arXiv subjects

Yunfei Chen

Publications and source records attributed to Yunfei Chen.

At least 19 recordsLinked to original sources

arXiv2026

Channel Map-Based Channel Estimation for Near-Field UM-MIMO with Movable Planar Arrays

Accurate channel estimation is essential for coherent transmission in ultra-massive multiple-input multiple-output (UM-MIMO) systems, where near-field propagation and high-dimensional spatial channels impose substantial signal processing challenges. Movable antenna architectures increase the estimation complexity further due to geometry-dependent channel variations. Existing approaches struggle to balance accuracy and complexity, motivating the use of environment-dependent propagation structures for efficient estimation. To this end, this paper proposes a unified channel map-based channel estimation framework for UM-MIMO systems, which integrates movable planar array reconfiguration and near-field spherical-wave modeling to support geometry-aware line-of-sight (LoS) estimation and efficient non-LoS (NLoS) recovery. A channel map-based LoS estimator is proposed combining coarse user position information with a Fisher information-guided antenna placement strategy. Two efficient NLoS estimation methods are also presented, including a sketch-based reduced-subspace estimator for low-complexity processing and a channel map-based estimator that leverages scatterer location information for near-optimal performance. The framework further incorporates visibility-region modeling and a structural similarity-based pilot assignment strategy for multi-user scenarios. Simulation results show that the proposed channel map-based framework improves estimation accuracy, reduces computational overhead, and enhances scalability compared with state-of-the-art benchmarks without channel maps.

eess.SP↗

arXiv2026

Intelligent Disruption: Undetectable Attacks on Wireless Autoencoders

Adversarial attacks can degrade the legitimate decision performance in wireless autoencoder communications. However, in complex scenarios with multiple adversaries, the cumulative leakage interference (CLI) caused by the multiple parallel attacks increases the chance of detecting the attacks, while dynamical environments also make the fixed attack strategies difficult to have stable effectiveness. To jointly enhance the undetectability, aggressivity and adaptability of adversarial attacks, we propose a deep learning based intelligent attack framework. Specifically, considering the CLI caused by the multiple parallel attacks, a deep neural network based transmit power control is established to reduce the interference leakage by regulating the transmit power of these adversaries, thereby improving the undetectability. Furthermore, to enhance the attack effectiveness and stability in the dynamic environment, the conditional generative adversarial attack is further developed. The generator takes the attack channel information as the conditional input to produce the perturbating signals to mislead the discriminator by making the attacked received signals resemble the clean received signals, while the discriminator distinguishes between the two under the same condition. Through the adversarial training, the generator can learn to create adaptive perturbating signals with enhanced attack performance. Simulation results demonstrate that the proposed framework outperforms benchmarks in terms of attack undetectability, aggressivity and adaptability.

cs.CR↗

arXiv2026

Deep Reinforcement Learning-Empowered Wireless Sensor Networking for 6G Closed-Loop Controls

Robots are increasingly deployed in remote or hazardous areas for mission-critical control tasks. Due to their limited individual capabilities, they have to rely on other field sensors to obtain the state information of targets, and also a dedicated edge information hub (EIH) to enable information exchange, sensing data analysis and control command generation. Such configuration follows a sensing-communication-computing-control (SC3) closed loop. To optimize the whole closed-loop performance, this paper minimizes the linear quadratic regulator (LQR) control cost by designing the sensor-to-EIH bandwidth allocation. Specifically, we first model the distortion noise caused by limited communication data rate based on the mutual information theory. Next, under the control policy based on the Kalman filter and LQR controller, we formulate the control process as a partially observable Markov decision process (POMDP), and develop a deep reinforcement learning (DRL)-based sensor-to-EIH bandwidth allocation scheme. The proximal policy optimization (PPO) algorithm is utilized to train the DRL agent. Simulation results are provided to show the superiority of the proposed DRL-based scheme.

cs.IT↗

arXiv2026

Orchestrating Communication, Computing, and Energy Transfer for Wireless-Powered 6G Closed-Loop Controls

Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computing-control (SC3) closed loops. However, the limited battery capacities of devices within these SC3 loops constrain operational duration and degrade control efficiency, particularly in remote or post-disaster scenarios. To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC3 closed loops. In this paper, we investigate a wireless-powered SC3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC3 closed loops. By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance. Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem. The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized. We recast the problem into a more tractable form and develop an iterative algorithm to solve it. For the special case of a single loop, we further analyze the properties of optimal solutions in energy-limited scenarios to provide insights for practical parameter configuration. Simulation results demonstrate the performance gains of the proposed scheme.

cs.IT↗

arXiv2026

TriPAH: Imbalance-Aware Tri-Prompt Affinity Hashing for Cross-Modal Medical Retrieval

In the era of big medical data, efficient cross-modal retrieval is pivotal for evidence-based diagnosis and large-scale case management. Cross-modal medical hashing retrieval aims to enable efficient image-text search and support downstream tasks such as case-based reasoning and decision support by learning compact, semantically aligned binary codes. However, current methods suffer from semantic fragmentation due to noisy clinical language, long-tailed labels, and brittle quantization that weakens alignment. We propose TriPAH, a Tri-Prompt Affinity Hashing framework. TriPAH synthesizes ontology-grounded, patient-level prompts conditioned on normalized clinical cues to yield low-noise textual representations for initial alignment. A lightweight prompt-token mixer performs hierarchical, multi-granularity alignment and produces quantization-ready features under an asymmetric multi-task objective coupling multi-positive contrastive alignment, imbalance-aware classification, and progressive quantization regularization. A patient-level consistency module further stabilizes codes across complementary views. Extensive experiments on three public datasets demonstrate that TriPAH significantly outperforms state-of-the-art methods.

cs.IR↗

arXiv2026

Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks

In hazardous environments, sensors and actuators can be deployed to see and operate on behalf of humans, enabling safe and efficient task execution. Functioning as a neural center, the edge information hub (EIH), which integrates communication and computing capabilities, coordinates these sensors and actuators into sensing-communication-computing-control (SC3) closed loops to enable autonomous operations. From a system-level optimization perspective, this paper addresses the problem of joint sensor-actuator pairing and resource allocation across multiple SC3 closed loops. To tackle the resulting mixed-integer nonlinear programming problem, we develop a learning-optimization-integrated actor-critic (LOAC) framework. In this framework, a deep neural network-based actor generates pairing candidates, while an optimization-based critic subsequently allocates communication and computing resources. The actor is then iteratively refined through feedback from the critic. Simulation results demonstrate that the LOAC framework achieves near-optimal solutions with low computational complexity, offering significant performance gains in reducing control cost.

cs.IT↗

arXiv2026

Latency-Constrained Resource Synergization for Mission-Oriented 6G Non-Terrestrial Networks

This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

cs.IT↗

arXiv2026

Shared Sky, Shared Spectrum: Coordinated Satellite-5G Networks for Low-Altitude Economy

Driven by both technological development and practical demands, the low-altitude economy relying on low-altitude aircrafts (LAAs) is booming. However, neither satellites nor terrestrial fifth-generation (5G) networks alone can effectively satisfy the communication requirements for ubiquitous lowaltitude coverage. While full integration of satellites and 5G networks offers theoretical benefits, the associated overhead and complexity pose significant challenges for rapid deployment. As a more economical and immediately viable alternative, this paper investigates partially-integrated networks where satellites and 5G systems operate with coarse synchronization yet achieve coordinated spectrum sharing, pooling their capabilities to jointly serve LAAs. Leveraging the inherent position-awareness of LAAs, we propose a framework for joint time-frequency spectrum sharing with an adaptive synchronization time scale, where only large-scale channel state information (CSI) is required. To avoid solving the NP-hard optimization problem directly, link-feature-aided clustering is employed following a divide-andconquer strategy. The proposed framework achieves substantial performance gains with low overhead and complexity, enabling swift advancement of low-altitude applications while paving the way for future integrated satellite-terrestrial network evolution.

cs.IT↗

arXiv2026

Physical Layer Security for Sensing-Communication-Computing-Control Closed Loop: A Systematic Security Perspective

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3 closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3 closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

cs.IT↗

arXiv2026

Task-Oriented Direct-to-Cell Satellite Communications for 6G Closed-Loop Autonomous Operations

Direct-to-cell (D2C) satellite communications have emerged as a crucial alternative to terrestrial communications in the sixth generation (6G) mobile networks due to their wide-area coverage capability. Unlike human-oriented communications, future 6G robot-oriented D2C satellite communications in autonomous operations place greater emphasis on the ultimate task completion than on the intermediate stage of data transmissions. Such a difference renders it crucial to evaluate the performance of each stage in a systematic manner and consider a multistage integrated optimization. Motivated by this, we model the system with a sensing-communication-computing-control (SC3) closed loop and analyze it from an entropy-based perspective, from which a task-oriented system design method is developed. Furthermore, to manage the complexity of the closed-loop network, we decompose it into fine-grained functional structures and investigate the key challenges of collaborative sensing, collaborative computing, and collaborative control. A case study is presented to compare the proposed task-oriented scheme with conventional communication-oriented schemes, showing that the proposed method has better performance in system-level control cost. Finally, several open issues are outlined for future research and practical implementation.

eess.SP↗

arXiv2026

Exposure-Aware Beamforming for mmWave Systems: From EM Theory to Thermal Compliance

Electromagnetic (EM) exposure compliance has long been recognized as a crucial aspect of communications terminal designs. However, accurately assessing the impact of EM exposure for proper design strategies remains challenging. In this paper, we develop a long-term thermal EM exposure constraint model and propose a novel adaptive exposure-aware beamforming design for an mmWave uplink system. Specifically, we first establish an equivalent channel model based on Maxwell's radiation equations, which accurately captures the EM physical effects. Then, we derive a closed-form thermal impulse response model from the Pennes bioheat transfer equation (BHTE), characterizing the thermal inertia of tissue. Inspired by this model, we formulate a beamforming optimization problem that translates rigid instantaneous exposure limits into a flexible long-term thermal budget constraint. Furthermore, we develop a low-complexity online beamforming algorithm based on Lyapunov optimization theory, obtaining a closed-form near-optimal solution. Simulation results demonstrate that the proposed algorithm effectively stabilizes tissue temperature near a predefined safety threshold and significantly outperforms the conventional scheme with instantaneous exposure constraints.

eess.SP↗

arXiv2026

Time-Scale-Adaptable Spectrum Sharing for Hybrid Satellite-Terrestrial Networks

Cooperation between satellite and terrestrial wireless networks promises great potential in meeting fast-growing demands for ubiquitous communications coverage. To tackle spectrum scarcity, spectrum sharing is studied for a hybrid satellite-terrestrial network where satellite links share the same group of time-slotted sub-carriers with terrestrial links opportunistically. In particular, with coarse network-wide time synchronization, a time-scale-adaptable spectrum sharing framework is proposed based on a satellite-terrestrial cooperation time scale that can be flexibly adjusted according to practical requirements. For generality, it is assumed that both full and partial frequency reuse could be adopted among the base stations (BSs) and satellite selection is supported when multiple satellites are available. Relying on only statistical channel state information (CSI), joint link scheduling and power control are explored to maximize the average sum rate of the network while ensuring quality of service (QoS) for users. To solve the complicated mixed integer programming (MIP) problem, a low-complexity spectrum sharing scheme is presented based on link-feature-sketching-aided hierarchical link clustering and Monte-Carlo-and-successive-approximation-aided transmit power optimization. Simulation results demonstrate that by link feature sketching, diversity of the links brought by the spatial distribution of the users could be well utilized. The proposed scheme promises a significant performance gain even under strict inter-link interference constraints.

cs.IT↗

arXiv2026

Interference-governed electromagnetic-thermal coupling and heat transport in pulse EUV-irradiated multilayer nanofilms

Mo-Si multilayer mirrors are central to extreme ultraviolet lithography, where nanoscale optical interference and heat accumulation together constrain reflectivity and operational stability. Here we develop an analytical electromagnetic-thermal coupling model that directly links transfer-matrix-based interference-controlled energy deposition with transient heat conduction in EUV-irradiated multilayers. The model reveals a fundamental trade-off whereby increasing the multilayer period number enhances reflectivity but simultaneously elevates temperature by impeding heat dissipation. Interference-driven volumetric absorption further gives rise to pronounced axial temperature gradients and a post-pulse downward migration of the heat-flux maximum, a delayed-heating effect inaccessible to conventional surface-flux-based models. Systematic analysis establishes scaling laws connecting interfacial thermal resistance, beam size, and incident energy density to thermal confinement and temperature rise. By incorporating interfacial compaction kinetics, the model enables a quantitative assessment of mirror lifetime. This work offers a theoretical tool for thermal-optical co-design of multilayer nanostructures including EUV mirrors under pulsed irradiation across a wide spectral range.

physics.app-ph↗

arXiv2026

A microscopic origin for the breakdown of the Stokes Einstein relation in ion transport

Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing anomaly is that smaller alkali metal ions, such as Li$^+$, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This breakdown is conventionally attributed to dielectric friction, a collective drag force arising from electrostatic interactions between a drifting ion and its surrounding solvent. Here, combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic force on a migrating ion is not a drag force but a net driving force. By contrasting charged ions with neutral particles, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and represent additional channels for energy dissipation, strongest for Li$^+$ and progressively weaker for Na$^+$ and K$^+$. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.

cond-mat.soft↗

arXiv2025

The Triple-C Paradigm: Cooperative, Complementary, and Competitive Modes for TBS-HAPS-LEO Integration

The growing demands of ubiquitous and resilient global coverage have pushed existing networks to their operational limits, making it increasingly difficult to meet all requirements on their own. Integrating \emph{Terrestrial Base Stations (TBS), High Altitude Platform Stations (HAPS)} and \emph{Low-Earth-Orbit (LEO)} satellites is envisioned as a promising solution, yet the coordination across these heterogeneous platforms remains an open challenge. This paper proposes a novel unifying \emph{Triple-C framework: Cooperation, Complementarity, and Competition}, that systematically defines the TBS-HAPS-LEO interaction to deliver seamless resilient and scalable connectivity. For each C, we detail the architectural methodology, required pre-requisites, and measurable deliverables that govern when and how the three layers should collaborate, complement each other, or contend. We further identify the enabling technologies across physical, logical, and cognitive layers to operationalize the proposed 3C paradigm. A rich portfolio of use cases and targeted applications demonstrates how this technological leap will make such integration both feasible and impactful. Comprehensive performance analysis and emulation results quantify the trade-offs of such integrated networks. In addition, we examine the economical, environmental, safety, privacy, standardization, and regulatory implications that shape the real-world implementation of the proposed framework. eventually, we provide the gap analysis, outline key technical/non-technical challenges, and a road-map of future research directions needed to unlock the full potential of Cooperation, Complementarity, and Competition operations in TBS-HAPS-LEO integrated networks.

eess.SP↗

arXiv2025

Telling stories, making Hanzi: AI-assisted co-creation with elderly migrants in urban China

This paper explores how older migrants in urban China can record stories that everyday language and design often miss. We ran two co-creation workshops with 10 elders. Activities combined oral storytelling, facilitator-mediated AI assistance, and hand-making. Large language models proposed candidate glyphs through a facilitator. Participants crafted new Hanzi to hold their stories. The resulting characters served as memory anchors for later sharing and retelling. Our interpretive analysis shows heterogeneity and adaptive capacity among participants. Participants experienced AI as a creative initiator that lowered barriers to expression and making, especially for those with lower digital literacy. The work challenges homogenizing assumptions about older adults and the presumption of uniform capacities and needs. We contribute a workshop framework that positions AI as a backstage facilitator. We also offer insights on engaging older migrants as sources of community memory and situated cultural knowledge within inclusive urban systems.

cs.HC↗

arXiv2025

On Path to Multimodal Historical Reasoning: HistBench and HistAgent

Recent advances in large language models (LLMs) have led to remarkable progress across domains, yet their capabilities in the humanities, particularly history, remain underexplored. Historical reasoning poses unique challenges for AI, involving multimodal source interpretation, temporal inference, and cross-linguistic analysis. While general-purpose agents perform well on many existing benchmarks, they lack the domain-specific expertise required to engage with historical materials and questions. To address this gap, we introduce HistBench, a new benchmark of 414 high-quality questions designed to evaluate AI's capacity for historical reasoning and authored by more than 40 expert contributors. The tasks span a wide range of historical problems-from factual retrieval based on primary sources to interpretive analysis of manuscripts and images, to interdisciplinary challenges involving archaeology, linguistics, or cultural history. Furthermore, the benchmark dataset spans 29 ancient and modern languages and covers a wide range of historical periods and world regions. Finding the poor performance of LLMs and other agents on HistBench, we further present HistAgent, a history-specific agent equipped with carefully designed tools for OCR, translation, archival search, and image understanding in History. On HistBench, HistAgent based on GPT-4o achieves an accuracy of 27.54% pass@1 and 36.47% pass@2, significantly outperforming LLMs with online search and generalist agents, including GPT-4o (18.60%), DeepSeek-R1(14.49%) and Open Deep Research-smolagents(20.29% pass@1 and 25.12% pass@2). These results highlight the limitations of existing LLMs and generalist agents and demonstrate the advantages of HistAgent for historical reasoning.

cs.AI↗

arXiv2025

Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication

Cooperative perception, leveraging shared information from multiple vehicles via vehicle-to-vehicle (V2V) communication, plays a vital role in autonomous driving to alleviate the limitation of single-vehicle perception. Existing works have explored the effects of V2V communication impairments on perception precision, but they lack generalization to different levels of impairments. In this work, we propose a joint weighting and denoising framework, Coop-WD, to enhance cooperative perception subject to V2V channel impairments. In this framework, the self-supervised contrastive model and the conditional diffusion probabilistic model are adopted hierarchically for vehicle-level and pixel-level feature enhancement. An efficient variant model, Coop-WD-eco, is proposed to selectively deactivate denoising to reduce processing overhead. Rician fading, non-stationarity, and time-varying distortion are considered. Simulation results demonstrate that the proposed Coop-WD outperforms conventional benchmarks in all types of channels. Qualitative analysis with visual examples further proves the superiority of our proposed method. The proposed Coop-WD-eco achieves up to 50% reduction in computational cost under severe distortion while maintaining comparable accuracy as channel conditions improve.

cs.CV↗