arXiv subjects

Kun Yang

Publications and source records attributed to Kun Yang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Anisotropic Photostriction and Strain-modulated Carrier Lifetimes in Orthorhombic Semiconductors

We demonstrate anisotropic photostriction in two-dimensional orthorhombic semiconductors using time-dependent density functional theory. By tracing the dynamics of photoexcited carriers, we establish a quantitative link between carrier density and lattice deformation in layered black phosphorus and germanium selenides. The structural response exhibits significant anisotropy, featuring lattice expansion along the armchair direction and contraction along the zigzag direction, which is attributed to the interplay between charge redistribution and intrinsic lattice anisotropy. Both the magnitude and orientation of the photostrictive strains can be tuned by photodoping densities, enabling precise control over the photoinduced response. Notably, the photoinduced strains significantly increase carrier recombination lifetimes by suppressing nonradiative recombination, primarily due to the enlarged bandgap and weakened nonadiabatic coupling. These results provide microscopic insight into the origin of anisotropic photostriction in low-dimensional systems and lay the groundwork for light-controllable, directionally sensitive optomechanical devices at the atomic scale.

cond-mat.mtrl-sci↗

arXiv2025

Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks

Quasi-periodic MicroPulses (QMP) are quasi-periodic microstructural features manifested in individual pulsar radio pulses, the study of which is crucial for understanding pulsar radiation mechanisms. Manual identification of QMP in large-scale pulsar single-pulse datasets remains highly inefficient. To address this, we propose a Dual-Stage Residual Network (DSR) that achieves automated QMP detection in FAST observational data through joint analysis of single-pulse profiles and their Amplitude Distribution Profiles (ADP), defined as the power spectra of the autocorrelation function derivatives of the microstructure residuals. The model was trained on PSR B1933+16 data from 2019 (10,486 single pulses) and evaluated on manually annotated PSR B1933+16 data from 2020 (9,657 single pulses). DSR achieved 96.10\% recall and 95.85\% precision on the test set. This approach provides an automated pipeline for large-scale, reproducible QMP identification and establishes the foundation for in-depth investigation of their physical mechanisms.

astro-ph.HE↗

arXiv2025

MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement

Recent advances demonstrate that reinforcement learning with verifiable rewards (RLVR) significantly enhances the reasoning capabilities of large language models (LLMs). However, standard RLVR faces challenges with reward sparsity, where zero rewards from consistently incorrect candidate answers provide no learning signal, particularly in challenging tasks. To address this, we propose Multi-Expert Mutual Learning GRPO (MEML-GRPO), an innovative framework that utilizes diverse expert prompts as system prompts to generate a broader range of responses, substantially increasing the likelihood of identifying correct solutions. Additionally, we introduce an inter-expert mutual learning mechanism that facilitates knowledge sharing and transfer among experts, further boosting the model's performance through RLVR. Extensive experiments across multiple reasoning benchmarks show that MEML-GRPO delivers significant improvements, achieving an average performance gain of 4.89% with Qwen and 11.33% with Llama, effectively overcoming the core limitations of traditional RLVR methods.

cs.AI↗

arXiv2025

An End-to-End Neural Network Transceiver Design for OFDM System with FPGA-Accelerated Implementation

The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers rely on cascaded discrete Fourier transform (DFT) and demodulation blocks, which are prone to inter-stage error propagation and suboptimal global performance. In this work, we propose two neural network (NN) models DFT-Net and Demodulation-Net (Demod-Net) to jointly replace the IDFT/DFT and demodulation modules in an OFDM transceiver. The models are trained end-to-end (E2E) to minimize bit error rate (BER) while preserving operator equivalence for hybrid deployment. A customized DFT-Demodulation Net Accelerator (DDNA) is further developed to efficiently map the proposed networks onto field-programmable gate array (FPGA) platforms. Leveraging fine-grained pipelining and block matrix operations, DDNA achieves high throughput and flexibility under stringent latency constraints. Experimental results show that the DL-based transceiver consistently outperforms the conventional OFDM system across multiple modulation schemes. With only a modest increase in hardware resource usage, it achieves approximately 1.5 dB BER gain and up to 66\% lower execution time.

eess.SY↗

arXiv2025

Multi-Subarray FD-RIS Enhanced Multi-user Wireless Networks: With Joint Distance-Angle Beamforming

The concept of the frequency diverse reconfigurable intelligent surface (FD-RIS) technology has been introduced, which can enable simultaneous implementation of distance-angle beamforming in far-field communication scenarios. In order to improve the managing ability on undesired harmonic signals and the diversity of frequency offsets, this paper presents a novel multi-subarray FD-RIS framework. In this framework, the RIS is evenly divided into multiple subarrays, each employing a distinct time-modulation frequency to enable the diversity of frequency offsets. Additionally, to suppress the undesired harmonic signals, a new time-modulation technique is employed to periodically adjust the phase-shift of each element. Based on the proposed multi-subarray FD-RIS, the signal processing model is first analytically derived. To evaluate the effectiveness of the proposed multi-subarray FD-RIS, we integrate it into a multi-user communication scenario and formulate an optimization problem that aims to maximize the weighted sum rate of all users. This is achieved by jointly optimizing the active beamforming, time delays, and modulation frequencies. Subsequently, a novel iterative algorithm is proposed to effectively solve this problem with low computing complexity. To evaluate the beamforming capability of the proposed multi-subarray FD-RIS,three communication scenarios with varying spatial correlations among users are considered. Simulation results demonstrate that the proposed multi-subarray FD-RIS can significantly enhance and sustain the performance of communication networks by leveraging unique distance-angle beamforming, even when users share the same angular position where traditional RIS experiences severe degradation.

cs.IT↗

arXiv2025

SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks

Efficient information exchange and reliable contextual reasoning are essential for vehicle-to-everything (V2X) networks. Conventional communication schemes often incur significant transmission overhead and latency, while existing trajectory prediction models generally lack environmental perception and logical inference capabilities. This paper presents a trajectory prediction framework that integrates semantic communication with Agentic AI to enhance predictive performance in vehicular environments. In vehicle-to-infrastructure (V2I) communication, a feature-extraction agent at the Roadside Unit (RSU) derives compact representations from historical vehicle trajectories, followed by semantic reasoning performed by a semantic-analysis agent. The RSU then transmits both feature representations and semantic insights to the target vehicle via semantic communication, enabling the vehicle to predict future trajectories by combining received semantics with its own historical data. In vehicle-to-vehicle (V2V) communication, each vehicle performs local feature extraction and semantic analysis while receiving predicted trajectories from neighboring vehicles, and jointly utilizes this information for its own trajectory prediction. Extensive experiments across diverse communication conditions demonstrate that the proposed method significantly outperforms baseline schemes, achieving up to a 47.5% improvement in prediction accuracy under low signal-to-noise ratio (SNR) conditions.

cs.AI↗

arXiv2025

Frequency Diverse (FD)-RIS-Enhanced Covert Communications: Defense Against Wiretapping via Joint Distance-Angle Beamforming

In response to the security blind zone challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user's achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

cs.IT↗

arXiv2025

Adversarially robust clustering with optimality guarantees

We consider the problem of clustering data points coming from sub-Gaussian mixtures. Existing methods that provably achieve the optimal mislabeling error, such as the Lloyd algorithm, are usually vulnerable to outliers. In contrast, clustering methods seemingly robust to adversarial perturbations are not known to satisfy the optimal statistical guarantees. We propose a simple robust algorithm based on the coordinatewise median that obtains the optimal mislabeling rate even when we allow adversarial outliers to be present. Our algorithm achieves the optimal error rate in constant iterations when a weak initialization condition is satisfied. In the absence of outliers, in fixed dimensions, our theoretical guarantees are similar to that of the Lloyd algorithm. Extensive experiments on various simulated and public datasets are conducted to support the theoretical guarantees of our method.

math.ST↗

arXiv2025

6D Movable Holographic Surface Assisted Integrated Data and Energy Transfer: A Sensing Enhanced Approach

Reconfigurable holographic surface (RHS) enables cost-effective large-scale arrays with high spatial gain. However, its amplitude-controlled holographic beamforming suffers from directional fluctuations, making it difficult to fully exploit the spatial gain of RHS. Fortunately, the promising 6D movable antenna (6DMA) provides a potential solution to this problem. In this paper, we study a 6D movable holographic surface (6DMHS) integrated data and energy transfer (IDET) system, where a three-stage protocol is proposed, consisting of an uplink sensing stage, an orientation adjustment stage and a downlink transmission stage, to coordinate the 6DMHS and effectively serve the IDET receivers. Firstly, the holographic-based sensing technology is proposed and the sensing information of the IDET receivers is exploited. Secondly, by fixing the rotations with the sensing information, the orientation optimization problem is formulated for designing the holographic beamforming of the RHS and adjusting the translations of the 6DMHS. As a result, the directions with maximum beamforming gain are aligned with each IDET receiver. Thirdly, by fixing the orientation of the 6DMHS and the holographic beamforming, the equivalent wireless channel is obtained. The IDET performance optimization problem is formulated for obtaining the optimal digital beamforming, power splitting factor and energy harvesting (EH) power. Simulation results demonstrate that the proposed scheme is capable of improving the IDET performance compared to the benchmarks.

eess.SP↗

arXiv2025

Circular Holographic MIMO Beamforming for Integrated Data and Energy Multicast Systems

Thanks to the application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain with lower hardware complexity. With the aid of a circular antenna arrangement of H-MIMO, integrated data and energy multicast (IDEM) can fully exploit the near-field channel to realize wider range of energy focusing and higher achievable rate. In this paper, we derive the closed-form near-field resolution function in 3D space and show the asymptotic spatial orthogonality of near-field channel for circular antenna array. We then investigate the beamforming designs for IDEM systems, where the minimum rate of data users (DUs) are maximized while guaranteeing the energy harvesting requirements for energy users (EUs). Specifically, the asymptotically optimal fully-digital beamformer is first obtained based on the spatial orthogonality. Then, the alternating optimization is adopted for the H-MIMO beamforming, where the digital beamformer is obtained in closed form and the analog beamformers of three different control modes are then obtained, respectively. Scaling schemes are also investigated to further improve the IDEM performance. Numerical results verify the correctness of the resolution function and asymptotic orthogonality. Moreover, the proposed beamforming schemes with very low complexity outperform benchmark schemes.

cs.IT↗

arXiv2025

UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models

Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks. Existing safety techniques -- including external guardrails, inference-time guidance, and post-training alignment -- each face limitations in balancing safety, utility, and controllability. In this work, we propose UpSafe$^\circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling. Our approach first identifies safety-critical layers and upcycles them into a sparse Mixture-of-Experts (MoE) structure, where the router acts as a soft guardrail that selectively activates original MLPs and added safety experts. We further introduce a two-stage SFT strategy to strengthen safety discrimination while preserving general capabilities. To enable flexible control at inference time, we introduce a safety temperature mechanism, allowing dynamic adjustment of the trade-off between safety and utility. Experiments across multiple benchmarks, base model, and model scales demonstrate that UpSafe$^\circ$C achieves robust safety improvements against harmful and jailbreak inputs, while maintaining competitive performance on general tasks. Moreover, analysis shows that safety temperature provides fine-grained inference-time control that achieves the Pareto-optimal frontier between utility and safety. Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.

cs.AI↗

arXiv2025

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network

In the upcoming 6G networks, integrated sensing and communications (ISAC) will be able to provide a performance boost in both perception and wireless connectivity. This paper considers a multiple base station (BS) architecture to support the comprehensive services of data transmission and multi-target sensing. In this context, a cooperative BS assignment and resource allocation (CBARA) strategy is proposed in this paper, aiming at jointly optimizing the communication and sensing (C&S) performance. The posterior Cramer-Rao lower bound and the achievable rate with respect to transmit power and bandwidth are derived and utilized as optimization criteria for the CBARA scheme. We develop a heuristic alternating optimization algorithm to obtain an effective sub-optimal solution for the non-convex optimization problem caused by multiple coupled variables. Numerical results show the effectiveness of the proposed solution, which achieves a performance improvement of 117% in communication rate and 40% in sensing accuracy, compared to the classic scheme.

cs.IT↗

arXiv2025

The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity

Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path's information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust "stable belts" and high-risk "dangerous wedges". Finally, an analysis of the landscape's curvature yields actionable, topology-aware design rules, including an optimal "Threshold-First" tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks.

cs.IT↗

arXiv2025

Low-Complexity Null-Space-Based Simultaneous Wireless Information and Power Transfer Scheme

Simultaneous wireless information and power transfer (SWIPT) has attracted sustained interest. We propose a null-space-based transmission scheme for multiuser SWIPT serving both energy users (EUs) and information users (IUs). Under a practical nonlinear energy-harvesting (EH) model and multiple waveform options, we revisit the role of dedicated energy beams (EBs). We show that, in general, dedicated EBs are unnecessary because information beams (IBs) with Gaussian signaling can simultaneously support wireless energy transfer (WET) and wireless information transfer (WIT), unless special energy-centric waveforms (e.g., deterministic sinusoidal waveforms) are employed and provide sufficient gains. Guided by these insights, we formulate an optimization problem for EB design to enable dedicated waveform transmission for WET, and we develop a low-complexity algorithm that reduces computation by ignoring the WET contribution of IBs during optimization. Numerical results corroborate that deterministic sinusoidal waveforms outperform Gaussian signaling when the received RF power lies in the EH high-efficiency region, making dedicated EBs beneficial. The proposed scheme achieves computational complexity reductions of 91.43\% and 98.54\% for the cases $M=8,,K^I=K^E=2$ and $M=16,,K^I=K^E=4$, respectively, with negligible performance loss, thereby validating the efficiency of the low-complexity algorithm.

eess.SP↗

arXiv2025

Performance Analysis of Pinching-Antenna-Enabled Internet of Things Systems

The pinching-antenna systems (PASS), which activate small dielectric particles along a dielectric waveguide, has recently emerged as a promising paradigm for flexible antenna deployment in next-generation wireless communication networks. While most existing studies assume rectangular indoor layouts with full coverage waveguide, practical deployments may involve geometric constraints, partial coverage, and non-negligible waveguide attenuation. This paper presents the first analytical investigation of PASS in a circular indoor environment, encompassing both full coverage and partial coverage waveguide configurations with/without propagation loss. A unified geometric-propagation framework is developed that jointly captures pinching-antenna placement, Internet of Things (IoT) device location distribution, and waveguide attenuation. Closed-form expressions for the outage probability and average achievable rate are derived for four scenarios, with accuracy validated via extensive Monte-Carlo simulations. The analysis reveals that, under the partial coverage waveguide scenario with propagation loss, the system performance demonstrates a non-monotonic trend with respect to the waveguide length, and the optimal length decreases as the attenuation coefficient increases. Numerical results further quantify the interplay between deployment strategy, waveguide propagation loss, and coverage geometry, offering practical guidelines for performance-oriented PASS design.

eess.SY↗

arXiv2025

On the Performance Analysis of Pinching-Antenna-Enabled SWIPT Systems

In this paper, we studies the performance of a novel simultaneous wireless information and power transfer (SWIPT) system enabled by a flexible pinching-antenna. To support flexible deployment and optimize energy-rate performance, we propose three practical pinching antenna placement-schemes: the edge deployment scheme (EDS), the center deployment scheme (CDS), and the diagonal deployment scheme (DDS). Moreover, a hybrid time-switching (TS) and power-splitting (PS) protocol is introduced, allowing dynamic adjustment between energy harvesting and information decoding. Under each deployment strategy and the transmission protocol, closed-form expressions for the average harvested energy and average achievable rate of a randomly located user equipment (UE) are derived based on the optimal positioning of the pinching-antenna. Numerical simulations confirm the accuracy of the theoretical analysis and illustrate the trade-off between rate and energy harvesting under different schemes.

eess.SY↗

arXiv2025

Algorithm Design and Prototype Validation for Reconfigurable Intelligent Sensing Surface: Forward-Only Transmission

Sensing-assisted communication schemes have recently garnered significant research attention. In this work, we design a dual-function reconfigurable intelligent surface (RIS), integrating both active and passive elements, referred to as the reconfigurable intelligent sensing surface (RISS), to enhance communication. By leveraging sensing results from the active elements, we propose communication enhancement and robust interference suppression schemes for both near-field and far-field models, implemented through the passive elements. These schemes remove the need for base station (BS) feedback for RISS control, simplifying the communication process by replacing traditional channel state information (CSI) feedback with real-time sensing from the active elements. The proposed schemes are theoretically analyzed and then validated using software-defined radio (SDR). Experimental results demonstrate the effectiveness of the sensing algorithms in real-world scenarios, such as direction of arrival (DOA) estimation and radio frequency (RF) identification recognition. Moreover, the RISS-assisted communication system shows strong performance in communication enhancement and interference suppression, particularly in near-field models.

eess.SP↗

arXiv2025

Profiling Multi-Level Operator Costs for Bottleneck Diagnosis in High-Speed Data Planes

This paper proposes a saturation throughput delta-based methodology to precisely measure operator costs in high-speed data planes without intrusive instrumentation. The approach captures non-linear scaling, revealing that compute-intensive operators like CRC exhibit super-linear behavior, while most others are sub-linear. We introduce the Operator Performance Quadrant (OPQ) framework to classify operators by base and scaling costs, exposing a cross-architecture Quadrant Shift between Arm and x86. This method provides accurate, architecture-aware bottleneck diagnosis and a realistic basis for performance modeling and optimization.

eess.SP↗