arXiv subjects

Ran Yang

Publications and source records attributed to Ran Yang.

At least 19 recordsLinked to original sources

arXiv2026

Robust Secure Beamforming for Movable Antenna Enhanced Integrated Sensing and Communications

In this letter, we investigate robust beamforming design for a movable antenna (MA)-enhanced secure integrated sensing and communications (ISAC) system with imperfect eaves?dropping channel state information (CSI). To improve radar sensing performance, we formulate a radar signal-to-interference?plus-noise ratio (SINR) maximization problem by jointly opti?mizing the transmit beamforming and antenna placement while ensuring communication data security. However, the resulting op?timization problem is inherently intractable due to the nonlinea mapping from antenna positions to channel coefficients, as well as the eavesdropper (Eve) channel uncertainty. To handle these challenges, we propose a block coordinate descent (BCD)-based algorithm incorporating successive convex approximation (SCA) and fractional programming (FP) techniques. Simulation results show that our proposed algorithm exhibits fast convergence and achieves a significant improvement in the radar SINR while guaranteeing communication security.

eess.SP↗

arXiv2026

ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement

Orientation-aware manipulation is essential in post-harvest agricultural processing, where produce must be grasped and placed in consistent configurations. This paper presents ROG-Grasp, a geometry-based robotic grasping and placement framework that estimates the produce orientation from root surface geometry using RGB-D perception. A YOLO-based root detector and point cloud plane fitting are used to infer the root normal, enabling stable grasp pose generation and orientation-constrained Cartesian motion planning. Experiments on tomatoes and onions demonstrate high success rates and stable execution time in both isolated and cluttered scenarios. Compared with vision-language-action (VLA) policies, the proposed method achieves more reliable and accurate grasp completion with faster execution. These results highlight the effectiveness of geometry-driven perception for practical orientation-controlled manipulation tasks. A video of our paper is available online https://youtu.be/Ir2UtGODdMo.

cs.RO↗

arXiv2026

Movable Antenna Enhanced Dual-Functional Radar-Communication: A Symbol-Level Precoding Approach

This letter investigates a symbol-level precoder design for movable antenna (MA)-enhanced dual-functional radar-communication (DFRC) systems. To enhance radar sensing capabilities, we formulate an optimization problem aimed at maximizing the minimum radar signal-to-interference-plus-noise ratio (SINR) across multiple targets in a cluttered environment. Our approach jointly designs the space-time transmitted waveforms, receiving filters, and antenna placement. However, the resulting problem is intractable to solve due to practical waveform constraints and the non-linear mapping from antenna positions to the corresponding channel coefficients. To address these challenges, we develop a bi-level optimization framework by leveraging deep reinforcement learning (DRL). Specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm is employed in the outer layer to optimize antenna placement, while penalty convex-concave procedure (CCP) and majorization-minimization (MM) techniques are incorporated in the inner layer for regularizing waveform design. Simulation results demonstrate that the proposed method significantly improves radar SINR and achieves a superior sensing-communication trade-off compared to benchmark schemes.

eess.SP↗

arXiv2026

Integrated photon-pair sources on periodically poled thin-film lithium tantalate

Chip-integrated photon-pair sources based on spontaneous parametric down-conversion (SPDC) have emerged as a promising solution for scalable quantum light generation. Thin-film lithium tantalate (TFLT) is a compelling $\chi^{(2)}$ platform, combining strong nonlinearity with a high optical-damage threshold, weak photorefractive response, and ferroelectricity that enables quasi-phase matching. However, SPDC-based photon-pair generation on TFLT has not yet been demonstrated. Here, we combine high-quality periodic poling with low-loss nanophotonic waveguides to realize photon-pair sources on TFLT in both traveling-wave and resonant configurations. In periodically poled straight waveguides, we achieve broadband photon-pair generation with high efficiency ($2.1~\mathrm{GHz}~\mathrm{mW}^{-1}$) and coincidence-to-accidental ratio (up to $3.8\times10^{5}$). We further confirm high-purity single-photon operation via heralded second-order correlation ($g^{(2)}_\mathrm{H}(0) = 0.0018 \pm 0.0002$) and high-fidelity time-energy entanglement through Franson interference (visibility of $98.9 \pm 0.5\%$). In periodically poled racetrack resonators, we map out a broad quantum frequency comb spanning the telecom C- and L-bands. By isolating individual frequency-correlated pairs, we measure a high spectral brightness of $11~\mathrm{GHz}~\mathrm{mW}^{-1}~\mathrm{GHz}^{-1}$. These results are competitive with the state of the art across $\chi^{(2)}$ integrated platforms, positioning TFLT as a strong contender for integrated quantum light sources, with applications in wavelength-multiplexed quantum communications and photonic quantum information processing.

physics.optics↗

arXiv2026

VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning and deployment on accessible hardware. The system integrates a Fairino FR5 collaborative arm, a Jodell RG52-50 electric gripper, and a dual-camera perception module, unified through a ZMQ-based communication architecture that seamlessly coordinates teleoperation, data collection, and policy deployment within a single framework. To enable safe manipulation of fragile objects without relying on explicit force sensing, we design a kirigami-based soft compliant gripper extension that induces predictable deformation under compressive loading, providing gentle and repeatable contact with delicate targets. We deploy and evaluate three state-of-the-art VLA models on the VILAS platform: pi_0, pi_0.5, and GR00T N1.6. All models are fine-tuned from publicly released pretrained checkpoints using an identical demonstration dataset collected via our teleoperation pipeline. Experiments on a grape grasping task validate the effectiveness of the proposed system, confirming that capable manipulation policies can be successfully trained and deployed on low-cost modular hardware. Our results further provide practical insights into the deployment characteristics of current VLA models in real-world settings.

cs.RO↗

arXiv2026

Flexible Cylindrical Array-Aided Secure Wireless Communications

Flexible-geometry arrays based on movable antennas have shown considerable potential for improving wireless communication performance. In this letter, we investigate a multiuser multiple-input single-output (MU-MISO) downlink secure communication system aided by a flexible cylindrical array (FCLA) and artificial noise (AN), where each antenna element rotates along circular tracks while the circular slices move along a vertical axis. To guarantee transmission security, we aim to maximize the achievable sum rate at multiple legitimate information receivers by jointly optimizing transmit beamforming, AN covariance matrix, and antenna placement under secrecy constraints for an eavesdropper. While the resulting problem is intractable to solve, we develop a block coordinate descent (BCD)-based framework that combines the Lagrangian dual transform, tight semidefinite relaxation (SDR), and Nesterov-accelerated projected gradient descent (PGD). Numerical results show that the proposed algorithm converges rapidly and achieves significant sum-rate gains over benchmark schemes by exploiting the geometry flexibility of the array.

eess.SP↗

arXiv2026

Quantum photonic frequency processor on thin-film lithium niobate

The rapid development of photonic quantum information processing necessitates precise and programmable control over optical frequency, a capability critical not only for achieving photon indistinguishability but also for exploiting a virtually unbounded frequency dimension. However, efficient and scalable processing of frequency-encoded photon states remains challenging, primarily due to the limited nonlinear optical interaction in most photonic materials. Here, by harnessing the high-performance thin-film lithium niobate electro-optic (EO) platform, we demonstrate an integrated quantum photonic frequency processor that enables coherent and programmable control of photon frequency with high precision. We establish a scalable architecture for frequency-encoded quantum information processing. Using a fully integrated photonic chip, we realize a universal set of frequency-encoded quantum logic gates, including arbitrary single-qubit rotation gates and the two-qubit controlled-phase gate. Furthermore, we demonstrate its application in high fidelity characterization of frequency-bin entangled states. Our work reveals the unprecedented potential of utilizing the frequency degree of freedom in integrated quantum photonic systems.

quant-ph↗

arXiv2026

Movable Antenna Empowered Covert Dual-Functional Radar-Communication

Movable antenna (MA) has emerged as a promising technology to flexibly reconfigure wireless channels by adjusting antenna placement. In this paper, we study a secured dual-functional radar-communication (DFRC) system aided by movable antennas. To enhance the communication security, we aim to maximize the achievable sum rate by jointly optimizing the transmitter beamforming vectors, receiving filter, and antenna placement, subject to radar signal-to-noise ratio (SINR) and transmission covertness constraints. We consider multiple Willies operating in both non-colluding and colluding modes. For noncolluding Willies, we first employ a Lagrangian dual transformation procedure to reformulate the challenging optimization problem into a more tractable form. Subsequently, we develop an efficient block coordinate descent (BCD) algorithm that integrates semidefinite relaxation (SDR), projected gradient descent (PGD), Dinkelbach transformation, and successive convex approximation (SCA) techniques to tackle the resulting problem. For colluding Willies, we first derive the minimum detection error probability (DEP) by characterizing the optimal detection statistic, which is proven to follow the generalized Erlang distribution. Then, we develop a minimum mean square error (MMSE)-based algorithm to address the colluding detection problem. We further provide a comprehensive complexity analysis on the unified design framework. Simulation results demonstrate that the proposed method can significantly improve the covert sum rate, and achieve a superior balance between communication and radar performance compared with existing benchmark schemes.

eess.SP↗

arXiv2026

On-Chip Generation of Co-Polarized and Spectrally Separable Photon Pairs

On-chip generation of high-purity single photons is essential for scalable photonic quantum technologies. Spontaneous parametric down-conversion (SPDC) is widely used to generate photon pairs for heralded single-photon sources, but intrinsic spectral correlations of the pairs often limit the purity and interference visibility of the heralded photons. Existing approaches to suppress these correlations rely on narrowband spectral filtering, which introduces loss, or exploiting different polarizations, which complicates on-chip integration. Here, we demonstrate a new strategy for generating spectrally separable photon pairs in thin-film lithium niobate nanophotonic circuits by harnessing higher-order spatial modes, with all interacting fields residing in the same polarization. Spectral separability is achieved by engineering group-velocity matching using higher-order transverse-electric modes, combined with a Gaussian-apodized poling profile to further suppress residual correlations inherent to standard periodic poling. Subsequent on-chip mode conversion with efficiency exceeding 95\% maps the higher-order mode to the fundamental mode and routes the photons into distinct output channels. The resulting heralded photons exhibit spectral purities exceeding 94\% inferred from joint-spectral intensity and 89\% from unheralded $g^{(2)}$ measurement. This approach enables flexible spectral and temporal engineering of on-chip quantum light sources for quantum computing and quantum networking.

quant-ph↗

arXiv2026

Movable Antenna Enhanced MIMO Communications with Spatial Modulation

Movable antenna (MA) has demonstrated great potential in enhancing wireless communication performance. In this paper, we investigate an MA-enabled multiple-input multiple-output (MIMO) communication system with spatial modulation (SM), which improves communication performance by utilizing flexible MA placement while reducing the cost of RF chains. To this end, we propose a joint transceiver design framework aimed at minimizing the bit error rate (BER) based on the maximum minimum distance (MMD) criterion. To address the intractable problem, we develop an efficient iterative algorithm based on alternating optimization (AO) and successive convex approximation (SCA) techniques. Simulation results demonstrate that the proposed algorithm achieves rapid convergence performance and significantly outperforms the existing benchmark schemes.

eess.SP↗

arXiv2025

Integrated polarization-entangled photon source for wavelength-multiplexed quantum networks

Entangled photons are fundamental resources for quantum communication, computing, and networking. Among them, polarization-entangled photon pairs play an important role due to their straightforward state manipulation and direct use in quantum key distribution, teleportation, and network protocols. However, realizing compact, efficient, and scalable polarization-entangled sources that meet the requirements of practical deployment remains a major challenge. Here, we present a simple yet high-performance on-chip polarization-entangled photon-pair source on thin-film lithium niobate (TFLN). Our device employs dual quasi-phase matching (D-QPM) that sequentially supports type-0 and type-I spontaneous parametric down-conversion in a single nanophotonic waveguide, eliminating the need for interferometers, polarization rotators, or other complex circuits. The source directly produces high-fidelity Bell states with broad bandwidth, high brightness, and low noise. Using this integrated platform, we realize wavelength-multiplexed entanglement distribution in a four-user quantum network deployed over metropolitan fiber links up to 50 km. These results establish a robust and scalable pathway toward practical quantum communication systems and multi-user quantum mesh networks based on integrated photonics.

physics.optics↗

arXiv2025

Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution

Discrete Wavelet Transform (DWT) has been widely explored to enhance the performance of image superresolution (SR). Despite some DWT-based methods improving SR by capturing fine-grained frequency signals, most existing approaches neglect the interrelations among multiscale frequency sub-bands, resulting in inconsistencies and unnatural artifacts in the reconstructed images. To address this challenge, we propose a Diffusion Transformer model based on image Wavelet spectra for SR (DTWSR). DTWSR incorporates the superiority of diffusion models and transformers to capture the interrelations among multiscale frequency sub-bands, leading to a more consistence and realistic SR image. Specifically, we use a Multi-level Discrete Wavelet Transform to decompose images into wavelet spectra. A pyramid tokenization method is proposed which embeds the spectra into a sequence of tokens for transformer model, facilitating to capture features from both spatial and frequency domain. A dual-decoder is designed elaborately to handle the distinct variances in low-frequency and high-frequency sub-bands, without omitting their alignment in image generation. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method, with high performance on both perception quality and fidelity.

cs.CV↗

arXiv2025

Movable Antenna Enhanced Covert Dual-Functional Radar-Communication: Joint Beamforming and Antenna Position Optimization

Movable antenna (MA) has emerged as a promising technology to flexibly reconfigure wireless channels by adjusting antenna placement. In this paper, we study a secured dual-functional radar-communication (DFRC) system enhanced by movable antennas. To ensure communication security, we aim to maximize the achievable sum rate by jointly optimizing the transmit beamforming vectors, receiving filter, and antenna placement, subject to radar signal-to-noise ratio (SNR) and transmission covertness constraints. To tackle this challenging optimization problem, we first employ a Lagrangian dual transformation process to reformulate it into a more tractable form. Subsequently, the problem is solved by employing a block coordinate descent (BCD) procedure, incorporating semidefinite relaxation (SDR), projected gradient descent (PGD), and successive convex approximation (SCA) techniques. Simulation results demonstrate that the proposed method can significantly improve the covert sum rate, and achieve a satisfactory balance between the communication and radar performance compared with existing benchmark schemes by leveraging the flexibility of movable antennas.

eess.SP↗

arXiv2025

SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model

Long-horizon robotic manipulation tasks require executing multiple interdependent subtasks in strict sequence, where errors in detecting subtask completion can cascade into downstream failures. Existing Vision-Language-Action (VLA) models such as $\pi_0$ excel at continuous low-level control but lack an internal signal for identifying when a subtask has finished, making them brittle in sequential settings. We propose SeqVLA, a completion-aware extension of $\pi_0$ that augments the base architecture with a lightweight detection head perceiving whether the current subtask is complete. This dual-head design enables SeqVLA not only to generate manipulation actions but also to autonomously trigger transitions between subtasks. We investigate four finetuning strategies that vary in how the action and detection heads are optimized (joint vs. sequential finetuning) and how pretrained knowledge is preserved (full finetuning vs. frozen backbone). Experiments are performed on two multi-stage tasks: salad packing with seven distinct subtasks and candy packing with four distinct subtasks. Results show that SeqVLA significantly outperforms the baseline $\pi_0$ and other strong baselines in overall success rate. In particular, joint finetuning with an unfrozen backbone yields the most decisive and statistically reliable completion predictions, eliminating sequence-related failures and enabling robust long-horizon execution. Our results highlight the importance of coupling action generation with subtask-aware detection for scalable sequential manipulation.

cs.RO↗

arXiv2025

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping

Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natural language instructions into visuomotor actions. However, current VLAs often struggle to satisfy precise task constraints, such as stopping based on numeric thresholds, since their observation-to-action mappings are implicitly shaped by training data and lack explicit mechanisms for condition monitoring. In this work, we propose CLAW (CLIP-Language-Action for Weight), a framework that decouples condition evaluation from action generation. CLAW leverages a fine-tuned CLIP model as a lightweight prompt generator, which continuously monitors the digital readout of a scale and produces discrete directives based on task-specific weight thresholds. These prompts are then consumed by $\pi_0$, a flow-based VLA policy, which integrates the prompts with multi-view camera observations to produce continuous robot actions. This design enables CLAW to combine symbolic weight reasoning with high-frequency visuomotor control. We validate CLAW on three experimental setups: single-object grasping and mixed-object tasks requiring dual-arm manipulation. Across all conditions, CLAW reliably executes weight-aware behaviors and outperforms both raw-$\pi_0$ and fine-tuned $\pi_0$ models. A video of our paper is available online https://youtu.be/MuMYj2QgReI.

cs.RO↗

arXiv2025

Robust Optimization for Movable Antenna-aided Cell-Free ISAC with Time Synchronization Errors

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising technology for future 6G networks. However, to fully capitalize on the potential of CF-ISAC, accurate time synchronization (TS) between access points (APs) is critical. Due to the limitations of current synchronization technologies, TS errors have become a significant challenge in the development of the CF-ISAC system. In this paper, we propose a novel CF-ISAC architecture based on movable antennas (MAs), which exploits spatial diversity to enhance communication rates, maintain sensing accuracy, and reduce the impact of TS errors. We formulate a worst-case sensing accuracy optimization problem for TS errors to address this challenge, deriving the worst-case Cram\'er-Rao lower bound (CRLB). Subsequently, we develop a joint optimization framework for AP beamforming and MA positions to satisfy communication rate constraints while improving sensing accuracy. A robust optimization framework is designed for the highly complex and non-convex problem. Specifically, we employ manifold optimization (MO) to solve the worst-case sensing accuracy optimization problem. Then, we propose an MA-enabled meta-reinforcement learning (MA-MetaRL) to design optimization variables while satisfying constraints on MA positions, communication rate, and transmit power, thereby improving sensing accuracy. The simulation results demonstrate that the proposed robust optimization algorithm significantly improves the accuracy of the detection and is strong against TS errors. Moreover, compared to conventional fixed position antenna (FPA) technologies, the proposed MA-aided CF-ISAC architecture achieves higher system capacity, thus validating its effectiveness.

eess.SP↗

arXiv2025

Distributed Distortion-Aware Robust Optimization for Movable Antenna-aided Cell-Free ISAC Systems

The cell-free integrated sensing and communication (CF-ISAC) architecture is a promising enabler for 6G, offering spectrum efficiency and ubiquitous coverage. However, real deployments suffer from hardware impairments, especially nonlinear distortion from power amplifiers (PAs), which degrades both communication and sensing. To address this, we propose a movable antenna (MA)-aided CF-ISAC system that mitigates distortion and enhances robustness. The PAs nonlinearities are modeled by a third-order memoryless polynomial, where the third-order distortion coefficients (3RDCs) vary across access points (APs) due to hardware differences, aging, and environmental conditions. We design a distributed distortion-aware worst-case robust optimization framework that explicitly incorporates uncertainty in 3RDCs. First, we analyze the worst-case impact of PA distortion on both the Cramer-Rao lower bound (CRLB) and communication rate. Then, to address the resulting non-convexity, we apply successive convex approximation (SCA) for estimating the 3RDCs. With these, we jointly optimize beamforming and MA positions under transmit power and sensing constraints. To efficiently solve this highly non-convex problem, we develop an MA-enabled self-attention convolutional graph neural network (SACGNN) algorithm. Simulations demonstrate that our method substantially enhances the communication-sensing trade-off under distortion and outperforms fixed-position antenna baselines in terms of robustness and capacity, thereby highlighting the advantages of MA-aided CF-ISAC systems.

eess.SP↗

arXiv2025

Squeezed Light Generation in Periodically Poled Thin-Film Lithium Niobate Waveguides

Squeezed states of light play a key role in quantum-enhanced sensing and continuous-variable quantum information processing. Realizing integrated squeezed light sources is crucial for developing compact and scalable photonic quantum systems. In this work, we demonstrate on-chip broadband vacuum squeezing at telecommunication wavelengths on the thin-film lithium niobate (TFLN) platform. Our device integrates periodically poled lithium niobate (PPLN) nanophotonic waveguides with low-loss edge couplers, comprising bilayer inverse tapers and an SU-8 polymer waveguide. This configuration achieves a fiber-to-chip coupling loss of 1.4 dB and a total homodyne detection loss of 4 dB, enabling a measured squeezing level of 1.4 dB. Additional measurements in a more efficient PPLN waveguide (without low-loss couplers) infer an on-chip squeezing level of over 10 dB at a pump power of 62 mW. These results underscore the potential of TFLN platform for efficient and scalable squeezed light generation.

physics.optics↗