arXiv subjects

Song Zhao

Publications and source records attributed to Song Zhao.

9 recordsLinked to original sources

arXiv2026

Fluid-Dynamic Interference Modeling for LEO Mega-Constellations: A Spatiotemporal Kinetic Field Approach

Low Earth orbit (LEO) mega-constellations create a highly non-stationary interference environment that cannot be accurately captured by static stochastic-geometry snapshots. This paper proposes a kinetic interference field framework that models the constellation as a compressible fluid shell evolving under orbital kinematics. By mapping satellite motion into a continuum flux field, we derive a hydrodynamic conservation law for the aggregate interference and obtain a closed-form expression for the time-varying outage probability via moment matching. The analysis reveals that high-latitude ``interference surges'' are a direct consequence of orbital compression and boundary flux, rather than random anomalies. Numerical validation against ephemeris-driven Monte Carlo simulations confirms the accuracy of the framework across time evolution, latitude, and design parameters. Leveraging the closed-form model, we further show that the conventional $90^{\circ}$ polar-orbit design is not universally outage-optimal. Instead, an inclination angle near $79^{\circ}$ at low altitude achieves a favorable trade-off between coverage continuity and geometric interference isolation. The proposed framework provides a tractable analytical tool for interference-aware 6G non-terrestrial network (NTN) design.

cs.NI↗

arXiv2026

Proper Sea Surface Roughness Enhances the Performance of Near-Shore Maritime Networks

Accurate performance analysis for near-shore maritime wireless communication is essential for ensuring robust and reliable operations. However, existing analytical models often rely on oversimplified propagation assumptions, such as a perfectly smooth sea surface, which fail to capture the full dynamics of the maritime channel. In this paper, we develop a physically grounded analytical framework using stochastic geometry that bridges this gap. The spatial distribution of vessels is modeled as a non-homogeneous Poisson point process to reflect realistic near-port densities. We replace the idealized smooth-sea assumption by deriving a novel reflection coefficient from the classical Rayleigh criterion, which explicitly links the path loss to the significant wave height. Integrating this roughness-aware channel model into the stochastic geometry framework, we derive new analytical expressions for the uplink coverage probability and average ergodic rate, providing the first tractable characterization of aggregate interference under such dynamic conditions. The analysis reveals a sea-state-dependent reliability--capacity trade-off: roughness-induced attenuation of the coherent specular reflection can suppress destructive-interference nulls and improve reliability-oriented coverage, while reducing high-SINR and average-rate performance. Available measurements support the underlying roughness-sensitive reflection mechanism, but direct VHF validation under rough sea conditions remains unavailable; the corresponding rough-sea results are therefore interpreted as model-based predictions. A cross-frequency ablation further confirms the wavelength dependence of the roughness effect and shows that the reflection coefficient must be evaluated for the operating frequency.

cs.NI↗

arXiv2026

Digital Tides: A Fluid-Dynamic Framework for Flux-Aware Infrastructure Provisioning in UAV Logistics Networks

The emergence of high-frequency pulsating logistics unmanned aerial vehicle (UAV) swarms gives rise to ``Digital Tides'', i.e., complex traffic dynamics that challenge sustainable resource provisioning in mobile computing networks. Conventional infrastructure provisioning strategies, which typically rely on static snapshot-based analysis and localized density estimation, fail to capture the macroscopic advection of computational workloads. As a result, reactive resource activation suffers from inherent hysteresis, yielding nominal efficiency gains at the cost of mission-critical service loss at the advancing wavefront. To address this issue, we develop a fluid-based spatiotemporal framework by explicitly solving the continuity equation to characterize the macroscopic velocity field of the workload flow. Building on this framework, we propose a flux-aware asymmetric activation strategy that leverages the derived information flux vector as a kinematic precursor of demand propagation. Unlike symmetric thresholding, the proposed control logic decouples activation and deactivation dynamics. Theoretical analysis confirms the intrinsic spatial phase-lead of the flux signal and shows that the proposed strategy generates a proactive guard ring to compensate for service setup latency, including delays caused by mobile edge computing container cold-starts. We further derive closed-form expressions for instantaneous service availability and period-average energy efficiency. In addition, we formulate a quality-of-service-penalized metric to evaluate effective energy efficiency under strict outage constraints. Numerical results show that the proposed flux-driven strategy enables zero-latency tracking of the mobile wavefront and achieves a Pareto-optimal trade-off between service reliability and energy consumption, outperforming reactive baselines in dynamic logistics corridors.

cs.NI↗

arXiv2026

Vorticity Dissipation Based Routing: A Fluid-Kinetic Framework for Loop-Free Transport in Ultra-Dense Networks

Discrete routing protocols in ultra-dense wireless networks are constrained by signaling overhead and transient routing loops that degrade radio-resource efficiency. While continuum modeling provides a scalable alternative, existing scalar density approaches lack the vector geometric structure required to characterize these topological anomalies. This paper introduces a fluid-kinetic framework, vorticity dissipation-based routing (VDR), utilizing the Helmholtz-Hodge decomposition. We demonstrate that the macroscopic traffic flux can be orthogonally decoupled into a demand-driven irrotational component and a loop-induced solenoidal component representing routing vorticity. Building on this insight, we define network vorticity as a macroscopic metric to quantify topological inefficiency. Routing optimization is formulated as a gradient flow on an enstrophy functional, yielding a vorticity dissipation equation as the governing dynamic law. Lyapunov stability analysis proves that this mechanism ensures the monotonic decay of global enstrophy toward an asymptotically loop-free equilibrium. Numerical results validate that VDR suppresses realized forwarding loops, reduces end-to-end delay, maintains robust packet delivery, and exhibits near-linear scaling under fixed-area densification while explicitly accounting for the grid-dependent Poisson-solver cost.

cs.NI↗

arXiv2026

An improved fully one-sided diffuse-interface immersed boundary method with target-value reconstruction for compressible flows

Although one-sided spreading has been shown to improve the near-wall accuracy of diffuse-interface immersed boundary methods (DIBMs), the effect of its asymmetric kernel support on the effective boundary location remains insufficiently understood. In this work, a detailed analysis of the one-sided spreading operator reveals an inward displacement of the effective boundary relative to the geometric boundary. To compensate for this displacement, a target-value reconstruction strategy is developed to ensure consistency between the values imposed at the effective boundary and the prescribed conditions at the geometric boundary. The strategy is incorporated into the fully one-sided diffuse-interface immersed boundary method (FODIBM) and applies to both Dirichlet and Neumann boundary conditions. Although confined to the target-value evaluation step, the modification substantially improves boundary-condition enforcement with negligible additional computational cost. Coupled with a hybrid lattice Boltzmann solver, the improved method consistently reduces L_2 and L_{\infty} error norms across different grid resolutions while retaining approximately second-order grid convergence. The no-slip and isothermal boundary-condition errors are reduced by 77% and 85%, respectively. Simulations involving various two- and three-dimensional geometries further show improved predictions relative to both the conventional DIBM and the original FODIBM. The results agree well with body-fitted reference solutions and experimental data, demonstrating accurate and computationally efficient simulations of compressible flows around complex geometries.

physics.flu-dyn↗

arXiv2026

A fully one-sided diffuse-interface immersed boundary method for wall-modeled large-eddy simulation

Diffuse-interface immersed boundary methods (DIBMs) provide a simple and robust approach for simulating flows involving complex geometries. However, their inherent diffusion effect can contaminate the near-wall flow field and significantly degrade wall-shear-stress prediction in wall-modeled large-eddy simulation (WMLES). To address this limitation, we develop a WMLES approach based on a fully one-sided diffuse-interface immersed boundary method (FODIBM). By performing interpolation and spreading exclusively inside the immersed body, the proposed method removes the cross-boundary diffusion effect that adversely affects wall modeling in conventional DIBMs. A wall-shear-stress enforcement strategy is developed by coupling the wall-parallel immersed-boundary forcing with the wall shear stress predicted by an explicit wall model. In addition, a tau-model based on the modeled turbulent shear-stress tensor is introduced to preserve the total shear-stress balance below the reference height. The method is first validated in high-Reynolds-number turbulent channel flows, showing good agreement with DNS data for the mean velocity, Reynolds shear stress, and skin-friction coefficient. Sensitivity studies with respect to grid resolution, reference height, wall inclination angle, and Reynolds number demonstrate the robustness of the method. Compared with the conventional DIBM, the proposed method substantially improves the overall prediction accuracy, particularly at low reference heights. The approach is further assessed for turbulent flow over a NACA23012 airfoil, where the predicted pressure distribution and lift coefficient agree well with experimental data.

physics.flu-dyn↗

arXiv2026

Fluid-Spatiotemporal Stochastic Geometry: Information Flow in Non-Stationary Fields

The fundamental limits of information flow in spatial networks are usually characterized under stationary spatial point processes, but this assumption cannot capture non-stationary regimes where the node intensity field evolves continuously in space and time. This paper develops Fluid-Spatiotemporal Stochastic Geometry (F-STSG), treating dynamic network topology as a hydrodynamic limit of the discrete node constellation. We formulate the identification of latent network dynamics as an inverse boundary value problem and, using the minimum kinetic energy principle from optimal transport, establish the existence and uniqueness of a scalar potential field governing the compressive evolution of network load. The resulting field-theoretic formulation couples continuous Lagrangian transport with discrete Eulerian interference geometry. Based on this model, we derive the information flux vector as a sufficient statistic for macroscopic advection and the material derivative as a kinematic predictor of topological divergence. We further characterize non-stationary network limits through energy-density scaling and source-channel interpretation, showing how coordination overhead, topology deformation, and control signaling requirements are linked to the kinematic entropy of the evolving network topology.

cs.NI↗

arXiv2023

3D Gaze Vis: Sharing Eye Tracking Data Visualization for Collaborative Work in VR Environment

Conducting collaborative tasks, e.g., multi-user game, in virtual reality (VR) could enable us to explore more immersive and effective experience. However, for current VR systems, users cannot communicate properly with each other via their gaze points, and this would interfere with users' mutual understanding of the intention. In this study, we aimed to find the optimal eye tracking data visualization , which minimized the cognitive interference and improved the understanding of the visual attention and intention between users. We designed three different eye tracking data visualizations: gaze cursor, gaze spotlight and gaze trajectory in VR scene for a course of human heart , and found that gaze cursor from doctors could help students learn complex 3D heart models more effectively. To further explore, two students as a pair were asked to finish a quiz in VR environment, with sharing gaze cursors with each other, and obtained more efficiency and scores. It indicated that sharing eye tracking data visualization could improve the quality and efficiency of collaborative work in the VR environment.

cs.HC↗

arXiv2022

Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra

Low-rank approximation of images via singular value decomposition is well-received in the era of big data. However, singular value decomposition (SVD) is only for order-two data, i.e., matrices. It is necessary to flatten a higher order input into a matrix or break it into a series of order-two slices to tackle higher order data such as multispectral images and videos with the SVD. Higher order singular value decomposition (HOSVD) extends the SVD and can approximate higher order data using sums of a few rank-one components. We consider the problem of generalizing HOSVD over a finite dimensional commutative algebra. This algebra, referred to as a t-algebra, generalizes the field of complex numbers. The elements of the algebra, called t-scalars, are fix-sized arrays of complex numbers. One can generalize matrices and tensors over t-scalars and then extend many canonical matrix and tensor algorithms, including HOSVD, to obtain higher-performance versions. The generalization of HOSVD is called THOSVD. Its performance of approximating multi-way data can be further improved by an alternating algorithm. THOSVD also unifies a wide range of principal component analysis algorithms. To exploit the potential of generalized algorithms using t-scalars for approximating images, we use a pixel neighborhood strategy to convert each pixel to "deeper-order" t-scalar. Experiments on publicly available images show that the generalized algorithm over t-scalars, namely THOSVD, compares favorably with its canonical counterparts.

cs.LG↗