arXiv subjects

Yinchao Yang

Publications and source records attributed to Yinchao Yang.

At least 19 recordsLinked to original sources

arXiv2026

Dual-Security for Indoor OFDM-ISAC Systems via Temporal Artificial Noise

With the rapid development of integrated sensing and communication (ISAC) as a key enabler for future wireless networks, ensuring the security of both communication and sensing functions has become increasingly important. Current secure ISAC studies focus restrictively either on the communication or the sensing security, but not both. To bridge this gap, this paper investigates security for both, i.e., dual-security, in indoor orthogonal frequency division multiplexing (OFDM) based ISAC systems. Specifically, we consider a scenario in which a sensing user (SU) is authorised for sensing but may eavesdrop on communication data, while a communication user (CU) is authorised for communication but may perform unauthorised sensing. We chose this scenario as the pathological case where an authorised eavesdropper has more information and is more effective than an unauthorised one. To address this case, we propose the use of temporal artificial noise (AN) to prevent malicious CU sensing by enlarging its time-domain sensing error, and simultaneously degrade SU data eavesdropping by reducing its frequency-domain signal-to-noise-plus-interference ratio (SINR) with standard OFDM receiver processing. Meanwhile, our proposed scheme guarantees the sensing performance of the SU and the communication performance of the CU. We present numerical results that demonstrate AN can effectively provide dual protection for sensing and communication in OFDM-ISAC systems while guaranteeing the performance of legitimate users.

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arXiv2026

Alternating Optimization for Joint Resource Allocation in Full-Duplex Multi-Sector Fluid Antenna-Enabled Near-Field Systems

This paper proposes a full duplex fluid antenna near field system (FD-FANS) with a multi-sector antenna array that jointly exploits resource allocation, antenna mobility, and group-based transmitting (TX) and receiving (RX) partitioning. A spherical wave uplink downlink channel is established that accounts for residual self interference (SI), wireless energy transfer (WET), and geometric constraints on antenna motion. Within the FD-FANS framework, an efficient protocol is devised to enable simultaneous downlink energy transmission (DET) and uplink data transmission (UDT) at the base station (BS). Furthermore, we formulate, for both perfect and imperfect SI cancellation (SIC), a weighted sum rate (WSR) maximization problem over time power allocation, antenna positions, and binary group selection, under practical average and peak power limits, per antenna box constraints, minimum spacing, and a half TX half RX balance. To tackle the resulting non convex mixed integer design, we develop an efficient alternating optimization (AO) framework based on majorization minimization successive convex approximation (MM SCA). The proposed algorithm monotonically improves the objective and converges to a stationary solution of a continuous relaxation. Simulation results demonstrate that the proposed scheme achieves consistent performance gains over several benchmark designs, including half duplex FANS (HD FANS), FD fixed position antenna near field system (FD FPANS), non-grouped FD FANS, and far field counterparts, in terms of average sum rate (ASR), energy efficiency (EE), and user fairness, while exhibiting robustness to residual SI and channel uncertainty.

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arXiv2026

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

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.

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arXiv2026

CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems

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.

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arXiv2026

Remote Teleoperation of Endovascular Intervention Robots: A Systematic Review

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.

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arXiv2026

Digital Twin Synchronization Over Mobile Embodied AI Network With Agentic Intelligence

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.

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arXiv2026

Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks

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 Cram\'er-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.

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arXiv2026

Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks

In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks. To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. In the considered model, each edge user employs hyperdimensional computing (HDC) for local training, which replaces complex neural updates with simple hypervector operations, and applies differential privacy (DP) noise to protect transmitted model information. We optimize the total energy of computation and communication under both latency and privacy constraints. We formulate the problem as an optimization that minimizes the total energy of all users by jointly allocating HDC dimension, transmission time, system bandwidth, transmit power, and CPU frequency. To solve this problem, a sigmoid-variant function is proposed to characterize the relationship between the HDC dimension and the convergence rounds required to reach a target accuracy. Based on this model, we develop two alternating optimization algorithms, where closed-form expressions for time, frequency, bandwidth, and power allocations are derived at each iteration. Since the iterative algorithm requires a feasible initialization, we construct a feasibility problem and obtain feasible initial resource parameters by solving a per round transmission time minimization problem. Simulation results demonstrate that the proposed FL-HDC-DP framework achieves up to 83.3% total energy reduction compared with the baseline, while attaining about 90% accuracy in approximately 3.5X fewer communication rounds than the NN baseline.

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arXiv2026

Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)

This paper investigates the problem of minimizing total energy consumption for secure federated learning (FL) in wireless edge networks, a key paradigm for decentralized big data analytics. To tackle the high computational cost and privacy challenges of processing large-scale distributed data with conventional neural networks, we propose an FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. Each edge device employs hyperdimensional computing (HDC) for lightweight local training and applies differential privacy (DP) noise to protect transmitted model updates. The total energy consumption is minimized through a joint optimization of the HDC dimension, transmit power, and CPU frequency. An efficient hybrid algorithm is developed, combining an outer enumeration search for HDC dimensions with an inner one-dimensional search for resource allocation. Simulation results show that the proposed framework achieves up to 83.3% energy reduction compared with baseline schemes, while maintaining high accuracy and faster convergence.

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arXiv2026

Dual Security for MIMO-OFDM ISAC Systems: Artificial Ghosts or Artificial Noise

Integrated sensing and communication (ISAC) enables the efficient sharing of wireless resources to support emerging applications, but it also gives rise to new sensing-based security vulnerabilities. Here, potential communication security threats whereby confidential messages intended for legitimate users are intercepted, but also unauthorized receivers (Eves) can passively exploit target echoes to infer sensing parameters without users being aware. Despite these risks, the joint protection of sensing and communication security in ISAC systems remains unexplored. To address this challenge, this paper proposes a two-layer dual-secure ISAC framework that simultaneously protects sensing and communication against passive sensing Eves and communication Eves, without requiring their channel state information (CSI). Specifically, transmit beamformers are jointly designed to inject artificial noise (AN) to introduce interference to communication Eves, while deliberately distorting the reference signal available to sensing Eves to impair their sensing capability. Furthermore, the proposed design generates artificial ghosts (AGs) with fake angle-range-velocity profiles observable by all receivers. Legitimate receivers can suppress these AGs, whereas sensing Eves cannot, thereby significantly reducing their probability of correctly detecting the true targets. Numerical results demonstrate that the proposed framework effectively enhances both communication and sensing security, while preserving the performance of communication users and legitimate sensing receivers.

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arXiv2025

Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

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arXiv2025

Fluid Antenna-enabled Near-Field Integrated Sensing, Computing and Semantic Communication for Emerging Applications

The integration of sensing and communication (ISAC) is a key enabler for next-generation technologies. With high-frequency bands and large-scale antenna arrays, the Rayleigh distance extends, necessitating near-field (NF) models where waves are spherical. Although NF-ISAC improves both sensing and communication, it also poses challenges such as high data volume and potential privacy risks. To address these, we propose a novel framework: near-field integrated sensing, computing, and semantic communication (NF-ISCSC), which leverages semantic communication to transmit contextual information only, thereby reducing data overhead and improving efficiency. However, semantic communication is sensitive to channel variations, requiring adaptive mechanisms. To this end, fluid antennas (FAs) are introduced to support the NF-ISCSC system, enabling dynamic adaptability to changing channels. The proposed FA-enabled NF-ISCSC framework considers multiple communication users and extended targets comprising several scatterers. A joint optimization problem is formulated to maximize data rate while accounting for sensing quality, computational load, and power budget. Using an alternating optimization (AO) approach, the original problem is divided into three sub-problems: ISAC beamforming, FA positioning, and semantic extraction ratio. Beamforming is optimized using the successive convex approximation method. FA positioning is solved via a projected Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, and the semantic extraction ratio is optimized using bisection search. Simulation results demonstrate that the proposed framework achieves higher data rates and better privacy preservation.

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arXiv2025

Integrated Sensing, Computing and Semantic Communication for Vehicular Networks

This paper introduces a novel framework for integrated sensing, computing, and semantic communication (ISCSC) within vehicular networks comprising a roadside unit (RSU) and multiple autonomous vehicles. Both the RSU and the vehicles are equipped with local knowledge bases to facilitate semantic communication. The framework incorporates a secure communication design to ensure that messages intended for specific vehicles are protected against interception. In this model, an extended Kalman filter (EKF) is employed by the RSU to accurately track all vehicles. We formulate a joint optimization problem that balances maximizing the probabilistically constrained semantic secrecy rate for each vehicle while minimizing the sum of the posterior Cramér-Rao bound (PCRB), subject to the RSU's computing capabilities. This non-convex optimization problem is addressed using Bernstein-type inequality (BTI) and alternating optimization (AO) techniques. Simulation results validate the effectiveness of the proposed framework, demonstrating its advantages in reliable sensing, high data throughput, and secure communication.

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arXiv2025

Integrated Sensing, Computing, and Semantic Communication with Fluid Antenna for Metaverse

The integration of sensing and communication (ISAC) is pivotal for the Metaverse but faces challenges like high data volume and privacy concerns. This paper proposes a novel integrated sensing, computing, and semantic communication (ISCSC) framework, which uses semantic communication to transmit only contextual information, reducing data overhead and enhancing efficiency. To address the sensitivity of semantic communication to channel conditions, fluid antennas (FAs) are introduced, enabling dynamic adaptability. The FA-enabled ISCSC framework considers multiple users and extended targets composed of a series of scatterers, formulating a joint optimization problem to maximize the data rate while ensuring sensing accuracy and meeting computational and power constraints. An alternating optimization (AO) method decomposes the problem into subproblems for ISAC beamforming, FA positioning, and semantic extraction. Simulations confirm the framework's effectiveness in improving data rates and sensing performance.

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arXiv2024

Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks

This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base station (BS) and paired users. The BS generates and combines semantic information of several frames simultaneously requested by paired users into a single signal. Users recover their frames from this combined signal and input the recovered frames into a GAI-based video frame interpolation model to generate the intermediate frame. To optimize transmission rates and temporal gaps between simultaneously transmitted frames, we formulate an optimization problem to maximize the system sum rate while minimizing temporal gaps. Since the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to its dependence on transmit power, we propose a three-step solution. First, we develop a user pairing algorithm that pairs two users with the highest preference value, a weighted combination of semantic transmission rate and temporal gap. Second, we optimize inter-group power allocation by formulating an optimization problem that allocates proper transmit power across all user groups to maximize system sum rates while satisfying each user's minimum rate requirement. Third, we address intra-group power allocation to enhance each user's performance. Simulation results demonstrate that our method improves transmission rates by up to 24.8%, 45.8%, and 66.1% compared to fixed-power non-orthogonal multiple access (F-NOMA), orthogonal joint source-channel coding (O-JSCC), and orthogonal frequency division multiple access (OFDMA), respectively.

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arXiv2024

E-Healthcare Systems: Integrated Sensing, Computing, and Semantic Communication with Physical Layer Security

This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT device computing capabilities. Semantic metrics such as semantic transmission rate and semantic secrecy rate are derived to evaluate data rate performance and GDPR risk, respectively, while the Cramér-Rao Bound (CRB) assesses sensing performance. Simulation results demonstrate the framework's effectiveness in ensuring reliable sensing, high data rates, and secure communication.

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arXiv2024

Fluid Antenna-Assisted Near-Field System

This paper proposes a fluid antenna (FA)-assisted near-field integrated sensing and communications (ISAC) system enabled by the extremely large-scale simultaneously transmitting and reflecting surface (XL-STARS). By optimizing the communication beamformer, the sensing signal covariance matrix, the XL-STARS phase shift, and the FA position vector, the Cramér-Rao bound (CRB), as a metric for sensing performance, is minimized while ensuring the standard communication performance. A double-loop iterative algorithm based on the penalty dual decomposition (PDD) and block coordinate descent (BCD) methods is proposed to solve the non-convex minimization problem by decomposing it into three subproblems and optimizing the coupling variables for each subproblem iteratively. Simulation results validate the superior performance of the proposed algorithm.

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arXiv2024

Fair Resource Allocation for Probabilistic Semantic Communication in IIoT

In this paper, the problem of minimum rate maximization for probabilistic semantic communication (PSCom) in industrial Internet of Things (IIoT) is investigated. In the considered model, users employ semantic information extraction techniques to compress the original data before sending it to the base station (BS). During this semantic compression process, knowledge graphs are employed to represent the semantic information, and the probability graph sharing between users and the BS is utilized to further compress the knowledge graph. The semantic compression process can significantly reduce the transmitted data size, but it inevitably introduces additional computation overhead. Considering the limited power budget of the user, we formulate a joint communication and computation optimization problem is formulated aiming to maximize the minimum equivalent rate among all users while meeting total power and semantic compression ratio constraints. To address this problem, two algorithms with different computational complexities are proposed to obtain suboptimal solutions. One algorithm is based on a prorate distribution of transmission power, while the other traverses the combinations of semantic compression ratios among all users. In both algorithms, bisection is employed in order to achieve the greatest minimum equivalent rate. The simulation results validate the effectiveness of the proposed algorithms.

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