arXiv subjects

Xingqi Zhang

Publications and source records attributed to Xingqi Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Fast Pinching-Antenna Activation for AirComp

A pinching-antenna system (PASS) is considered for over-the-air computation (AirComp). Multiple dielectric waveguides are deployed at the base station, and one pinching antenna (PA) is activated on each waveguide. For practical implementation, each PA is restricted to a finite set of preconfigured locations. The resulting discrete activation problem is formulated to minimize the AirComp mean-squared error (MSE). After the optimal aggregation vector is derived, the minimum MSE is expressed through an inverse Gram matrix. A rank-one recursion is derived to evaluate the exact MSE reduction produced by each candidate. Greedy search and beam search are then developed for fast tree search. To further reduce complexity, coherent aggregation search is proposed from a first-order MSE approximation. It admits a separable closed-form selection rule and is asymptotically optimal in the low signal-to-noise ratio (SNR) regime. Numerical results show that the proposed methods substantially improve the AirComp accuracy of PASS over conventional antenna arrays.

eess.SP↗

arXiv2026

Beamforming Gain with Single-RF Movable Arrays

A single-radio-frequency (RF) movable array is investigated, in which all movable elements are driven by a single RF chain with equal amplitude and equal phase. The achievable beamforming gain enabled by antenna placement is analyzed. Linear beamforming gain scaling with the number of antennas is shown to be achievable in single-path channels, while coherent-combining conditions and aperture requirements are established for multipath channels. For multiuser transmission, the optimal max-min power allocation is derived in closed form, based on which an element-wise coordinate-search algorithm is developed for antenna placement design. Numerical results validate the analysis and reveal a fundamental tradeoff: beamforming gains can be achieved through antenna placement alone, but only at the expense of increased aperture resources.

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arXiv2026

Spatio-Sequential Recurrent Network for 3-D Tunnel Propagation Modeling

Fine-mesh parabolic wave equation (PWE) simulations are high-fidelity but time-consuming, which limits real-time tunnel propagation analysis and motivates coarse-to-fine reconstruction. Existing machine learning (ML)-assisted tunnel models typically provide only one-dimensional (1-D) longitudinal refinement or two-dimensional (2-D) cross-sectional refinement, rather than joint 3-D enhancement. Motivated by this gap, this letter proposes a U-shaped gated spatio-sequential recurrent neural network (UG-SSRNN), a spatio-sequential reconstruction model for tunnel electromagnetic fields. UG-SSRNN jointly super-resolves transverse slices and models longitudinal evolution. It uses sliding-window context encoding and a K-layer convolutional recurrent backbone with a shared propagation-context state and diagonal feedback. A prediction-aware upsampling head leverages the previous prediction to improve slice-to-slice consistency. Experiments on four tunnel cross sections, unseen-material and unseen-frequency tests, and validation in the Massif Central tunnel show close agreement with fine-mesh PWE references. The proposed approach significantly reduces tunnel electromagnetic modeling time.

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arXiv2026

A Stable SBP-SAT FDTD Subgridding Method Without Region Split

A provably stable summation-by-parts simultaneous approximation term (SBP-SAT) finite-difference time-domain (FDTD) subgridding method without region split is proposed. By designing projection SBP operators tailored for embedded topological features and deriving the corresponding SAT boundary conditions, this approach guarantees long-time stability through discrete energy analysis. Unlike conventional SBP-SAT FDTD subgridding techniques that rely on aligned or multi-block configurations, the proposed method enables a direct coupling between an internal refined region and a single surrounding coarse-grid domain without introducing auxiliary blocks or causing domain fragmentation. Numerical results validate the efficiency, accuracy, and topological flexibility of the proposed method. Compared with existing multi-block SBP-SAT methods, this method effectively reduces computational complexity by minimizing SAT boundary conditions and improves calculation accuracy near grid interfaces.

cs.CE↗

arXiv2026

Optimal Analog Beamforming and Power Allocation for Multiuser TDMA Systems

The joint design of analog beamforming and power allocation is investigated for a single radio-frequency chain multiuser time-division multiple access system under a max-min signal-to-noise ratio (SNR) criterion. A hardware-efficient phased-array architecture is considered, where the beamforming vector is shared by all users and is subject to constant-modulus constraints. For any fixed analog beamformer, the optimal power allocation is first derived in closed form, by which the original problem is reduced to phase-shift optimization only. Then, globally optimal branch-and-bound (BB) algorithms are developed for discrete and continuous phase shifts. Numerical results show that the proposed BB algorithms achieve the global optimum and provide reliable benchmarks for evaluating the performance gap of low-complexity alternating-optimization methods.

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arXiv2026

Uncertainty Quantification of Radio Wave Propagation over Irregular Terrains Using Adaptive Polynomial Chaos Expansion

Accurate modeling of radio wave propagation over irregular terrains is crucial for designing reliable wireless communication systems in such environments, yet uncertainties in the antenna configuration are not quantified within deterministic models. In this paper, we present, to the best of our knowledge, the first uncertainty quantification (UQ) study of realistic antenna configurations for irregular-terrain propagation. An adaptive polynomial chaos expansion (APCE) method is improved and coupled with a two-way parabolic wave equation (PWE) method to address this problem efficiently. The polynomial basis is extended according to variance contributions and terminated by a composite criterion combining validation error and sample-to-basis ratio, enabling stable coefficient estimations via least-square regression without additional regularization. Convergence analysis shows a monotonic error decay with increasing training samples, producing compact, low-interaction models and improved accuracy and robustness over the previous APCE methods. For two realistic terrain profiles, the proposed method accurately predicts the mean and the 5th-95th percentile range of the path loss, matching Monte Carlo (MC) references using only 30 PWE simulations. Using a fixed sampling budget, APCE outperforms standard and sparse PCE, with the largest gains observed for the 5th and 95th percentile estimates; as the sample size increases, APCE maintains low errors with reduced trial-to-trial variability.

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arXiv2026

Continuous-Aperture Array-Based ISAC Over Fading Channels

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

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arXiv2026

Dynamic Antenna Placement for Mobile Users in Urban Micro Pinching-Antenna Systems

The pinching-antenna systems (PASS) enable blockage mitigation in urban micro (UMi) networks through flexible antenna placement. However, the joint optimization of antenna positions and beamforming precoding is inherently nonconvex and becomes significantly more challenging under user mobility. To address this issue, we propose a bilevel optimization framework for dynamic antenna positioning and beamforming precoding design. In the outer level, a soft actor-critic (SAC) agent learns a continuous control policy for real-time antenna positioning, while in the inner level, zero-forcing (ZF) precoding is applied based on the instantaneous effective channel. Numerical results demonstrate that the proposed framework significantly improves spectral efficiency (SE) and enhances robustness against user mobility and random blockages.

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arXiv2026

Fairness-Oriented Optimization of NOMA-Enabled Pinching-Antenna Systems Under Blockage and Imperfect CSI

The pinching-antenna system (PASS) has been proposed as a promising solution for mitigating line-of-sight (LoS) blockages by dynamically repositioning pinching antennas (PAs) along a dielectric waveguide. This paper develops a fairness-oriented downlink design for a non-orthogonal multiple access (NOMA)-enabled PASS, where the longitudinal placement of PAs and the NOMA power allocation coefficients are jointly optimized to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) across all users under transmit power and waveguide constraints. A soft-blockage channel model incorporating waveguide attenuation and imperfect channel state information (CSI) is developed. To ensure the feasibility of successive interference cancellation under CSI uncertainty, a conservative SINR evaluation framework is proposed. The resulting non-convex max-min SINR optimization problem is efficiently solved using a tailored particle swarm optimization (PSO) algorithm. Numerical results demonstrate that the proposed design improves the minimum user SINR by approximately 7-10 dB compared with fixed-antenna systems and non-robust optimization baselines under moderate blockage and imperfect CSI.

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arXiv2026

Secure Multiuser Beamforming With Movable Antenna Arrays

A movable antenna (MA)-enabled secure multiuser transmission framework is developed to enhance physical-layer security. Novel expressions are derived to characterize the achievable sum secrecy rate based on the secure channel coding theorem. On this basis, a joint optimization algorithm for digital beamforming and MA placement is proposed to maximize the sum secrecy rate via fractional programming and block coordinate descent. In each iteration, every variable admits either a closed-form update or a low-complexity one-dimensional or bisection search, which yields an efficient implementation. Numerical results demonstrate the effectiveness of the proposed method and show that the MA-enabled design achieves higher secrecy rates than conventional fixed-position antenna arrays.

cs.IT↗

arXiv2026

Physics-Informed Deep Recurrent Back-Projection Network for Tunnel Propagation Modeling

Accurate and efficient modeling of radio wave propagation in railway tunnels is is critical for ensuring reliable communication-based train control (CBTC) systems. Fine-grid parabolic wave equation (PWE) solvers provide high-fidelity field predictions but are computationally expensive for large-scale tunnels, whereas coarse-grid models lose essential modal and geometric details. To address this challenge, we propose a physics-informed recurrent back-projection propagation network (PRBPN) that reconstructs fine-resolution received-signal-strength (RSS) fields from coarse PWE slices. The network integrates multi-slice temporal fusion with an iterative projection/back-projection mechanism that enforces physical consistency and avoids any pre-upsampling stage, resulting in strong data efficiency and improved generalization. Simulations across four tunnel cross-section geometries and four frequencies show that the proposed PRBPN closely tracks fine-mesh PWE references. Engineering-level validation on the Massif Central tunnel in France further confirms robustness in data-scarce scenarios, trained with only a few paired coarse/fine RSS. These results indicate that the proposed PRBPN can substantially reduce reliance on computationally intensive fine-grid solvers while maintaining high-fidelity tunnel propagation predictions.

cs.ET↗

arXiv2025

Movable Antennas-Assisted Over-the-Air Computation: Dynamic and Static Design

A novel over-the-air computation (AirComp) framework empowered by movable antennas (MAs) is proposed to significantly enhance computation accuracy. Within this framework, the joint optimization of transmit power control, antenna positioning, and receive beamforming is investigated. Two design strategies are developed: (i) a dynamic design, where MA positions are optimized based on fast-varying instantaneous channel state information (CSI); and (ii) a static design, where antenna positions are optimized using only slow-varying statistical CSI. Numerical results validate the superior MSE performance of the proposed MA-enabled AirComp framework and demonstrate its clear advantage over benchmark systems employing conventional fixed-position antennas (FPAs).

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arXiv2025

Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements

High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data.

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arXiv2025

Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning

Urban railway systems increasingly rely on communication based train control (CBTC) systems, where optimal deployment of access points (APs) in tunnels is critical for robust wireless coverage. Traditional methods, such as empirical model-based optimization algorithms, are hindered by excessive measurement requirements and suboptimal solutions, while machine learning (ML) approaches often struggle with complex tunnel environments. This paper proposes a deep reinforcement learning (DRL) driven framework that integrates parabolic wave equation (PWE) channel modeling, conditional generative adversarial network (cGAN) based data augmentation, and a dueling deep Q network (Dueling DQN) for AP placement optimization. The PWE method generates high-fidelity path loss distributions for a subset of AP positions, which are then expanded by the cGAN to create high resolution path loss maps for all candidate positions, significantly reducing simulation costs while maintaining physical accuracy. In the DRL framework, the state space captures AP positions and coverage, the action space defines AP adjustments, and the reward function encourages signal improvement while penalizing deployment costs. The dueling DQN enhances convergence speed and exploration exploitation balance, increasing the likelihood of reaching optimal configurations. Comparative experiments show that the proposed method outperforms a conventional Hooke Jeeves optimizer and traditional DQN, delivering AP configurations with higher average received power, better worst-case coverage, and improved computational efficiency. This work integrates high-fidelity electromagnetic simulation, generative modeling, and AI-driven optimization, offering a scalable and data-efficient solution for next-generation CBTC systems in complex tunnel environments.

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arXiv2025

Exploiting Movable Antennas in Multicast Communications

This article investigates the integration of movable antennas (MAs) into multicast communication systems. By discretizing the motion of the MAs, a novel MA-assisted multicast transmission architecture is formulated. An alternating optimization (AO) algorithm based on successive convex approximation is proposed to optimize the transmit beamforming and antenna positions. To gain further insights, the two-user case is examined, and a closed-form expression for the optimal beamformer is derived. On this basis, a low-complexity greedy search algorithm is developed to optimize the placement of the MAs. Furthermore, under the assumption of a line-of-sight propagation environment, a branch-and-bound algorithm is designed to determine the globally optimal antenna configuration with reduced complexity compared to exhaustive search. Numerical simulations confirm that the proposed methods effectively enhance the achievable multicast rate.

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arXiv2025

Linear Receive Beamforming for CAPA Systems

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is analyzed. Three continuous beamforming techniques are proposed under the criteria of maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-squared error (MMSE). \romannumeral1) For \emph{MRC beamforming}, a closed-form expression for the beamformer is derived to maximize per-user signal power. The achieved uplink rate and mean-squared error (MSE) in detecting received data symbols are analyzed. \romannumeral2) For \emph{ZF beamforming}, a closed-form beamformer is derived based on channel correlation to eliminate interference. As a further advance, its optimality in maximizing effective channel gain while ensuring zero inter-user interference is proven. \romannumeral3) \emph{MMSE beamforming} is established as the optimal linear receive approach for CAPAs in terms of maximizing per-user rate and minimizing MSE. Closed-form expressions are derived for the MMSE beamformer and the achievable sum-rate and sum-MSE. It is mathematically proven that all proposed beamformers lie within the signal subspace spanned by users' spatial responses. Numerical results demonstrate that CAPAs outperform conventional spatially-discrete arrays (SPDAs) by achieving higher sum-rates and lower sum-MSEs under the proposed linear beamforming techniques.

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arXiv2025

Secure Wireless Communications via Frequency Diverse Arrays

A novel frequency diverse array (FDA)-assisted secure transmission framework is proposed, which leverages additional frequency offsets to enhance physical layer security. Specifically, an FDA-assisted wiretap channel is considered, where the transmit beamforming and frequency offsets at each antenna are jointly optimized. A novel alternating optimization-based method is introduced to address the non-convex problem of secure transmission, focusing on minimizing transmit power and maximizing the secrecy rate. Numerical results are provided to demonstrate the superiority of the FDA-based framework compared to systems employing traditional phased array antennas in secure transmission.

eess.SP↗

arXiv2025

Performance Analysis of Physical Layer Security: From Far-Field to Near-Field

The secrecy performance in both near-field and far-field communications is analyzed using two fundamental metrics: the secrecy capacity under a power constraint and the minimum power requirement to achieve a specified secrecy rate target. 1) For the secrecy capacity, a closed-form expression is derived under a discrete-time memoryless setup. This expression is further analyzed under several far-field and near-field channel models, and the capacity scaling law is revealed by assuming an infinitely large transmit array and an infinitely high power. A novel concept of "depth of insecurity" is proposed to evaluate the secrecy performance achieved by near-field beamfocusing. It is demonstrated that increasing the number of transmit antennas reduces this depth and thus improves the secrecy performance. 2) Regarding the minimum required power, a closed-form expression is derived and analyzed within far-field and near-field scenarios. Asymptotic analyses are performed by setting the number of transmit antennas to infinity to unveil the power scaling law. Numerical results are provided to demonstrate that: i) compared to far-field communications, near-field communications expand the areas where secure transmission is feasible, specifically when the eavesdropper is located in the same direction as the intended receiver; ii) as the number of transmit antennas increases, neither the secrecy capacity nor the minimum required power scales or vanishes unboundedly, adhering to the principle of energy conservation.

cs.IT↗