arXiv subjects

Yifeng Qin

Publications and source records attributed to Yifeng Qin.

10 recordsLinked to original sources

arXiv2026

Non-Diffracting Beams for Near-Field Millimeter-Wave Communications: Advantage Regimes Under Aperture and Blockage Constraints

Near-field blockage changes the beam-design objective in millimeter-wave links: maximizing the unblocked on-axis gain does not necessarily maximize blocked-link performance. This paper studies when phase-only, aperture-constrained non-diffracting (ND) beams provide a blocked-link advantage over equal-aperture, equal-power conventional reference beams. We develop a unified annular-spectrum framework that generates isotropic Bessel-like and anisotropic Mathieu-like beams under discrete phased-array constraints, and a geometry-aware analysis centered on three propagation landmarks: the peak-intensity distance, the crossover distance, and an effective post-blockage recovery distance. Their relationship yields a recovery-before-crossover condition linking blockage size, depth, cone angle, and usable ND range, and motivates a blocked-link gain ratio that maps directly onto an achievable-rate gap at every operating SNR. The analysis also explains why anisotropic Mathieu-like beams can outperform isotropic ones under direction-dependent blockage. Monte Carlo simulations verify the predicted advantage regimes, an auxiliary comparison against a near-field focusing baseline confirms that the advantage persists against an unblocked-optimal array, and sensitivity studies over cone-angle choice and partial-transmission blockers show that the opaque-screen picture is a conservative reading of the underlying physics. The results identify Bessel-like and Mathieu-like beams as practical candidates for blockage-resilient near-field communications.

physics.optics↗

arXiv2026

Accelerating Inverse Design of Optical Metasurfaces: Analytic Gradients of Periodic Green's Functions via Quasi-Modular Forms

The inverse design of nonlocal metasurfaces requires the precise optimization of lattice geometry to engineer spatial dispersion and high-Q resonances. However, gradient-based optimization is frequently bottle-necked by the evaluation of the periodic Dyadic Green's Function (DGF), where traditional Finite Difference (FD) methods suffer from an inherent trade-off between truncation error and numerical instability near spectral singularities. In this work, we present an end-to-end Analytic Gradient Engine for 2D Bravais lattices. By mapping the spectral lattice sums of the Coupled Dipole Approximation (CDA) to the theory of Quasi-Modular Forms (QMF), we derive exact, closed-form expressions for the gradients of the interaction matrix with respect to the modular lattice parameter $τ$. Our framework explicitly handles conditionally convergent terms via regularization and addresses the non-holomorphic outlier $σ_4^{(2)}$ via a hybrid numerical strategy. We further introduce a robust evaluation scheme combining $SL(2, \mathbb{Z})$ domain reduction with automatic error certificates. Experimental validation demonstrates that our engine achieves machine-precision derivatives ($10^{-15}$) and a 6.5$\times$ speedup in optimization convergence compared to finite-difference baselines, enabling the robust design of giant anisotropy in regimes where traditional methods fail.

physics.optics↗

arXiv2026

Pearcey-Inspired Quartic Wavefront Shaping for Obstructed Near-Field Multi-User Communications

Radiative near-field (RNF) beamforming is vulnerable to blockages that disrupt Fresnel zones. This letter proposes an obstruction-unaware wavefront shaping strategy inspired by catastrophe optics. By superimposing a calibrated quartic phase, we generate a Pearcey-like wave packet that exhibits structural stability against perturbations. We establish a fair comparison protocol where the quartic beam is calibrated in free space to avoid exploiting obstruction knowledge. Numerical results demonstrate up to 8.5~dB SINR gain over conventional focusing for multi-user scenarios near the depth-of-focus limit. Crucially, this gain stems from improved channel conditioning under partial blockage, which mitigates the severe noise amplification inherent to zero-forcing precoding.

physics.optics↗

arXiv2026

Airy Beamforming for Radiative Near-Field MU-XL-MIMO: Overcoming Half-Space Blockage

The move to next-generation wireless communications with extremely large-scale antenna arrays (ELAAs) brings the communications into the radiative near-field (RNF) region, where distance-aware focusing is feasible. However, high-frequency RNF links are highly vulnerable to blockage in indoor environments dominated by half-space obstacles (walls, corners) that create knife-edge shadows. Conventional near-field focused beams offer high gain in line-of-sight (LoS) scenarios but suffer from severe energy truncation and effective-rank collapse in shadowed regions, often necessitating the deployment of auxiliary hardware such as Reconfigurable Intelligent Surfaces (RIS) to restore connectivity. We propose a beamforming strategy that exploits the auto-bending property of Airy beams to mitigate half-space blockage without additional hardware. The Airy beam is designed to ``ride'' the diffraction edge, accelerating its main lobe into the shadow to restore connectivity. Our contributions are threefold: (i) a Green's function-based RNF multi-user channel model that analytically reveals singular-value collapse behind knife-edge obstacles; (ii) an Airy analog beamforming scheme that optimizes the bending trajectory to recover the effective channel rank; and (iii) an Airy null-steering method that aligns oscillatory nulls with bright-region users to suppress interference in mixed shadow/bright scenarios. Simulations show that the proposed edge-riding Airy strategy achieves a Signal-to-Noise Ratio (SNR) improvement of over 20 dB and restores full-rank connectivity in shadowed links compared to conventional RNF focusing, virtually eliminating outage in geometric shadows and increasing multi-user spectral efficiency by approximately 35\% under typical indoor ELAA configurations. These results demonstrate robust RNF multi-user access in half-space blockage scenarios without relying on RIS.

cs.IT↗

arXiv2025

Non-trivial Bifocal and Optical Vortex Generation of DNG Materials Unveiled by Generalized Transfer Matrix Method and Matrix Fourier Optics

The investigation and analysis of exotic physical phenomena facilitated by metamaterials have emerged as a compelling area of interest in physics, material science, and engineering. However, there remains a lack of suitable theoretical tools for scrutinizing light-matter interactions in metamedia, particularly those involving distinct constitutive tensors, such as polarization cross-coupling or non-reciprocal propagation induced by bianisotropy. In this research, we apply the generalized transfer matrix method and matrix Fourier Optics to devise a novel hybrid methodology adept at efficiently tracing an optical beam as it propagates through transverse-homogeneous, longitudinal-inhomogeneous bianisotropic media. Our proof-of-concept demonstration elucidates the interaction between a nonparaxial Gaussian incident beam with various linear/circular polarizations and a mismatched anisotropic double-negative (DNG) metamaterial. We unveil the nontrivial bifocal effect and optical vortex (OV) generation and focusing phenomena in a three-dimensional environment, findings that have not been documented previously. Significantly, the proposed efficient methodology holds the potential for application in examining more promising physical phenomena exhibited by intricate metamaterials, enabling a more visually rigorous approach.

physics.optics↗

arXiv2023

Total variation distance between a jump-equation and its Gaussian approximation

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the approximate random variable $X^\varepsilon\_t$ converges to the original random variable $X\_t$ in total variation distance and we estimate the error. We also give an estimate of the distance between the densities of the laws of the two random variables. These are done by using some integration by parts techniques in Malliavin calculus.

math.PR↗

arXiv2023

Approximation schemes for McKean-Vlasov and Boltzmann type equations (error analyses in total variation distance)

We deal with Mckean-Vlasov and Boltzmann type jump equations. This means that the coefficients of the stochastic equation depend on the law of the solution, and the equation is driven by a Poisson point measure with intensity measure which depends on the law of the solution as well. In [3], Alfonsi and Bally have proved that under some suitable conditions, the solution $X_t$ of such equation exists and is unique. One also proves that $X_t$ is the probabilistic interpretation of an analytical weak equation. Moreover, the Euler scheme $X_t^{\mathcal{P}}$ of this equation converges to $X_t$ in Wasserstein distance. In this paper, under more restricted assumptions, we show that the Euler scheme $X_t^{\mathcal{P}}$ converges to $X_t$ in total variation distance and $X_t$ has a smooth density (which is a function solution of the analytical weak equation). On the other hand, in view of simulation, we use a truncated Euler scheme $X^{\mathcal{P},M}_t$ which has a finite numbers of jumps in any compact interval. We prove that $X^{\mathcal{P},M}_{t}$ also converges to $X_t$ in total variation distance. Finally, we give an algorithm based on a particle system associated to $X^{\mathcal{P},M}_t$ in order to approximate the density of the law of $X_t$. Complete estimates of the error are obtained.

math.PR↗

arXiv2023

Approximation for the invariant measure with applications for jump processes (convergence in total variation distance)

In this paper, we establish an abstract framework for the approximation of the invariant probability measure for a Markov semigroup. Following Pag{è}s and Panloup [40] we use an Euler scheme with decreasing step (unadjusted Langevin algorithm). Under some contraction property with exponential rate and some regularization properties, we give an estimate of the error in total variation distance. This abstract framework covers the main results in [40] and [14]. As a specific application we study the convergence in total variation distance to the invariant measure for jump type equations. The main technical difficulty consists in proving the regularzation properties-this is done under an ellipticity condition, using Malliavin calculus for jump processes.

math.PR↗

arXiv2023

Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design

Conventional meta-atom designs rely heavily on researchers' prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by Generative Adversarial Networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameter requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory. By learning the Markov process that transforms the original structure into a Gaussian distribution, the proposed method can gradually remove the noise starting from the Gaussian distribution and generate new high-degree-of-freedom meta-atoms that meet S-parameter conditions, which avoids the model instability introduced by the adversarial training process of GANs and ensures more accurate and high-quality generation results. Experiments have proven that our method is superior to representative methods of GANs in terms of model convergence speed, generation accuracy, and quality.

cs.LG↗

arXiv2022

Total variation distance between a jump-equation and its Gaussian approximation

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the approximate random variable $X^\varepsilon_t$ converges to the original random variable $X_t$ in total variation distance and we estimate the error. We also give an estimate of the distance between the densities of the laws of the two random variables. These are done by using some integration by parts techniques in Malliavin calculus.

math.PR↗