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Yuanwei Liu

Publications and source records attributed to Yuanwei Liu.

At least 19 recordsLinked to original sources
arXiv2026

Exploiting Pinching Antenna Systems in NOMA Covert Communication Networks

Pinching antenna systems (PASS) provide flexible transmitter-side radiation reconfiguration for high-frequency wireless communications. This paper applies PASS to non-orthogonal multiple access (NOMA) covert communication networks, where activated pinching antennas (PAs) transmit superimposed signals to a pair of users stochastically distributed within a rectangle. Two passive wardens respectively monitor the downlink transmissions intended for two users. More specifically, we derive the closed-form detection error probability (DEP) expressions for two wardens under bounded noise uncertainty. Then we analytically determine the optimal detection thresholds and minimum DEPs in the worst-case covert detection scenarios. The covert transmission rates of two users are further characterized by taking into consideration ideal successive interference cancellation and non-ideal successive interference cancellation at near user and intra-NOMA interference at far user. Numerical results are presented to confirm that: 1) PASS-NOMA is capable of providing higher covert rates relative to conventional orthogonal multiple access OMA schemes; 2) As the number of activated PAs begun to increase, the near user has ability to obtain a larger covert rate gain than far user; and 3) The DEP of PASS-NOMA networks is larger than that of both PASS-OMA and traditional antenna based NOMA in specific output power regions.

cs.IT↗
arXiv2026

Visual Autoregressive Generation with Next Token Imagination

The space of possible visual content is enormous, and arguably unbounded. Any training set, however large, therefore covers only a tiny fraction of it. Yet generative models are typically trained to fit only the images they are given. In visual autoregressive (AR) generation, for example, each token is predicted from the preceding ground-truth (GT) tokens and supervised solely by the GT next token. Confining learning to observed data in this way limits how much of the visual distribution a model can capture. In this work, we hypothesize that letting a model imagine beyond its training data, by exploring plausible visual realizations it has never observed, can ease this limitation. We use visual AR generation as a proof of concept, since its fixed, discrete token space makes imagination easier to steer and constrain. We propose Next Token Imagination (NTI), a method that allows the model to explore beyond the GT next token, subject to a carefully designed criterion that keeps this imagination bounded by the underlying visual distribution. Extensive experiments on ImageNet show that NTI substantially and consistently improves the fidelity of generated images, approaching state-of-the-art diffusion and AR models while using a smaller model and lower training cost.

cs.CV↗
arXiv2026

Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation

The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks from bent-pipe relays toward intelligent onboard processing. This shift confronts a fundamental tension, where modern artificial intelligence (AI) training paradigms require intensive computation, abundant memory, and stable connectivity, all of which are scarce under the dynamic topologies, intermittent links, and severe size, weight, and power (SWaP) constraints of satellite networks. Addressing this tension demands rethinking AI workflows, from training and inference to the emerging deployment of large language models (LLMs), under a constraint-aware design philosophy. To the best of our knowledge, this paper provides the first survey that jointly covers all three, whereas prior surveys examine these aspects in isolation. We begin by identifying the deployment challenge of satellite intelligence (SI) and deriving design principles for SI. We then examine three advancing paradigms: i) distributed training, where federated learning (FL) and split learning (SL) enable satellite-ground co-training under resource limitations; ii) collaborative inference, which exploits single-satellite, multi-satellite, and satellite-ground architectures to meet real-time service demands; and iii) on-orbit LLM adaptation and serving, which tackles the frontier challenge of deploying LLMs under severe orbital constraints and resource-aware serving. Finally, we identify open challenges and future directions. By bridging the AI and satellite networks, this paper provides a research roadmap for the implementation of space-native AI technologies.

cs.NI↗
arXiv2026

When to Stop Penalizing Actions? Martingale GRPO for VLA Reinforcement Learning

A failed vision-language-action (VLA) rollout does not imply that every action contributed to failure. Yet in group-relative policy optimization (GRPO), a single trajectory-level advantage is assigned to all actions in a rollout. Potentially useful recovery attempts can thus be penalized alongside the errors they aim to correct. Ideal stopping conditions for failure feedback are characterized using success-probability and policy-score martingales. At these stops, the removed gradient contribution has zero conditional expectation given the retained information. Truncation therefore preserves the expectation of a reference GRPO group-gradient estimator and can reduce its covariance. One such stop occurs when the conditional probability of success is zero, motivating error and recovery cues for selecting candidate feedback boundaries. Martingale GRPO (MART-GRPO) is introduced with a vision-language model (VLM) for event-guided truncation. The VLM is fine-tuned on our annotated training set using low-rank adaptation (LoRA). The last visible error without observed recovery is localized by the frozen VLM in complete rollout videos. Failure feedback is retained through the selected error interval and removed afterward, while successful-rollout loss terms and original loss normalization are preserved. For retrospective boundaries, changes in expectation and variance are analyzed to assess departures from ideal stopping. Experiments with OpenVLA-OFT on RoboTwin 2.0 show that MART-GRPO improves success rates over full-trajectory GRPO across multiple tasks.

cs.AI↗
arXiv2026

Generative Site-Specific Beamforming for Next-Generation Spatial Intelligence

This article proposes generative site-specific beamforming (GenSSBF) for next-generation spatial intelligence in wireless networks. Site-specific beamforming (SSBF) has emerged as a promising paradigm to mitigate the channel acquisition bottleneck in multiantenna systems by exploiting environmental priors. However, classical SSBF based on discriminative deep learning struggles: 1) to properly represent the inherent multimodality of wireless propagation and 2) to effectively capture the structural features of beamformers. In contrast, by leveraging conditional generative models, GenSSBF addresses these issues via learning a conditional distribution over feasible beamformers. By doing so, the synthesis of diverse and high-fidelity beam candidates from coarse channel sensing measurements can be guaranteed. This article presents the fundamentals, system designs, and implementation methods of GenSSBF. Case studies in both indoor and outdoor scenarios show that GenSSBF attains near-optimal beamforming gain with ultra-low channel acquisition overhead. Finally, several open research problems are highlighted.

cs.IT↗
arXiv2026

Performance Analysis of Pinching Antenna Systems Enabled NOMA Communications

Pinching antenna systems (PASS) have the advantages in the perspective of flexible antenna reconfiguration, line-of-sight (LoS) creation, and scalability features. To highlight the ascendancy of PASS, we survey the integration of PASS into non-orthogonal multiple access (NOMA) networks. The locations of nodes are randomly distributed within a circular coverage region. The influencing factors of LoS and none-line-of-sight (NLoS) propagation links from PASS to non-orthogonal nodes are taken into consideration. To characterize performance of PASS-NOMA, we deduce the outage probability and ergodic data rates expressions of two nodes over LoS/NLoS fading channels. In light of these theoretical results, the formally unbounded diversity gains are also characterized with node n under ideal successive interference cancellation (ISIC) and node f over LoS links, when the SNR exceeds a finite threshold. The slopes of ergodic data rate for node n with ISIC equals to one and that of node f presents a zero slope. In addition, the PASS-NOMA system throughput are evaluated in different transmission modes. It is shown from the numerical results that: 1) The outage behaviors of PASS-NOMA networks under LoS/NLoS conditions outperform that of PASS aided orthogonal multiple access (OMA) networks and that of conventional antenna systems (CASS) aided OMA/NOMA networks; 2) The employment of PASS enables the larger ergodic data rates relative to PASS-OMA and CASS-OMA/NOMA networks; and 3) As the quantity of communication radius and PASS' height rises, the performance of PASS-NOMA networks are declined at a certain extent.

cs.IT↗
arXiv2026

Beam-STeP: Generative Site-Specific Beam Prediction with Spatio-Temporal Priors

A generative site-specific beam prediction (Beam-STeP) method is proposed. Spatial priors from site-specific propagation and mobility information are jointly exploited with temporal priors from historical beam observations for beam prediction. To address the beam prediction ambiguity from multimodal spatio-temporal priors, the generative model is adopted, where a history-conditioned diffusion model is proposed to learn the multimodal conditional distribution of future beamforming vectors. Multiple beam candidates are sampled from the distribution using a denoising diffusion implicit model (DDIM). The candidate with the highest received power is then selected for transmission. Simulation results across three urban scenarios demonstrate that: i) Beam-STeP generally achieves higher normalized beamforming gains than the baselines under the same probing budget, particularly as beam prediction progresses; ii) site-specific propagation and mobility priors improve recursive beam prediction performance; and iii) DDIM sampling enables a flexible gain-latency trade-off.

eess.SP↗
arXiv2026

Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins

Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation, but constructing and maintaining uniformly high-fidelity WDTs for large-scale urban environments is costly. This paper studies task-oriented nonuniform WDT refinement (TONR) by addressing the following question: given a limited sensing and reconstruction budget, which building geometries in the initial WDT should be refined to best preserve wireless fidelity? A resource-constrained building selection problem is formulated to minimize the expected discrepancy between the wireless response of the refined WDT and the physical environment. Through local first-order approximation, the refinement value of each building is shown to depend jointly on its correctable geometry uncertainty and the sensitivity of the task response to that uncertainty. This value is then estimated solely from the initial low-fidelity WDT using physically interpretable geometry perturbations and central finite differences. To reduce the computational cost, a propagation-relevance ellipsoid is proposed to filter out buildings unlikely to contribute significantly to the wireless propagation. The resulting knapsack problem is solved under both equal and heterogeneous refinement costs. Simulations across multiple urban scenarios show that the proposed algorithm can substantially improve wireless fidelity by refining only a small subset of buildings.

eess.SP↗
arXiv2026

Exploiting Vulnerabilities: Universal Adversarial Attacks on Vision-Language-Action Models in Robotics

Recently, Vision-Language-Action (VLA) models have revolutionized robotic manipulation by seamlessly integrating visual perception, language understanding, and action generation in an end-to-end learning framework. However, since these models are designed to interact directly with the physical world and humans, their security is critical, and even small vulnerabilities can lead to catastrophic failures. In this work, we propose the Universal Adversarial Object, a sphere with optimized surface texture that significantly degrades task success rates when placed within the robot's field of view. Specifically, our approach introduces a multi-level attack framework that jointly disrupts trajectory planning, task execution, and action control. We validate our method in both simulated and real-world robotic settings. Experimental results demonstrate that the adversarial object reduces the average task success rates by 31.2%-39.9% for two representative VLA models (Pi0 and RDT), with success rates dropping to near zero in complex scenarios. Index Terms--Vision-Language-Action models, adversarial attack, robotic security, universal adversarial object

cs.RO↗
arXiv2026

Movable-Element STARS-Assisted NOMA Communications

This paper proposes a movable-element (ME) enabled simultaneously transmitting and reflecting surface (ME-STARS)-assisted non-orthogonal multiple access (NOMA) communication system, where the positions of STARS elements can be flexibly reconfigured to introduce additional spatial degrees-of-freedom. Our objective is to maximize the sum achievable rate by jointly optimizing the active beamforming at the base station (BS) as well as the passive beamforming and MEs positions at the STARS. To tackle the resultant highly-coupled non-convex optimization problem, an alternating optimization-based algorithm is proposed, in which the original problem is decomposed into three subproblems. For the BS and ME-STARS beamforming subproblems, the successive convex approximation method is employed. For the MEs positions optimization, penalty terms are incorporated into the objective function to handle the constraints, thereby transforming the original problem into an unconstrained formulation. The gradient descent algorithm is subsequently applied to iteratively approach the sub-optimal solution. Numerical results demonstrate that: i) ME-STARS-assisted NOMA communications achieve superior performance compared to the NOMA communications assisted by STARS with fixed-position elements; and ii) the performance gain brought by the MEs is significant even with restricted moving region size.

eess.SP↗
arXiv2026

Agents in the Scene: An Agentic Framework for Resource-Efficient Site-Specific Base Station Deployment

An agentic framework is proposed for autonomous site-specific base station (BS) deployment in wireless network planning. In contrast to conventional approaches that rely on manual site surveys or extensive ray-tracing (RT) simulations with significant human intervention, the proposed framework autonomously explores and optimizes BS deployment under a limited RT evaluation budget, enabling resource-efficient network planning. To this end, a continuous, geometry-grounded deployment action space is first constructed from three-dimensional (3D) wireless digital twins. Within this action space, an agent team operates through a stateful perception--reasoning--reflection loop. Specifically, a Placement Agent first generates candidate BS deployments in two complementary modes: an experience-guided mode that refines promising solutions, and an exploration mode that avoids getting stuck in local optima. After the candidate deployments are evaluated through RT, a Reflection Agent interprets the RT results together with the scene geometry, identifies performance-limiting factors such as blockage, overlapping coverage, and uncovered areas, and converts these diagnoses into guidance for subsequent deployment. Through this iterative process, site-specific experience is accumulated and deployment plans are optimized without human intervention. Numerical results in two realistic urban scenarios show that: 1) the proposed approach substantially outperforms heuristic, learning-based, and large language model (LLM)-assisted methods; 2) it achieves highly competitive coverage against the optimal solution while requiring substantially fewer transmitter-level RT evaluations; 3) site-specific reflection effectively turns raw RT feedback into refinement guidance, whereas the dual-mode mechanism preserves diversity and facilitates escape from local optima.

eess.SY↗
arXiv2026

Discrete Coupling and Localized Motion for Pinching-Antenna Systems (PASS)

The practical implementation of pinching-antenna systems (PASS) is challenging due to hardware limitations in large-scale antenna movement and continuous radiation power adjustment. This paper proposes a practical PASS-enabled downlink multi-user multiple-input multiple-output communication framework that enables discrete radiation power control and localized discrete antenna movement. Specifically, a discrete coupling strength model is exploited to tune the radiation power at each pinching antenna (PA) through quantized coupling spacing levels. Moreover, each PA can only move among discrete locations within a limited region determined by the movement speed and duration. Based on the proposed framework, a joint optimization problem of the PA positions, coupling strength, and transmit beamforming is formulated. Considering waveguide attenuation, the total average power consumption is minimized, subject to each user's minimum SINR requirement and localized motion constraints. To address this coupled mixed-integer nonconvex optimization problem, a globally optimal branch-and-bound-based algorithm is first developed for the multi-waveguide single-user scenario. To further reduce complexity, a scalable genetic algorithm-assisted particle swarm optimization (GA-PSO) method is developed for the multi-waveguide multi-user scenario, where GA operations are incorporated to preserve population diversity and alleviate premature convergence. Simulation results demonstrate that the proposed design significantly reduces the power consumption compared with the conventional PASS schemes and MIMO architectures.

eess.SP↗
arXiv2026

Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC

Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.

eess.SP↗
arXiv2026

Fast Beam-Brainstorm: Few-Step Generative Site-Specific Beamforming with Flexible Probing

A novel generative site-specific beamforming (GenSSBF) approach, termed fast beam-brainstorm (F-BBS), is proposed to address the practical bottlenecks of slow beam generation and fixed channel probing lengths in existing GenSSBF. To accelerate beam generation, F-BBS utilizes a two-stage distillation strategy that learns an average velocity field, instead of an instantaneous one, to guide the beam generative process. This strategy enables larger generation steps, realizing few-step or even one-step beam generation. Furthermore, to accommodate flexible channel probing lengths, a stochastic masking mechanism and a beam index-aware masked condition encoder are proposed, enabling a single trained model to operate with variable-length channel probing observations without retraining. Therefore, FBBS achieves the fast generation of high-fidelity communication beams from coarse and variable-length channel probing feedback, i.e., reference signal received power (RSRP), from user equipments. Simulation results on accurate ray-tracing datasets show that 1) F-BBS achieves comparable performance while reducing the beam generation cost by over 90% compared with diffusion-based GenSSBF solutions, 2) F-BBS realizes robust performance across variable channel probing length, and 3) FBBS offers a desirable trade-off between beamforming gain and beam probing overhead.

eess.SP↗
arXiv2026

KDGen-BF: A Generative Site-Specific Multi-User Beamforming Approach

This paper proposes knowledge-distilled generative beamforming (KDGen-BF) framework for site-specific multi-user beamforming. KDGen-BF generates a multi-user beamforming weights from low-dimensional reference signal received power (RSRP) observations without acquiring instantaneous channel state information (CSI). To address the ambiguity caused by limited RSRP observations and interference coupling, KDGen-BF formulates multi-user beamforming as a conditional generation problem and directly outputs beamforming weights beyond a finite codebook. A diffusion transformer is trained through knowledge-distillation and exponential-moving-average (KD-EMA) guidance, and multi-candidate strategy is used for online deployment. Numerical results on multiple DeepMIMO scenarios demonstrate that: 1) under limited probing budgets, KDGen-BF outperforms all baselines; 2) with larger probing budgets, KDGen-BF achieves performance comparable to exhaustive search over the discrete Fourier transform (DFT) codebook and outperforms all other baselines; and 3) under noisy RSRP observations, KDGen-BF remains robust and outperforms all compared baselines.

eess.SP↗
arXiv2026

Joint Communication and Control Beamforming: A Closed-Loop Control Perspective

A joint communication and control (JCC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users (CUs) and controls a physical plant in a closed loop. In the downlink, BS-generated control inputs are transmitted to and recovered at the plant, with wireless actuation distortion incorporated into the plant-state evolution. In the uplink, the plant state is reported to the BS and tracked by a Kalman filter (KF) for subsequent control-input generation. To characterize long-term control performance under communication-control interference, finite- and infinite-horizon linear quadratic Gaussian (LQG) costs are derived, directly linking beamforming design to plant-state evolution. JCC beamforming problems are then formulated for vector- and scalar-valued control inputs to minimize the infinite-horizon LQG cost subject to per-user communication signal-to-interference-plus-noise ratio (SINR) requirements. For the vector case, a second-order cone programming (SOCP)-based successive convex approximation method is developed for the resulting nonconvex problem. For the scalar case, a closed-form infinite-horizon LQG cost is derived, and the communication-control Pareto boundary is optimally characterized by an SOCP-based bisection method. Its optimality follows from the strict monotonicity of the scalar control cost with respect to the control SINR. Numerical results show that the derived costs closely match Monte Carlo simulations, the KF accurately tracks the ground-truth plant-state trajectory, and the proposed methods consistently outperform the zero-forcing benchmark. This confirms the benefit of balancing communication-control interference, especially with limited spatial degrees of freedom (DoFs).

eess.SP↗
arXiv2026

A Survey of Pinching-Antenna Systems (PASS)

The pinching-antenna system (PASS), recently proposed as a flexible-antenna technology, has been regarded as a promising solution for several challenges in next-generation wireless networks. It provides large-scale antenna reconfiguration, establishes stable line-of-sight links, mitigates signal blockage, and exploits near-field advantages through its distinctive architecture. This article aims to present a comprehensive overview of the state of the art in PASS. The fundamental principles of PASS are first discussed, including its hardware architecture, circuit and physical models, and signal models. Several emerging PASS designs, such as segmented waveguide-enabled PASS (SWAN), center-fed PASS (C-PASS), and multi-mode PASS (M-PASS), are subsequently introduced, and their design features are discussed. In addition, the properties and promising applications of PASS for wireless sensing are reviewed. On this basis, recent progress in the performance analysis of PASS for both communications and sensing is surveyed, and the performance gains achieved by PASS are highlighted. Existing research contributions in optimization and machine learning are also summarized, with the practical challenges of beamforming and resource allocation being identified in relation to the unique transmission structure and propagation characteristics of PASS. Finally, several variants of PASS are presented, and key implementation challenges that remain open for future study are discussed.

eess.SP↗
arXiv2026

Continuous-Aperture Array-Based ISAC Over Fading Channels

A framework of continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) under a fading communication channel is proposed. A continuous operator-based signal model is developed, and the statistics of the communication channel gain are characterized via Landau's eigenvalue theorem. On this basis, the performance of the CAPA-based ISAC system is analyzed by considering three continuous beamforming designs: i) the sensing-centric (S-C) design that optimizes sensing performance, ii) the communication-centric (C-C) design that optimizes communication performance, and iii) the Pareto-optimal design that balances the sensing-communication trade-off. For the S-C and C-C design, closed-form expressions for the sensing rate (SR), ergodic communication rate (CR), and outage probability are derived, and high-signal-to-noise ratio asymptotic analysis is conducted to obtain the multiplexing and diversity gains. For the Pareto-optimal design, the Pareto-optimal beamformer achieving the Pareto boundary is derived, and the achievable SR-CR region is characterized. Numerical results demonstrate that the proposed CAPA-ISAC scheme outperforms both conventional spatially discrete arrays-based ISAC and CAPA-based frequency-division sensing and communications.

eess.SP↗