracking angle range and privacy issues, the IMU-based solutions have accumulated errors, and the traditional microphone array-based solutions have low accuracy. Thus, none of these methods provides accurate and stable head orientation. In this paper, we propose EHeadTracker, an enhanced fine-grained head orientation tracking system based on head-mounted acoustic devices. This system recognizes rich head motions and achieves high-precision head direction tracking, while solving the problem of pivot point initialization. The experimental results show that the system can achieve an average error of 6 degrees in the head orientation tracking. To the best of our knowledge, EHeadTracker is the first system to use head-mounted acoustic devices to achieve head orientation tracking and has the highest accuracy in all current work.

5G wireless networks leverage complex scheduling, retransmission, and adaptation mechanisms to maximize their efficiency. These mechanisms interact to produce significant fluctuations in uplink and downlink capacity and latency, markedly impacting the the performance of real-time communication and multimedia applications, such as video conferencing. These applications are particularly sensitive to such fluctuations, resulting in lag, stuttering, distorted audio, and low video quality. In this paper, we present a cross-layer view of 5G networks and their impact on and interaction with video-conferencing applications. We conduct novel, detailed measurements of both private CBRS and commercial carrier cellular network dynamics, capturing physical- and link-layer events and correlating them with their effects at the network and transport layers, and the video-conferencing application itself. Our two datasets comprise days of low-rate campus-wide Zoom telemetry data, and hours of high-rate, correlated WebRTC-network-5G telemetry data. Based on these data, we trace performance anomalies back to root causes, identifying 24 previously unknown causal event chains that degrade 5G video conferencing. Armed with this knowledge, we build Domino, a tool that automates this process and is user-extensible to future wireless networks and interactive applications.

Rapid delay variations in today's access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today's video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks.

5G New Radio cellular networks are designed to provide high Quality of Service for application on wirelessly connected devices. However, changing conditions of the wireless last hop can degrade application performance, and the applications have no visibility into the 5G Radio Access Network (RAN). Most 5G network operators run closed networks, limiting the potential for co-design with the wider-area internet and user applications. This paper demonstrates NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can passively measure fine-grained RAN capacity, latency, and retransmission information. Application servers can take advantage of the measurements to achieve better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. We demonstrate the performance of NR-Scope by decoding the downlink control information (DCI) for downlink and uplink traffic of a 5G Standalone base station in real-time.

Congestion-control algorithms for video-conferencing applications work well in wired networks but are fragile in cellular networks due to high delay variations and variable capacity in these networks. This paper investigates the causes of delay variations in cellular networks using a cross-layer approach. By measuring a WebRTC application over LTE at both the physical and network layers, we identify the effects of such delay inflation caused by physical-layer resource scheduling and link-layer retransmissions.

Eye movements play a significant role in human-computer interaction and are widely recognized as an essential health indicator, making their detection both appealing and technically challenging. In this paper, we present a system named USEE that achieves high-precision capture of weak and aperiodic eye movements by utilizing fine-grained and ubiquitous ultrasound signals, capturing both blinking and more subtle saccades. We first identify signal changes associated with eye movements by capturing the unique impact of blinking. Further, we establish a pioneering relationship between the residuals from signal decomposition and subtle eye movements. Utilizing inno-vative signal processing architectures, we mitigate interference and effectively extract eye movement features. Subsequently, we employ one-dimensional convolutional operations in place of signal cross-correlation, designing filters for motion category identification and a lightweight convolutional neural network for saccade direction classification. This enables our system to serve as a foundational sensing layer for eye movement tracking, applicable across diverse applications. We implement USEE on both a research-purpose platform and a commodity Raspberry Pi. Extensive experimental results demonstrate the effectiveness of our system, achieving 91% accuracy in saccade recognition and 94% in blink detection. The system proves robust, even in challenging scenarios with strong interference, such as the presence of moving pedestrians.

Acoustic ranging has been viewed as a promising Human-Computer Interaction (HCI) technology in many scenarios, such as Augmented Reality (AR)/Virtual Reality (VR) and smart appliances. Most ranging systems with distributed devices undergo an extra calibration process to remove the timing errors. However, the calibration process needs user intervention. Furthermore, it should assume that the clock drifts are linear and stable, which is disabled within tens of minutes. In this paper, we introduce a self-calibrated acoustic ranging system that achieves sub-millimeter accuracy on distributed asynchronous devices. Based on our theoretical timing model, we precisely cancel both the system delay and the nonlinear clock drift with carefully designed Orthogonal Frequency-Division Multiplexing (OFDM) ranging signals. Our synchronization scheme achieves a timing accuracy of 1.9 microseconds, which allows us to build large-scale virtual acoustic arrays. Based on such a calibration scheme, our localization system achieves a ranging error of$\rm{0.39}~mm$within three meters in real-world experiments.

Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors and then optimize loops of operators. However, such unidirectional and one-off workflow strictly separates graph-level optimization and operator-level optimization into different system layers, missing opportunities for unified tuning.

Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this article, we introduce RespTracker , the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RespTracker uses a multi-stage algorithm to separate and recombine respiration signals from multiple paths so that it can track the respiration rate of multiple moving subjects. And it leverages features from multiple dimensions to separate different users in the same area. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects and cover a distance of three meters.

Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this paper, we introduce RESPTRACKER, the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RESPTRACKER uses a two-stage algorithm to separate and recombine respiration signals from multiple paths in a short period so that it can track the respiration rate of multiple moving subjects. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects at a distance of three meters.