Yinchao Yang

Zhonghao Liu, Yahao Ding, Yinchao Yang et al.

Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications, short-term human pose prediction facilitates continuous human tracking and resource allocation in advance. In this paper, we propose a Cramer-Rao bound (CRB) guided resource allocation framework for indoor mmWave ISCC systems to minimize the human pose prediction error under communication, latency, and energy constraints. We characterize the impact of sensing power on range-estimation uncertainty and point-cloud perturbation based on the CRB. To capture the impact of computation resources on prediction performance, we adopt an adaptive-depth Mamba-based pose prediction model, where lightweight prediction heads are attached after every layer to enable inference with different model depths. With this unified sensing-computation modeling, we establish a quantitative relationship among sensing power, model depth, and prediction error. Furthermore, we formulate a joint resource allocation problem to minimize the pose prediction error. To solve this problem efficiently, we develop an alternating optimization (AO)-based algorithm, where closed-form solutions are derived for the sensing power and model depth update steps. Simulation results show that the proposed scheme significantly reduces pose prediction error compared with baseline methods, validating its effectiveness for resource-constrained indoor human-centric ISCC systems.

Zhonghao Liu, Yinchao Yang, Yahao Ding et al.

This paper investigates a multi-user indoor integrated sensing and communication (ISAC) system operating in the terahertz (THz) band, designed for adaptive communication based on gesture recognition. Leveraging gesture tracking through an extended Kalman filter (EKF), the access point (AP) dynamically adjusts resource allocation in response to detected gesture variations, thereby improving sensing accuracy. Based on the gesture recognition results, the AP further updates the communication quality requirements of different users, enabling efficient resource allocation. To this end, an adaptive joint optimization algorithm for power allocation and beamforming is developed to maximize the overall sensing signal-to-interference-plus-noise ratio (SINR) while satisfying the gesture-dependent communication quality of service (QoS) constraints. Simulation results demonstrate that the proposed method effectively responds to gesture dynamics, achieving superior sensing accuracy and communication performance compared with conventional single-variable optimization baselines.

Xingyu Chen, Yinchao Yang, Nikola Fischer et al.

Remote robotic-assisted endovascular intervention offers a promising approach to reduce clinician radiation exposure and physical strain, while extending specialized vascular care to geographically distant regions. Despite advancements, teleoperated endovascular intervention remains underexplored, especially for time-sensitive interventions like mechanical thrombectomy for acute stroke. The aim of the current review was to determine the evidence regarding teleoperated endovascular robotic systems, covering technical feasibility, communication infrastructure, and clinical outcomes. The review further identified research gaps and future directions. Following PRISMA guidelines, 16 studies were included that met the inclusion criteria out of 2501 initial search results. We found that teleoperated catheters and guidewires, driven by mechanical or electromagnetic systems, can be navigated across distances up to 7000 km. With robust communication infrastructure, network latency remained within clinically acceptable limits (30-163 ms). Although initial outcomes highlighted 100% procedural success in small-scale human trials, most evidence stemmed from animal or phantom models. Overall, the findings suggest that teleoperated endovascular intervention can reduce occupational hazards, expand patient access to urgent procedures, and optimize resource allocation. Future research should be conducted in low and middle income countries to demonstrate broader geographical access. Ultimately, multi-center clinical trials are required to validate the safety, efficacy, and generalization in diverse clinical settings.

Zhouxiang Zhao, Jiaxiang Wang, Yahao Ding et al.

Efficient digital twin (DT) synchronization relies on maintaining high-fidelity virtual representations with minimal age of information (AoI). However, the synergistic potential of cooperative sensing and autonomous mobility of the sensing agent remains underexplored in existing DT synchronization frameworks. In this paper, we propose an agentic AI-empowered mobile embodied AI network (MEAN) framework for DT synchronization. In the proposed hybrid architecture, the base station (BS) conducts global orchestration, while the agents autonomously execute a five-stage closed-loop workflow: move-to-sense, cooperative sensing, onboard semantic processing, channel-aware mobility, and uplink transmission. To optimize synchronization performance, we formulate a joint topology dispatching and multidimensional resource allocation problem aimed at minimizing the maximum twin deviation across regions, subject to heterogeneous sensing fidelity and energy budget constraints. To tackle this, we develop a hierarchical two-layer optimization algorithm, where the outer-layer refines multi-agent assignment via a dynamic matching game, and the inner-layer iteratively optimizes the continuous resources. Extensive simulation results verify the convergence of the proposed algorithm and demonstrate its substantial superiority over multiple baseline schemes in reducing synchronization deviation. Furthermore, the results reveal that semantic compression serves as a vital substitute for channel resources in latency reduction under constrained bandwidth, while autonomous velocity adaptation provides an essential degree of freedom for the system to navigate the fundamental energy-time trade-off.

Yinchao Yang, Yahao Ding, Jiaxiang Wang et al.

Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming matrices. Performance is extensively evaluated via the Cramér-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (ISAC) schemes under constrained resource conditions.