arXiv subjects

Xianghao Yu

Publications and source records attributed to Xianghao Yu.

At least 19 recordsLinked to original sources

arXiv2026

Active Learning for Low-Altitude Radio Map Construction via Plug-and-Play Flow Matching

The deployment of unmanned aerial vehicles (UAVs) in low-altitude airspace requires accurate and timely radio maps for reliable communication and safe navigation. However, constructing such radio maps is challenging due to the prohibitive overhead of exhaustive measurements and the limited flight endurance of UAVs. To address this challenge, we propose an active learning framework based on flow matching for efficient low-altitude radio map construction from sparse measurements. We first analyze a plug-and-play (PnP) inference scheme with a flow-matching prior. By characterizing the late-stage refinement behavior through an ordinary differential equation (ODE), we theoretically show how the inference steps smoothly align with a continuous ODE flow to refine the map details. Recognizing that the early generative stages are largely noise-dominated, this insight motivates our proposed truncated flow matching plug-and-play (TFM-PnP) approach. TFM-PnP utilizes a spatial interpolation-based initialization to start the reconstruction from an intermediate flow time, thereby bypassing the inefficient early stages. We further use the generative diversity of flow matching to derive an uncertainty map to guide the UAV trajectory design. Specifically, we propose a weighted sampling approach to select a target location, followed by a Utility-Aware Path Search (UAPS) algorithm to design the corresponding UAV trajectories. Simulation results based on Sionna ray-tracing datasets show that the proposed framework outperforms the considered baselines, achieving more than 50% reduction in normalized mean squared error (NMSE).

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arXiv2026

A Gram-Attention Learning Framework for Spatially Non-Stationary Channel Estimation

As next-generation wireless systems migrate to high-frequency bands, extremely large-scale multiple-input multiple-output (XL-MIMO) is indispensable for combating severe path loss. However, the received path energy along the extremely large array aperture (ELAA) may exhibit spatial non-stationarity, rendering the conventional discrete Fourier transform (DFT) codebook inadequate for channel estimation (CE) due to its full-array angular representation. To address this mismatch, we propose a novel joint angle-subarray (JAS) codebook, where each codeword corresponds to an angular direction and a specific subarray. Spatially non-stationary CE is thus transformed into structured sparse support detection in the JAS domain. However, reliable support recovery remains challenging for conventional model-based methods. We therefore develop a signal processing and deep learning co-design framework, where data-driven JAS parallel support detection is followed by least-squares (LS)-based channel reconstruction. The matched filter response of each JAS grid is embedded into a high-dimensional feature vector. We then propose a Gram-attention module that incorporates the Gram matrix as a physical prior to guide feature purification, enabling the network to suppress leakage-related components while preserving support-relevant information of each grid. The purified features are passed to decoupled heads to infer the hierarchical sparse supports in the JAS domain. Finally, LS is performed over the low-dimensional dictionary constructed from the detected supports to retrieve path gains. Simulation results demonstrate that the proposed framework achieves the highest support detection F1-score and the lowest CE normalized mean square error (NMSE) among all baselines, while ablation studies verify the benefits of integrating the Gram-domain physical prior with data-driven representation learning.

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arXiv2026

SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services

Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely on hardware profiling, configuration enumeration, or heuristic scheduling, offering limited analytical guidance for cost-efficient resource allocation. In this paper, we propose SARA, a Service level objectives (SLOs)-Aware Resource Allocation framework for disaggregated agentic LLM serving systems, which maximizes goodput under a deployment cost constraint and a series of quantile-based SLO constraints. By capitalizing on queuing theory, we first model the prefill, KV cache transfer, and decode stages as an M/G/k queue, an M/G/1 queue, and a generalized birth-death process, respectively. The analysis reveals that the prefill and decode stages are dominantly limited by computational capacity and high-bandwidth memory (HBM) resources, respectively. With these mathematical models, we further derive tractable tail behaviors of different stage-wise service level metrics for both light- and heavy-tailed workloads. These characterizations explicitly map workload, model architecture, and hardware parameters to stage-wise SLO constraints and minimum resource requirements. Finally, we develop an effective resource allocation framework to maximize system goodput under limited cost budgets. Simulation and hardware results demonstrate that the proposed framework accurately predicts the stage-wise SLO with mean errors below 5%, and improves system goodput by 26.6% on average over state-of-the-art baseline methods under the same deployment cost.

cs.PF↗

arXiv2026

Antenna Placement for Monostatic Near-Field Wireless Sensing

The emergence of movable antenna technology enables flexible antenna placement, allowing transceiver antennas to more effectively exploit spatial degrees of freedom. In this paper, we investigate a monostatic near-field sensing system, aiming to minimize the worst-case squared position error bound (SPEB) over the entire near-field region by jointly optimizing the transmit and receive antenna placements, along with the transmit power allocation. Toward this end, we first derive the closed-form expression of SPEB by calculating the Cramér-Rao bound (CRB) for estimating the target's angle and distance. It is shown that the derived SPEB is governed by the power-weighted moments of the transmit antenna locations and the moments of the receive antenna locations, motivating the development of a moment-based optimization algorithm. Functional analysis of the SPEB's moment structure proves that the optimal antenna distributions can be realized by a centro-symmetric structure, and shows that the worst-case target is located at the array broadside on the Rayleigh boundary. Moreover, by leveraging moment-based analysis and the Richter-Tchakaloff theorem, we derive closed-form antenna distributions with three points supported at the aperture center and two edges. Specifically, the optimal transmit antenna distribution can be exactly realized by activating only three transmit antennas while the optimal receive antenna distribution consists of three clusters. Numerical results show that the proposed closed-form design significantly outperforms conventional antenna placements and power allocation benchmark schemes.

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arXiv2026

Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness

Building on advances in reconfigurable antenna techniques, movable antennas (MAs) can dynamically reshape antenna arrays and introduce additional spatial degrees of freedom (DoFs), thereby further improving communication performance. Despite these benefits, existing MA design algorithms often entail prohibitively high computational complexity from discrete positioning selection, which prevents practical implementations of MAs. In this paper, we investigate efficient solutions for the mutual information (MI) maximization problem of a multi-user multiple-input multiple-output (MU-MIMO) uplink communication system aided by discrete MAs. To this end, we first formulate the discrete MA positioning problem with the assumption of perfect channel state information (CSI). Then, we prove that the design problem falls into the category of monotone submodular maximization subject to a 2-system constraint. Accordingly, we propose a low-complexity distance-constrained submodular position search algorithm, which is theoretically shown to achieve at least 1/3 of the optimum. Furthermore, we extend our approach to scenarios with imperfect CSI, and show that the proposed submodular optimization-based design remains robust against channel estimation errors. Numerical results demonstrate that the proposed scheme can achieve at least 90% of the optimal solution's MI gain under both perfect and imperfect CSI assumptions. Remarkably, the algorithm achieves orders-of-magnitude complexity reduction (e.g., 34.4x faster than the branch-and-bound approach) while maintaining significant MI gains.

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arXiv2026

Spatially Reconfigurable Antenna Systems for 6G: EM-based Channel Modeling, Measurements, and Orientation Design

Spatially reconfigurable antenna systems (SRASs) are recognized as a key physical-layer technology for sixth-generation (6G) systems. By dynamically adjusting each antenna element's spatial configuration, e.g., position and orientation, SRASs can revamp favorable channel conditions for reliable high-rate data transmission. However, in widely adopted channel models, antennas are typically modeled as ideal isotropic radiators, and the vectorial nature of electromagnetic (EM) propagation is neglected. This oversimplified model precludes full exploitation of the degrees of freedom offered by SRASs for performance enhancement. To address this issue, in this paper, by leveraging the theoretical framework of spherical vector wave expansion, we develop an EM-based channel model tailored for SRAS-enabled multiple-input multiple-output (MIMO) systems. The proposed EM-based channel model is applicable to antennas with arbitrary structures and intrinsically accounts for the vectorial nature of EM propagation, thereby enabling accurate characterization of EM effects such as polarization mismatch on channel gain. Full-wave simulations and experimental measurements are conducted, and the results show excellent agreement with theoretical predictions. Simulation results also reveal that antenna orientation exerts a more pronounced influence on the achievable rate than antenna displacement. Therefore, building upon the derived channel model, a manifold optimization method is proposed to maximize the sum-rate of an SRAS-enabled multiuser-MIMO system by optimizing antenna orientations. Simulation results demonstrate that the proposed scheme improves the sum-rate by up to 16.6\% and 19.3\% compared to systems employing movable antennas and conventional fixed antennas, respectively.

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arXiv2026

WARA: A Closed-Loop Multi-Agent Framework for Wireless Optimization Autoresearch

Large language model (LLM) agents have shown growing capabilities in tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a particular focus on wireless resource allocation optimization, an essential area for characterizing the fundamental performance limits of wireless systems and enhancing their practical performance under dynamic channel and network conditions. Specifically, we propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system that transforms an initial research topic into a complete research package. WARA organizes the research workflow into three phases: 1) research gap identification and problem proposal, 2) optimization modeling, algorithm design, and experimentation, and 3) research deliverable construction. Each phase follows an artifact-mediated process, in which structured upstream artifacts are consumed to generate downstream outputs. Controller-managed gates validate these artifacts and maintain consistency among problem formulations, algorithms, experiments, and research claims. When validation fails, WARA repairs only the affected artifact instead of restarting the entire workflow. We further design an LLM-based ScoringAgent to evaluate manuscript-level research validity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed papers. These results demonstrate the potential of closed-loop artifact control for end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ

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arXiv2026

Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

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arXiv2026

Bending the Rules of Propagation: Caustic Beamforming for Next-Generation Wireless Systems

Conventional beamforming techniques primarily steer energy along desired directions or focus it at specific locations. These techniques become fragile when facing frequent blockage and highly dynamic propagation environments. In this article, we present caustic beamforming as a new paradigm for wireless beam control. First, we classify representative caustic beams according to their underlying mathematical origins and present three unique properties, namely self-bending, self-healing, and near-field non-diffracting. Building on these propagation properties, we then propose several application scenarios in sixth-generation (6G) networks. We undertake two case studies focused on physical layer security and service stability that highlight the capability of caustic beams to bypass potential eavesdroppers, deliver more uniform coverage, and sustain blockage-resilient links. We further discuss the enabling hardware architectures that facilitate practical deployments, and finally outline key open challenges regarding caustic beams that require further research.

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arXiv2026

Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO

The next-generation wireless networks are envisioned to jointly support high-rate communications and ubiquitous sensing. Ultra-Massive Multiple-Input Multiple-Output (UM-MIMO) offers abundant spatial Degrees of Freedom (DoFs) for both functions, yet its large aperture shifts electromagnetic propagation into the near field, invalidating conventional far-field (plane-wave) assumptions. While near-field channel modeling has been studied, existing channel estimation methods are inadequate: on-grid designs suffer from non-orthogonal codebooks, and off-grid methods lack convergence guarantees, yielding unreliable estimates. Moreover, channel estimation and localization are typically designed in isolation, preventing the exchange of information that could otherwise enable mutual performance improvement. To address this difficulty, we propose a unified framework that exploits near-field characteristics to jointly design channel estimation and cooperative localization. Specifically, we develop a Variational Newtonized Near-field Channel Estimation (VNNCE) algorithm that extracts position-aware soft information from the channel, and a Gaussian Fusion Cooperative Localization (GFCL) method that leverages this information across multiple Base Stations (BSs) for enhanced accuracy.

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arXiv2026

Throughput Analysis for Near-Field Mobile Communications: Beamfocusing or Caustic Beamforming?

The migration to the Terahertz (THz) band and the deployment of extremely large antenna arrays (ELAAs) are transitioning wireless communications into the radiative near-field regime, fundamentally evolving conventional angular beam steering to beamfocusing (BF). However, the combination of the extremely narrow beamwidth and the mobility of the users necessitates frequent beamfocusing reconfigurations, incurring a significant switching overhead that degrades the system achievable throughput. In this regard, caustic beamforming (CB) is a promising alternative based on the synthesis of a continuous curved beam, which eliminates the need for beam tracking at the expense of a distributed beamforming gain. By leveraging the Airy beam as a canonical model, this paper develops an analytical framework to compare the throughputs achieved by CB and BF. Our main results include closed-form throughput expressions for both beamforming strategies and a performance boundary for paradigm selection. First, we derive the BF throughput by modeling a defocusing penalty induced by continuous user movement. The optimal beam dwell time that maximizes the throughput is analytically determined, and the impact of user speed and switching overhead on the throughput is quantified. For the CB scheme, we demonstrate that its throughput is determined by the signal-to-noise ratio (SNR) and the geometry of the trajectory of the user, yet invariant to the user speed. Finally, we analytically establish a threshold for the switching overhead to define the crossover point of the achievable throughput of both beamformers. Crucially, this threshold asymptotically vanishes at extremely high frequencies, positioning the continuous CB scheme as the preferred beam design paradigm for high-mobility THz communications.

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arXiv2026

WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents

Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a focus on wireless resource allocation optimization. We propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system for automated wireless optimization research. Given only an initial topic, WARA decomposes the workflow into three phases: research gap identification and problem proposal, wireless optimization modeling, algorithm design and experimentation, and research deliverable construction. Across these phases, WARA uses artifact-mediated control: upstream artifacts are consumed as inputs, structured outputs are stored for downstream use, and controller-managed gates validate consistency among models, algorithms, experiments, and claims. When validation fails, WARA repairs only the responsible artifact instead of restarting the whole workflow. We present a representative wireless resource allocation case study showing how WARA converts an initial topic into a complete research package with executable evidence and a synthesized technical manuscript. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed technical papers. These results indicate that closed-loop artifact control is a promising path toward end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ.

cs.NI↗

arXiv2026

UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning

The low-altitude economy (LAE) is reshaping the industrial landscape by deploying unmanned aerial vehicles (UAVs) to facilitate a wide range of applications demanding flexible aerial mobility. Integrating edge artificial intelligence (AI) into LAE platforms creates a compelling paradigm where UAVs provide real-time AI-driven analysis while simultaneously executing their primary aerial mission duties. However, realizing this paradigm remains challenging due to the strict mission constraints imposed by these primary duties and the throughput bottlenecks of wireless links. To bridge this gap, we propose a UAV-assisted cooperative edge inference framework where UAVs execute mission-critical LAE duties, quantified by trajectory deviations from reference paths, while concurrently supporting ground devices via intermediate feature offloading. Within this framework, UAV trajectories, inference task offloading decisions, and feature compression ratios are jointly optimized to maximize the system performance. We cast this joint optimization task into a constrained partially observable Markov decision process (POMDP) framework. To efficiently solve it, we propose HDRL-MoE, a novel hierarchical deep reinforcement learning framework that decouples the optimization of slow-varying inference decisions from rapidly changing UAV trajectory control. Furthermore, HDRL-MoE integrates a mixture-of-experts (MoE) architecture, where a router network orchestrates discrete offloading decisions while expert networks independently optimize the feature compression ratios. Extensive simulations show that HDRL-MoE achieves significant inference accuracy gains over baselines and exhibits high scalability and efficiency through its MoE design.

cs.NI↗

arXiv2026

A General EM-Based Channel Model for Reconfigurable Antenna Systems

Reconfigurable antenna systems (RASs), such as fluid antennas and movable antennas, are poised to play a pivotal role in sixth-generation (6G) systems by dynamically adapting the antenna elements for system performance enhancement. However, unlocking their full potential requires channel models that accurately capture the influence of antenna configurations on the radiation, propagation, and reception of signals. Existing channel models suffer from several limitations, such as neglecting polarization effects, being restricted to specific antenna types, or relying on oversimplified assumptions. In this paper, we propose a general electromagnetic (EM)-based channel model grounded in spherical vector wave expansion (SVWE). The proposed EM-based channel model captures the impact of antenna position and orientation on the channel gain, thereby making it particularly well-suited for RASs. The effectiveness and accuracy are validated through comparisons with commercial simulation software, demonstrating excellent agreement in predicted channel gains. Moreover, it is shown that antenna orientation is a critical factor governing communication performance, and that dynamically adjusting the antenna orientation yields up to 70% improvement in achievable communication rate compared to a fixed-antenna configuration.

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arXiv2026

Integrated Sensing and Communications for Low-Altitude Economy with Deterministic Sensing and Gaussian Information Signals

Reliable surveillance and communication for unmanned aerial vehicles (UAVs) are crucial for enabling and sustaining the accelerated growth of the low-altitude economy. Integrated sensing and communications (ISAC) offers a cost-effective and scalable framework for target sensing by leveraging existing wireless communication systems. This paper investigates a bistatic downlink ISAC architecture tailored to UAV operations, in which a BS communicates with a legitimate UAV and detects a potential unauthorized intruder in the surveillance region. We assume that the BS transmits superimposed ISAC waveforms comprising both Gaussian-information-bearing and deterministic sensing components. First, we develop a Neyman-Pearson (NP)-based optimal detector that jointly exploits both deterministic sensing and stochastic signal components. Subsequently, we optimize the transmit beamforming design at the BS to maximize the minimum detection probability over the entire surveillance region, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the authorized UAV and a total transmit power budget at the BS. The resulting design problem is highly non-convex, which is efficiently addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which fully leverages the synergy between both types of signals, over conventional benchmark schemes that treat information-bearing signals merely as interference. Furthermore, the results reveal a fundamental sensing-communication trade-off, where increasing the communication-rate threshold directs more transmit power to Gaussian-information-bearing signals, thereby reducing the power allocated to deterministic components and consequently weakening detection performance.

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arXiv2026

Energy-Efficient Velocity Profile Optimization for Movable Antenna-Enabled Sensing Systems

Movable antennas (MAs) enable the reconfiguration of array geometry within a bounded region to exploit sub-wavelength spatial degrees of freedom in wireless communication and sensing systems. However, most prior research has predominantly focused on the communication and sensing performance, overlooking the mechanical power consumption inherent in antenna movement. To bridge this gap, this paper investigates a velocity profile optimization framework for MA-assisted direction-of-arrival (DoA) estimation, explicitly balancing sensing accuracy with mechanical energy consumption of MAs. We first establish a Newtonian-based mechanical energy model, and formulate a functional optimization problem for sensing energy efficiency (EE) maximization. By applying the calculus of variations, this formulation is transformed into an infinite-dimensional problem defined by the Euler-Lagrange equation. To solve it, we propose a spectral discretization framework based on the Galerkin method, which expands the velocity profile over a sinusoidal basis. In the regime where energy consumption is dominated by linear damping, we prove that the optimal velocity profile follows a closed-form sinusoidal shape. For more general scenarios involving strong nonlinear aerodynamic drag, we leverage the Markov-Lukács theorem to transform the kinematic constraints into strictly convex sum-of-squares (SOS) conditions. Consequently, the infinite-dimensional problem is reformulated as a tractable finite-dimensional nonlinear algebraic system, which is solved by a two-layer algorithm combining Dinkelbach's method with successive convex approximation (SCA). Numerical results demonstrate that our optimized velocity profile significantly outperforms baselines in terms of EE across various system configurations. Insights into the optimized velocity profiles and practical design guidelines are also provided.

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arXiv2026

A Unified Codebook Design for Curvature-Reconfigurable Apertures: Seamless Near to Far Field Coverage

Beam training for extremely large-scale arrays with curvature-reconfigurable apertures (CuRAs) faces the critical challenge of severe, geometry-dependent angle-range coupling. While most existing designs compartmentalize near field and far field scenarios, we propose a unified, distance-adaptive hierarchical codebook framework for 1-D and 2-D CuRAs that seamlessly bridges both propagation regimes. Under a spherical-wave model, we first characterize the beamforming-gain correlation in a polar angular domain, deriving an angle-dependent angular sampling rule to capture the varying curvature. To achieve full-range coverage, we introduce a direction-dependent effective Rayleigh distance (ERD) as a soft boundary to gate the range sampling. Crucially, by sampling uniformly in the reciprocal-range domain, the proposed codebook provides precise, dense focusing within the ERD and automatically degenerates into sparse, angle-only steering beyond it. This mechanism eliminates the need for hard mode-switching between near- and far-field operations. Simulation results demonstrate that our unified design consistently outperforms representative baselines in spectral efficiency and alignment accuracy, offering a comprehensive solution for full-range CuRA communications.

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arXiv2026

Robust and Secure Near-Field Communication via Curved Caustic Beams

Near-field beamfocusing with extremely large aperture arrays can effectively enhance physical layer security. Nevertheless, even small estimation errors of the eavesdropper's location may cause a pronounced focal shift, resulting in a severe degradation of the secrecy rate. In this letter, we propose a physics-informed robust beamforming strategy that leverages the electromagnetic (EM) caustic effect for near-field physical layer security provisioning, which can be implemented via phase shifts only. Specifically, we partition the transmit array into caustic and focusing subarrays to simultaneously bypass the potential eavesdropping region and illuminate the legitimate user, thereby significantly improving the robustness against the localization error of eavesdroppers. Moreover, by leveraging the connection between the phase gradient and the EM wave departing angle, we derive the corresponding piece-wise closed-form array phase profile for the subarrays. Simulation results demonstrate that the proposed scheme achieves up to an 80% reduction of the worst-case eavesdropping rate for a localization error of 0.25 m, highlighting its superiority for providing robust and secure communication.

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