arXiv subjects

Shutao Zhang

Publications and source records attributed to Shutao Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting

Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), which models the channel multipath angular power spectrum (APS) from the reference signal received power (RSRP) measurement, has emerged as a state-of-the-art method tailored for efficient network optimization. However, despite its effectiveness, LSCM cannot predict APS at the vast majority of locations where no measurements are available, which significantly restricts its applicability in large-scale, real-world scenarios. To address this challenge, we present point-cloud-assisted tangent Gaussian splatting (PC-TGS), the first framework to extrapolate APS to unmeasured outdoor grids by integrating sparse radio measurements with dense LiDAR-based geometry. PC-TGS represents environmental scatterers as anisotropic 3D Gaussians, initialized and refined through a relaxed-mean reparameterization of the raw point cloud. A tangent-plane projection accurately maps each Gaussian into the local angular domain, while a depth-aware electromagnetic splatting process aggregates their contributions. To ensure practical deployment, we derive a closed-form Gaussian-weighted average (GWA) for APS bin integration and provide a provable error bound. { Evaluations on a LiDAR-scanned city-scale dataset (5M points, 6,310 RSRP samples) demonstrate that PC-TGS achieves better APS and RSRP prediction performance compared to state-of-the-art baselines and faster inference time for APS extrapolation task. These results highlight the potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.

eess.SP↗

arXiv2026

Engineering Hybrid Resonances in Nanophotonics

Hybridization of resonances is known to overcome inherent limitations of individual systems, enabling advanced functionalities and applications. Here we discuss hybrid plasmonic-Mie resonators that emerged recently as a promising direction in advancing nanophotonic structures by synergistically combining the strong near-field enhancement of plasmonic components with the low-loss, multipolar resonances of dielectric Mie elements. We review the recent progress in the field, encompassing the fundamental physical principles, structural design strategies, material platforms, computational optimization approaches, and representative device implementations. Our discussion starts by evaluating the complementary characteristics of plasmonic and Mie resonances followed by a description of the coupling between these resonances in order to boost light-matter interactions. Afterward, we explore the performance of efficient hybrid resonators for different application areas. Apart from the conventional metal-dielectric systems, we consider the recent class of epsilon-near-zero (ENZ) materials, which can provide unique advantages in terms of field localization, phase engineering, and energy flow management in the vicinity of zero-permittivity conditions, offering more flexibility in designing hybrid nano-optical devices. Lastly, we point out potential research avenues aiming to improve functional and efficient nanophotonic devices, especially those involving emerging topological material systems, such as Sb2Te3, Bi2Te3, Bi2Se3, combining plasmonic amplification, dielectric confinement, and spin-dependent optical behavior.

physics.optics↗

arXiv2026

Toward Distributed User Scheduling and Coordinated Beamforming in Multi-Cell mmWave Networks: A Sensing-Assisted Framework

Providing guaranteed quality of service for cell-edge users remains a longstanding challenge in wireless networks. While coordinated interference management was proposed decades ago, its potential has been limited by computational complexity and backhaul resource constraints. Distributed user scheduling and coordinated beamforming (D-USCB) offers a scalable solution but faces practical challenges in acquiring inter-cell channel state information (CSI), as base stations (BSs) are often restricted to signal strength measurements, and high-dimensional CSI exchange incurs substantial overhead. Inspired by integrated sensing and communication (ISAC), this paper proposes a sensing-assisted D-USCB (SD-USCB) framework to maximize the network throughput of multi-cell mmWave networks. Firstly, the framework leverages channel knowledge maps (CKMs) that map user locations to CSI estimates, where user locations are proactively sensed via ISAC echoes. Secondly, we employ a signal-to-average-leakage-plus-interference-plus-noise ratio (SALINR) metric for distributed ISAC beamforming optimization, in which BSs simultaneously communicate with users and sense their locations. These two components jointly enable distributed coordinated transmission with only user location information exchanged among BSs, thereby substantially reducing backhaul overhead. In addition, we devise efficient distributed user scheduling and ISAC beamforming algorithms to jointly optimize communication and sensing performance. Extensive numerical results demonstrate significant improvements in network throughput, validating the efficacy of the proposed framework.

eess.SP↗

arXiv2025

Iterated Agent for Symbolic Regression

Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpretable models. This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search. By generating candidate expressions guided by natural-language rationales, our method biases discovery towards models that are not only accurate but also conceptually coherent and interpretable. We demonstrate IdeaSearchFitter's efficacy across diverse challenges: it achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines; discovers mechanistically aligned models with good accuracy-complexity trade-offs on real-world data; and derives compact, physically-motivated parametrizations for Parton Distribution Functions in a frontier high-energy physics application. IdeaSearchFitter is a specialized module within our broader iterated agent framework, IdeaSearch, which is publicly available at https://www.ideasearch.cn/.

physics.comp-ph↗

arXiv2025

Collins-type fragmentation energy correlator in semi-inclusive deep inelastic lepton-hadron scattering

We initiate a systematic study of fragmentation energy correlators (FECs), which generalize traditional fragmentation functions and encode non-perturbative information about transverse dynamics in parton fragmentation processes. We define boost-invariant, non-perturbative FECs and derive a corresponding collinear factorization formula. A spin decomposition of the FECs is carried out, analogous to that of transverse-momentum-dependent fragmentation functions. In this work we focus particularly on the Collins-type quark FEC, which is sensitive to chiral symmetry breaking and characterizes the azimuthal asymmetry in the fragmentation of a transversely polarized quark. We perform a next-to-leading-order calculation of the corresponding hard coefficient in semi-inclusive deep-inelastic scattering for the quark non-singlet component, thereby validating the consistency of our theoretical framework.

hep-ph↗

arXiv2025

RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization

Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localized statistical channel modeling (LSCM) is the state-of-the-art channel modeling framework tailored for cellular network optimization. However, traditional LSCM methods, which infer the channel's Angular Power Spectrum (APS) from Reference Signal Received Power (RSRP) measurements, suffer from critical limitations: they are typically confined to single-cell, single-grid and single-carrier frequency analysis and fail to capture complex cross-domain interactions. To overcome these challenges, we propose RF-LSCM, a novel framework that models the channel APS by jointly representing large-scale signal attenuation and multipath components within a radiance field. RF-LSCM introduces a multi-domain LSCM formulation with a physics-informed frequency-dependent Attenuation Model (FDAM) to facilitate the cross frequency generalization as well as a point-cloud-aided environment enhanced method to enable multi-cell and multi-grid channel modeling. Furthermore, to address the computational inefficiency of typical neural radiance fields, RF-LSCM leverages a low-rank tensor representation, complemented by a novel Hierarchical Tensor Angular Modeling (HiTAM) algorithm. This efficient design significantly reduces GPU memory requirements and training time while preserving fine-grained accuracy. Extensive experiments on real-world multi-cell datasets demonstrate that RF-LSCM significantly outperforms state-of-the-art methods, achieving up to a 30% reduction in mean absolute error (MAE) for coverage prediction and a 22% MAE improvement by effectively fusing multi-frequency data.

cs.LG↗

arXiv2025

A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling

Localized statistical channel modeling (LSCM) is crucial for effective performance evaluation in digital twin-assisted network optimization. Solely relying on the multi-beam reference signal receiving power (RSRP), LSCM aims to model the localized statistical propagation environment by estimating the channel angular power spectrum (APS). However, existing methods rely heavily on drive test data with high collection costs and limited spatial coverage. In this paper, we propose a measurement report (MR) data-driven framework for LSCM, exploiting the low-cost and extensive collection of MR data. The framework comprises two novel modules. The MR localization module addresses the issue of missing locations in MR data by introducing a semi-supervised method based on hypergraph neural networks, which exploits multi-modal information via distance-aware hypergraph modeling and hypergraph convolution for location extraction. To enhance the computational efficiency and solution robustness, LSCM operates at the grid level. Compared to independently constructing geographically uniform grids and estimating channel APS, the joint grid construction and channel APS estimation module enhances robustness in complex environments with spatially non-uniform data by exploiting their correlation. This module alternately optimizes grid partitioning and APS estimation using clustering and improved sparse recovery for the ill-conditioned measurement matrix and incomplete observations. Through comprehensive experiments on a real-world MR dataset, we demonstrate the superior performance and robustness of our framework in localization and channel modeling.

eess.SP↗

arXiv2025

Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling

This paper presents MM-LSCM, a self-supervised multi-modal neural radio radiance field framework for localized statistical channel modeling (LSCM) for next-generation network optimization. Traditional LSCM methods rely solely on RSRP data, limiting their ability to model environmental structures that affect signal propagation. To address this, we propose a dual-branch neural architecture that integrates RSRP data and LiDAR point cloud information, enhancing spatial awareness and predictive accuracy. MM-LSCM leverages volume-rendering-based multi-modal synthesis to align radio propagation with environmental obstacles and employs a self-supervised training approach, eliminating the need for costly labeled data. Experimental results demonstrate that MM-LSCM significantly outperforms conventional methods in channel reconstruction accuracy and robustness to noise, making it a promising solution for real-world wireless network optimization.

eess.SP↗

arXiv2025

Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery

Efficient quantum state preparation remains a central challenge in first-principles quantum simulations of dynamics in quantum field theories, where the Hilbert space is intrinsically infinite-dimensional. Here, we introduce a large language model (LLM)-assisted framework for quantum-circuit design that systematically scales state-preparation circuits to large lattice volumes. Applied to a 1+1d XY spin chain, the LLM autonomously discovers a compact 4-parameter circuit that captures boundary-induced symmetry breaking with sub-percent energy deviation, enabling successful validation on the \texttt{Zuchongzhi} quantum processor. Guided by this insight, we extend the framework to 2+1d quantum field theories, where scalable variational ans\"atze have remained elusive. For a scalar field theory, the search yields a symmetry-preserving, 3-parameter shallow-depth ansatz whose optimized parameters converge to size-independent constants for lattices $n \ge 4$, providing, to our knowledge, the first scalable ansatz for this class of 2+1d models. Our results establish a practical route toward AI-assisted, human-guided discovery in quantum simulation.

quant-ph↗

arXiv2025

PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed evaluation items. These deficiencies necessitate more rigorous assessment methods. To address these limitations, we introduce PHYBench, a benchmark of 500 original physics problems ranging from high school to Physics Olympiad difficulty. PHYBench addresses data contamination through original content and employs a systematic curation pipeline to eliminate flawed items. Evaluations show that PHYBench activates more tokens and provides stronger differentiation between reasoning models compared to other baselines like AIME 2024, OlympiadBench and GPQA. Even the best-performing model, Gemini 2.5 Pro, achieves only 36.9% accuracy compared to human experts' 61.9%. To further enhance evaluation precision, we introduce the Expression Edit Distance (EED) Score for mathematical expression assessment, which improves sample efficiency by 204% over binary scoring. Moreover, PHYBench effectively elicits multi-step and multi-condition reasoning, providing a platform for examining models' reasoning robustness, preferences, and deficiencies. The benchmark results and dataset are publicly available at https://www.phybench.cn/.

cs.CL↗

arXiv2025

Enabling Highly Efficient Infrared Silicon Photodetectors via Disordered Metasurfaces with Upconversion Nanoparticles

Silicon photodetectors are highly desirable for their CMOS compatibility, low cost, and fast response speed. However, their application the infrared (IR) is limited by silicon's intrinsic bandgap, which restricts its detection to photons with wavelengths shorter than 1100 nm. Although several methods have been developed to extend silicon photodetectors further in the IR range, these approaches often introduce additional challenges, such as increased fabrication complexity and compatibility issues with standard CMOS processes. Here, we present an approach to overcome these limitations by integrating disordered metasurfaces with upconversion nanoparticles (UCNPs), enabling IR detection by silicon photodetectors. The disordered design consists of hybrid Mie-plasmonic cavities, which can enhance both the near-field localization and wide-band light absorption from visible to IR, improving photocurrent conversion. Compared to ordered structures, the infrared absorption and near field of the highly disordered configuration are increased by 2.6-folds and 3.9-folds, respectively. UCNPs not only convert near-infrared photons into visible light but also enhance absorption in the mid-infrared range, thereby improving hot electron generation. The measured responsivity of the disordered element for 1550 nm laser is up to 0.22 A/W at room temperature, corresponding to an external quantum efficiency of 17.6%. Our design not only enhances the photocurrent performance significantly, but also extends the working wavelength of silicon photodetectors to IR wavelength, making them suitable for broad spectrum applications.

physics.optics↗

arXiv2025

RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation

A radiomap represents the spatial distribution of wireless signal strength, which is critical for applications like network optimization. However, constructing a radiomap relies on measuring radio signal power across the entire system, which is costly in outdoor environments due to large network scales. We present RadCloudSplat, a framework that extends 3D Gaussian Splatting (3DGS) to radio frequencies for efficient and accurate radiomap extrapolation from sparse measurements. RadCloudSplat models environmental scatterers and radio paths using 3D Gaussians, capturing key factors of radio wave propagation. It employs a relaxed-mean (RM) scheme to reparameterize the positions of 3D Gaussians from noisy and dense 3D point clouds. A camera-free 3DGS-based projection is proposed to map 3D Gaussians onto 2D radio beam patterns. Furthermore, a regularized loss function and recursive fine-tuning using highly structured sparse measurements in real-world settings are applied to ensure robust generalization. Experiments on synthetic and real-world data show state-of-the-art extrapolation accuracy and execution speed, solidifying the framework's credibility for real-world deployment.

eess.SP↗

arXiv2024

Chalcogenide Metasurfaces Enabling Ultra-Wideband Detectors from Visible to Mid-infrared

Thermoelectric materials can be designed to support optical resonances across multiple spectral ranges to enable ultra-wide band photodetection. For instance, antimony telluride (Sb2Te3) chalcogenide exhibits interband plasmonic resonances in the visible range and Mie resonances in the mid-infrared (mid-IR) range, while simultaneously possessing large thermoelectric Seebeck coefficients. In this paper, we designed and fabricated Sb2Te3 metasurface devices to achieve resonant absorption for enabling photodetectors operating across an ultra-wideband spectrum, from visible to mid-IR. Furthermore, relying on asymmetric Sb2Te3 metasurface, we demonstrated the thermoelectric photodetectors with polarization-selectivity. This work provides a potential platform towards the portable ultrawide band spectrometers at room temperature, for environmental sensing applications.

physics.optics↗

arXiv2024

System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation

Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3GPP. This paper presents a system-level simulation framework for NB-IoT networks to evaluate their performance. The system-level simulator is structured into four parts: initialization, pre-generation, main simulation loop, and post-processing. Additionally, three essential features are investigated to enhance coverage, support massive connections, and ensure low power consumption, respectively. Simulation results demonstrate that the cumulative distribution function curves of the signal-to-interference-and-noise ratio fully comply with industrial standards. Furthermore, the throughput performance explains how NB-IoT networks realize massive connections at the cost of data rate. This work highlights its practical utility and paves the way for developing NB-IoT networks.

cs.NI↗

arXiv2023

A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel's angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths' powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method.

eess.SP↗

arXiv2022

Reversed Hardy-Littlewood-Sobolev inequality on Heisenberg group $\mathbb{H}^n$ and CR sphere $\mathbb{S}^{2n+1}$

This paper is mainly devoted to the study of the reversed Hardy-Littlewood-Sobolev (HLS) inequality on Heisenberg group $\mathbb{H}^n$ and CR sphere $\mathbb{S}^{2n+1}$. First, we establish the roughly reversed HLS inequality and give a explicitly lower bound for the sharp constant. Then, the existence of the extremal functions with sharp constant is proved by subcritical approach and some compactness techniques. Our method is rearrangement free and can be applied to study the classical HLS inequality and other similar inequalities.

math.AP↗

arXiv2021

Selective quantum Zeno effect of ultracold atom-molecule scattering in dynamic magnetic fields

We demonstrated that final states of ultracold scattering between atom and molecule can be selectively produced using dynamic magnetic fields of multiple frequencies. The mechanism of the dynamic magnetic field control is based on a generalized quantum Zeno effect for the selected scattering channels. In particular, we use an atom-molecule spin flip scattering to show that the transition to the selected final spin projection of the molecule in the inelastic scattering can be suppressed by dynamic modulation of coupling between the Floquet engineered initial and final states.

quant-ph↗

arXiv2021

A Syntactic Approach to Studying Strongly Equivalent Logic Programs

In the field of Answer Set Programming (ASP), two logic programs are strongly equivalent if they are ordinarily equivalent under any extensions. This property provides a theoretical foundation for studying many aspects of logic programs such as program simplification and transformation etc. Therefore, strong equivalence has been investigated extensively for ASP and its extensions such as LPMLN. In this paper, we present a syntactic approach to studying the strong equivalence of logic programs, which provides several interesting results and would help us understand the strong equivalence from a new perspective. Firstly, we present the notions of independent sets and five kinds of syntactic transformations (S-* transformations) for logic programs. And we investigate the strong equivalence (SE) and non-strong equivalence (NSE) preserving properties of the S-* transformations in the contexts of ASP and LPMLN. Secondly, based on the properties of the S-* transformations, we present a fully automatic algorithm to discover syntactic conditions that preserve strong equivalences (SE-conditions) of ASP and LPMLN programs. To discover the SE-conditions efficiently, we present four kinds of approaches to improve the algorithm. Thirdly, we present a preliminary method to simplify the discovered SE-conditions and report the simplified SE-conditions of several kinds of LPMLN programs. After that, we present a discussion on the discovered SE-conditions and some existing problems. Finally, we present a comparison between SE-conditions discovering approaches in this paper and in the related work.

cs.LO↗