arXiv subjects

Wen Chen

Publications and source records attributed to Wen Chen.

At least 19 recordsLinked to original sources

arXiv2026

Movable Antennas Enabled Wireless Powered Networks: Principles and Technologies

As an emerging framework, movable antenna (MA)-enabled wireless powered networks (WPNs) have attracted growing attention. WPNs integrate wireless communication and energy transfer. MA can dynamically adjust the position of antenna units by introducing additional spatial degrees of freedom, so as to make full use of channel gain, optimize the effect of energy beamforming, and further improve the performance of WPNs. In this article, we first classify the implementations of MA by driving architectures, and review the fundamental principles of WPNs. We then highlight the key advantages of MA-enabled WPNs in enhancing wireless power transfer efficiency, realizing flexible and adaptive beamforming, and improving system robustness and interference resilience. Furthermore, four representative application scenarios and three key enabling technologies are discussed. A case study is also presented to show the reduction in transmit power brought by MA for WPNs. Finally, we discuss the challenges and future directions of MA-enabled WPNs, aiming to provide reference for future research and practice.

cs.NI↗

arXiv2026

Joint Antenna Rotation and Polarforming for Satellite Communications

This paper investigates joint antenna rotation and polarforming for low-Earth-orbit (LEO) communications, where a satellite equipped with rotatable dual-polarized antenna elements receives uplink signals from ground terminals (GTs) distributed across multiple clusters. In particular, antenna rotation redistributes directional gains across angularly separated clusters, while polarforming improves the separability of spatially correlated GT channels within each cluster. We formulate a weighted sum-rate maximization problem that jointly optimizes rotatable antenna (RA) orientation trajectories, element-wise polarformers, and digital receive filters under steering-range and inter-epoch slew constraints. To solve the resulting nonconvex problem, we develop a weighted minimum mean-square error (WMMSE)-based alternating algorithm, in which the optimal polarformer for each element is derived in semi-closed form, while an exact boresight parameterization yields convex feasible sets for RA-trajectory optimization, enabling Armijo-based gradient updates. Simulation results demonstrate that the proposed joint design outperforms benchmark schemes and reveal the complementary roles of antenna rotation in directional-gain allocation and polarforming in improving channel separability.

eess.SP↗

arXiv2026

UserArena: Benchmarking Interactive User Simulation for Long-Horizon Shopping Agents

Agentic recommendation pursues shopper goals through sequences of context-dependent decisions. Developing and evaluating such policies requires faithful recommendation world models that capture both the interactive shopping environment and users' behavioral responses over long interactions. Existing resources rarely provide long-horizon, fine-grained real-user trajectories or support trajectory-level evaluation and offline validation against production A/B results. We introduce UserArena, a real-world e-commerce interactive benchmark containing 64,729 session trajectories from 11,010 users, spanning 566,102 items and 13 action types. UserArena pairs these trajectories with an interactive environment and supports offline validation against outcomes from a controlled production A/B test, enabling five-level evaluation from output validity and local decisions to sequential behavior, personalization, and outcomes. To improve simulator fidelity over these long-horizon interactions, we further propose On-Policy Trajectory Distillation (OPTD), which uses trajectory-level intent to provide dense teacher targets on student-generated rollouts. Experiments show that OPTD achieves the strongest overall fidelity among the evaluated simulators under UserArena's evaluation protocol and most closely reproduces the production policy effect in offline simulation.

cs.IR↗

arXiv2026

GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery

Agentic artificial intelligence (AI) systems hold immense potential for automating complex workflows in materials discovery and design, but extending them to domain-intensive mechanistic investigations remains challenging. Here, we present GBClaw, an agentic AI framework for investigating the structure and deformation of general grain boundaries (GBs). Although general GBs constitute the vast majority of real-world interfaces in polycrystalline materials, they remain understudied because their structural complexity and interfacial mismatch complicate experimental and modeling research. GBClaw integrates large language models (LLMs) into a modular architecture comprising an orchestrator, specialized agents, domain-specific tools, simulation interfaces, and hierarchical memory. Through dynamic replanning and iterative workflow refinement, the framework effectively connects systematic simulations with targeted mechanistic analysis. Applied to 14 general asymmetric tilt GBs in nickel, GBClaw characterized interfacial defect structures and identified diverse deformation modes across these incommensurate boundaries. Comparing boundary sliding with and without secondary GB misfit dislocations revealed substantial differences in shear strength, underscoring the critical role of defect hierarchies in sliding resistance. Furthermore, researcher-guided follow-up calculations traced an anomalously high shear resistance to metastable boundary structures. This work demonstrates that combining agentic AI with domain expertise bridges high-throughput computational screening with deep physical understanding of complex material interfaces.

cond-mat.mtrl-sci↗

arXiv2026

Joint Beamforming Optimization and Dynamic Tracking in RIS-Enabled Secure ISAC Systems

This paper investigates a reconfigurable intelligent surface (RIS)-enabled secure integrated sensing and communication (ISAC) system, where the direct links between the base station (BS) and users are blocked, and the mobile eavesdropper is treated as the sensing target. The mobility of the eavesdropper induces time-varying wiretap channels, potentially compromising secure transmission when outdated eavesdropper information is used. To address this challenge, the BS dynamically tracks the eavesdropper over consecutive time slots and exploits the predicted state information to adapt its transmission strategy. A sum secrecy rate maximization problem is formulated by jointly optimizing the BS beamforming, artificial noise, and RIS reflection coefficients, subject to a constraint on the tracking error covariance to ensure the required sensing accuracy. To tackle the resulting nonconvex optimization problem with temporal coupling, an iterative algorithm integrating an extended Kalman filter (EKF) with an alternating optimization framework is developed. Specifically, the EKF recursively predicts and updates the eavesdropper's state, while the joint optimization problem is decomposed into four tractable subproblems that are solved iteratively. Simulation results demonstrate that the proposed algorithm achieves improved secrecy performance while effectively tracking the trajectory of the mobile eavesdropper, compared with benchmark schemes.

eess.SP↗

arXiv2026

Movable Antenna-Enabled Multi-Static Cooperative Secure ISAC: Architecture, Mechanisms, and Enabling Technologies

The broadcast nature of integrated sensing and communication (ISAC) signals exposes them to physical-layer security (PLS) threats, including eavesdropping and jamming. Conventional fixed-position antennas provide limited spatial flexibility under highly correlated legitimate, sensing, and adversarial channels, while multi-static cooperative ISAC offers distributed spatial diversity but remains vulnerable to information leakage. To address these limitations, this article proposes a novel movable antenna (MA)-enabled multi-static cooperative secure ISAC framework centered on dual-scale spatial reconfiguration. Specifically, continuous MA repositioning reshapes local propagation channels, while distributed multi-static nodes provide complementary network-level spatial diversity. Their synergy transforms local antenna geometry and distributed network geometry into a unified controllable spatial resource for jointly enhancing communication, sensing, and PLS. This article first presents the system architecture of the proposed framework and its representative application scenarios, followed by an analysis of the physical-layer mechanisms underlying the resulting gains. Subsequently, we elaborate on the key enabling technologies for practical deployment. A case study further demonstrates the communication-sensing-security trade-off and antenna-saving potential of the proposed framework. Finally, open challenges and future research directions are identified toward practical MA-enabled multi-static secure ISAC networks.

eess.SP↗

arXiv2026

Dual Secure Sensing and Communications in MA-Enabled Networked ISAC Systems

Integrated sensing and communication (ISAC) is envisioned as a cornerstone technology for next-generation networks, yet it introduces significant security risks in both communication and sensing domains. To mitigate the coverage limitations inherent in conventional mono-static or bi-static setups, this paper proposes a networked ISAC framework empowered by movable antennas (MAs). By leveraging flexible antenna reconfiguration, MAs provide additional spatial degrees of freedom to significantly boost beamforming gain and interference suppression. Specifically, we investigate a novel dual-domain security paradigm where networked base stations collaboratively protect uplink (UL) and downlink (DL) transmissions from communication eavesdropping while simultaneously safeguarding target sensing data against sensing eavesdroppers (SEs). To this end, we formulate a joint optimization problem to maximize the sum of secrecy rates, subject to sensing requirements, SE eavesdropping probability constraint, and practical MA deployment constraints, by jointly optimizing receive combiners, transmit beamformers, UL power allocation, and MA positions. To address this non-convex problem with highly coupled variables, an efficient alternating optimization algorithm is proposed, integrating closed-form solutions for receive combiners, successive convex approximation for transmit beamforming and UL power, and a tailored sensing-aware multi-candidate particle swarm optimization for the high-dimensional MA positioning. Extensive numerical results validate the effectiveness of the proposed algorithms, demonstrating that the proposed MA-enabled networked cooperative ISAC design significantly outperforms conventional fixed-position antenna-based designs in terms of security performance, while achieving notable power- and antenna-saving gains.

eess.SP↗

arXiv2026

Robust Design of Sensing-Assisted Secure Transmission for RIS-Aided ISAC System under Eavesdropper 3D Positioning Uncertainty

Localization errors in estimating the position of a potential eavesdropper (Eve) pose a critical challenge to secure transmission in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems. To address this issue, this paper proposes a sensing-assisted robust secure transmission framework that exploits ISAC sensing to characterize the positioning uncertainty induced by these errors. The RIS is partitioned into a reflection region and a sensing reception region, supporting sensing and communication through spatial separation. By jointly exploiting observations collected at the base station (BS) and the RIS sensing reception region, maximum likelihood estimation (MLE) is employed to estimate the arrival angles, from which Eve's three-dimensional (3D) position is obtained through cross localization. The resulting localization Cramer-Rao lower bound (CRLB) is then used to construct an ellipsoidal uncertainty region around the estimated position. A two-stage optimization strategy is developed. In Stage I, the sensing beamformer and RIS reflection coefficients are jointly optimized to improve localization accuracy. In Stage II, the resulting positioning uncertainty is incorporated into a robust secure transmission design, where the communication beamformer and RIS reflection coefficients are jointly optimized to maximize the worst-case secrecy rate. Simulation results demonstrate that the proposed approach outperforms benchmark schemes without RIS assistance and with random RIS configurations in terms of secrecy performance. The proposed framework effectively mitigates secrecy performance degradation caused by localization errors in Eve's position.

eess.SP↗

arXiv2026

Antenna Positioning and Beamforming Optimization in MA Enabled Secure ISAC Systems: A Gradient-Based Meta Learning Approach

Integrated sensing and communications (ISAC) significantly improves spectral efficiency but introduces security risks regarding the interception of embedded communication signals. This paper proposes an movable antenna (MA)-enabled secure ISAC system that utilizes the spatial degrees of freedom of MA to mitigate these risks. Then, a problem is formulated to maximize the system secrecy rate by jointly optimizing antenna positioning, transmit beamforming, and artificial noise. However, the principal challenge arises from the non-convexity of the optimization problem and the strong coupling of the optimization variables. Generally, traditional optimization methods for this problem suffer from complex mathematical derivations, while existing deep learning approaches rely heavily on the training data distribution. To address these issues, we introduce a gradient-based meta learning (GML) algorithm, which works without pre-training and demonstrates favorable performance. Specifically, the algorithm establishes a neural network for each optimization variable, where the gradient of the objective function with respect to the variable serves as the input, and the output of the network determines the variable's update step. By handling the constraints and constructing penalty terms, the global loss function is used to guide the optimization process. Extensive numerical simulations confirm that the proposed algorithm achieves satisfactory performance in terms of both communication security and sensing capabilities.

eess.SP↗

arXiv2026

UniWAM: Unified World-Action Model

Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-action models inherit spatiotemporal priors from video generation models, yet remain limited in semantic understanding and reasoning under distribution shifts. We introduce UniWAM, a unified architecture that integrates a physical reasoner, a world generator, and an action predictor to jointly learn semantic understanding of the physical world, visual generation, and action prediction. To ensure the quality of the training data, we developed a rigorous data cleaning and annotation pipeline for both human egocentric data and robot data. To adapt the vision-language component to embodied tasks while preserving its inherited language capabilities, we represent low-level actions in natural language and introduce a pre-training recipe that assigns complementary supervision from visual question answering (VQA) data, human egocentric data, and robot demonstrations to the appropriate model components. During post-training, future visual noise augmentation reduces reliance on precise future predictions, while history-conditioned flow matching uses encoded action history to initialize action generation. Together, these designs significantly reduce denoising steps while maintaining performance. UniWAM achieves state-of-the-art (SOTA) performance across multiple evaluations, including in-distribution performance, robustness, generalization, instruction following, and long-horizon task execution. Furthermore, we uncover a log-linear scaling law of unified human-robot co-training, demonstrating the effectiveness of large-scale pre-training on a mixture of human and robot data.

cs.RO↗

arXiv2026

Adaptive Beam Hopping and Power Control for Dual-Layer Over-the-Air Online Federated Learning in LEO Satellite Networks

This paper investigates over-the-air (OTA) computation enabled online federated learning (FL) in low-Earth orbit (LEO) satellite networks. Specifically, we consider a dual-layer OTA aggregation architecture, where ground devices upload analog model updates to serving satellites via uplink OTA aggregation, and satellites forward the aggregated signals to a data processing center through the second round OTA aggregation. Then, we formulate a long-term data-utilization maximization problem in which devices continuously collect new data and untrained samples gradually lose freshness. The problem is subject to the satellite beam budget, transmit-power limit, and global mean squared error (MSE) constraint that governs end-to-end aggregation distortion. This yields a coupled mixed-integer nonlinear programming (MINLP) problem, involving tightly coupled discrete beam-hopping decisions and continuous power control. Due to the combinatorial action space and nonconvex constraints, the problem is NP-hard and computationally intractable. Furthermore, the time-varying satellite topology and dynamic data generation render it a sequential decision-making problem, necessitating adaptive online scheduling. To address these issues, we cast the problem as a Markov decision process and develop a proximal policy optimization (PPO)-based deep reinforcement learning framework that jointly optimizes adaptive beam hopping and power control, using an MSE-aware reward to balance data utilization and aggregation accuracy. Numerical simulation results verify that the proposed algorithm consistently outperforms other benchmark schemes, achieving superior long-term data utilization and faster FL convergence while satisfying the MSE requirement.

cs.IT↗

arXiv2026

Beamforming and Trajectory Optimization for Aerial RIS-Aided Secure ISAC via Model Predictive Control

This paper proposes a novel aerial reconfigurable intelligent surface (RIS)-aided secure integrated sensing and communication (ISAC) architecture. An unmanned aerial vehicle (UAV)-mounted RIS establishes virtual line-of-sight links to bypass blockages, serving legitimate users while sensing potential eavesdroppers. To ensure communication security and sensing reliability, we formulate an infinite-horizon dynamic control problem maximizing the long-term average system secrecy rate and minimizing UAV flight energy. This formulation jointly optimizes the UAV trajectory, active beamforming with artificial noise (AN) at the base station, and RIS phase shifts, subject to strict radar signal-to-noise ratio and UAV kinematic constraints. Due to the highly coupled non-convex variables, solving this problem directly is intractable. Therefore, we propose an online joint optimization framework based on model predictive control (MPC). Within each receding horizon, alternating optimization is employed to decouple the problem into three subproblems, efficiently solved via successive convex approximation and difference of convex programming. Extensive simulations demonstrate that our MPC-based algorithm significantly outperforms static baselines in secrecy rate, exhibiting superior online correction capability and robustness against environmental perturbations.

eess.SP↗

arXiv2026

Broadband LEO Satellite Constellations for Next-Generation Navigation: Potentials, Enabling Technologies, and Challenges

The rapid deployment of broadband low Earth orbit (LEO) constellations provides new opportunities for next-generation navigation. Compared with conventional global navigation satellite systems (GNSS), broadband LEO systems provide stronger received signals, wider bandwidths, rapidly varying satellite geometry, and larger constellation scales, offering new capabilities for positioning, navigation, and timing (PNT). Meanwhile, their communication-oriented waveforms, high dynamics, and resource-constrained architectures introduce new challenges that are fundamentally different from those in GNSS. This article provides a comprehensive overview of broadband LEO constellations for next-generation navigation, covering their fundamental characteristics, representative use cases, key enabling technologies, and open challenges. We first compare broadband LEO systems with conventional GNSS and summarize the main technical routes for LEO-based PNT. We then discussed the emerging use cases in GNSS-challenged, multipath-dominant, and integrated communication and navigation (ICAN) scenarios. Key enabling technologies including waveform design, high-dynamic signal processing, and constellation resource scheduling are further reviewed. As a representative case study, the navigation-oriented optimization of the Starlink primary synchronization sequence demonstrates that subcarrier power allocation can improve multipath resistance while maintaining ranging accuracy. Finally, several challenges and future directions of broadband LEO-based navigation are discussed, aiming to provide reference for future researches.

eess.SP↗

arXiv2026

Tight degeneracy bounds in online Ramsey games

In the $q$-color online Ramsey game, Builder and Painter play on an infinite independent set of vertices. At each step, Builder draws an edge and Painter immediately assigns it one of $q$ colors. Builder aims to force a monochromatic copy of a fixed graph $H$. We prove that, for every $q \ge 2$ and $d \ge 1$, Builder can force a monochromatic copy of any $d$-degenerate graph $H$ while drawing a graph of degeneracy at most $d$. The bound $d$ is tight, and this resolves in the affirmative a problem of Conlon, Fox and Sudakov.

math.CO↗

arXiv2026

Tensor-Based Dynamic Channel Estimation for mmWave Movable Antenna MIMO Systems

This paper investigates the dynamic channel estimation algorithm in mmWave movable antenna (MA) multiple-input multiple-output (MIMO) systems. To achieve highly accurate channel estimation, we propose a tensor decomposition-based channel estimation algorithm. First, by leveraging the path response model and utilizing the intrinsic sparsity of mmWave channels, the channel corresponding to MA pairs at the base station and mobile station is transformed into a superposition of channels from sparse paths. Next, the received signal is constructed as a fourth-order tensor to fully capture the high-dimensional structural information of the MA MIMO channel. Then, two tensor decomposition schemes are adopted to extract the factor matrices, and our analysis reveals that the uniqueness of the decomposition can be guaranteed in our model. Subsequently, the propagation loss, frequency offset, angle of arrival/departure, and time delay are obtained based on these factor matrices and the channel matrix can be rebuilt. Additionally, Cramér-Rao bound (CRB) is also derived as a performance evaluation standard, proving that the proposed algorithm achieves a higher estimation accuracy and nearly approaches this minimum bound. Moreover, normalized mean square error (NMSE) is selected as the evaluation metrics for estimation accuracy. Finally, simulation results reveal a notable reduction in the estimation error of the proposed algorithm when compared to the baseline algorithms, confirming its estimation advantage.

eess.SP↗

arXiv2026

MVG-WAM: Multiple View Geometry-Aware World-Action Modeling for Robotic Manipulation

World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. However, their multi-view interfaces typically tile images or concatenate tokens, leaving the geometric relationships among synchronized cameras implicit. This makes it harder to connect global scene context with the local geometry required for interaction. We introduce the Multi-View Geometry-Aware World-Action Model (MVG-WAM), which organizes these observations as related projections of one physical world rather than separate images on a canvas. Our model combines an epipolar-constrained global state with view-indexed geometric states jointly inferred from synchronized observations. Camera-aware routing supplies each video region with its corresponding geometric context and the shared global state, explicitly structuring the representation used for action prediction. We further ground the geometry-aware representation in metric scale through multi-horizon future-depth supervision, without requiring depth decoding during action rollout. MVG-WAM achieves average success rates of 99.1% on LIBERO and 92.07% on RoboTwin 2.0, demonstrating competitive performance across both benchmarks. Real-world experiments on Cobot Magic further demonstrate a 91.3% success rate across 150 trials spanning three manipulation tasks.

cs.RO↗

arXiv2026

Experimental and numerical study of shock wave propagation in water generated by pulsed arc electrohydraulic discharges

The objective of this study is to simulate the propagation of the shock wave in water due to an explosion. The study is part of a global research program on the development of an alternative stimulation technique to conventional hydraulic fracturing in tight gas reservoirs aimed at inducing a distributed state of microcracking of rocks instead of localized fracture. We consider the possibility of increasing the permeability of rocks with dynamic blasts. The blast is a shock wave generated in water by pulsed arc electrohydraulic discharges. The amplitude of these shock waves is prescribed by the electrohydraulic discharges which generate high pressures of several kilobars within microseconds. A simplified method has been used to simulate the injected electrical energy as augmentation of enthalpy in water locally. The finite element code EUROPLEXUS is used to perform fluid fast dynamic computation. The predicted pressure is consistent with the experimental results. In addition, shock wave propagation characteristics predicted with simulation can be valuable reference for design of underwater structural elements and engineering of underwater explosion.

physics.chem-ph↗

arXiv2026

RIS-Assisted Secure ISAC: Fundamentals, Applications, and Future Directions

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) networks, yet its broadcast nature at the physical layer leaves transmissions vulnerable to malicious eavesdropping, particularly when physical blockages degrade direct links. Reconfigurable intelligent surface (RIS) offers a promising defense paradigm at the physical layer to address these issues, by reconstructing virtual line-of-sight links and dynamically reshaping the spatial propagation environment. In this article, we provide a comprehensive overview of RIS-assisted secure ISAC systems, covering its fundamentals, practical applications, and open future research directions. We first outline cooperative deployment topologies and basic electromagnetic operating principles. We then analyze the technical advantages of passive RIS integration in mitigating bottlenecks in resource competition across communication secrecy, sensing precision, and hardware sustainability. We also present representative applications, including urban vehicular networks, monitoring with unmanned aerial vehicle support, industrial Internet-of-thing, and privacy in smart homes. A case study quantifies gains in secrecy rate and spatial nulling capabilities achieved through joint active and passive beamforming under full blockage of direct links. Finally, we identify key open challenges and future research directions to enable practical, scalable, and intelligent deployment of RIS-assisted secure ISAC systems.

eess.SP↗