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Lihao Zhang

Publications and source records attributed to Lihao Zhang.

At least 19 recordsLinked to original sources
arXiv2026

RadioSight: Predictive mmWave XR Network Optimization from Dynamic Neural Radio Fields

Next-generation extended reality (XR) networks rely on mmWave communication for multi-gigabit throughput, yet highly directional links are vulnerable to user mobility and blockages, causing frequent outages under reactive beam management. Emerging neural radio fields can predict radio propagation, but prior work remains limited to offline channel reconstruction. We introduce RadioSight, a real-time multi-modal radio field system for predictive mmWave optimization and proactive Multi-User MIMO beamforming. RadioSight combines backward beam-tracing with real-time semantic object synchronization to anticipate RF geometry changes without full model retraining. Implemented as an edge-executable pipeline for commercial 28 GHz arrays, RadioSight determines each scheduling window's beams during the preceding window without exhaustive beam sweeps. Experiments show that RadioSight reduces beam-search error by up to ~50%, improves median throughput by 2x, and enhances link stability.

cs.NI↗
arXiv2026

Real-Time Cross-Layer Semantic Error Correction Using Language Models and Software-Defined Radio

As Language Models (LMs) advance, Semantic Error Correction (SEC) has emerged as a promising approach for reliable network designs. Yet existing methods prioritize intent over accuracy, falling short of verbatim recovery. Our recent work, Cross-Layer SEC (CL-SEC), addressed this by fusing physical-layer Log-Likelihood Ratios (LLRs) with semantic context, but its real-time feasibility remained unvalidated. This paper demonstrates CL-SEC on a live Software-Defined Radio (SDR) testbed, resolving implementation barriers with: 1) an SDR middleware enabling real-time LLR extraction from FPGA hardware, and 2) a generalized inference interface supporting modern encoder-decoder LMs. Real-world experiments confirm that the cross-layer fusion significantly outperforms either source alone.

cs.NI↗
arXiv2026

CL-SEC: Cross-Layer Semantic Error Correction Empowered by Language Models

Achieving reliable communication has long been a fundamental challenge in networked systems. Semantic Error Correction (SEC) leverages the semantic understanding capabilities of language models (LMs) to perform application-layer error correction, complementing conventional channel decoding. While promising, existing SEC approaches rely solely on context captured by LMs at the application layer, ignoring the rich information available at the physical layer. To address this limitation, this paper introduces Cross-Layer SEC (CL-SEC), an LM-empowered error correction framework that integrates cross-layer information from both the physical and application layers to jointly correct corrupted words in text communication. Using a Bayesian combination in product form tailored to this framework, CL-SEC achieves significantly improved performance over methods that process information in isolated layers. CL-SEC shows substantial gains across multiple error-correction metrics, including bit-error rate, word-error rate, and semantic fidelity scores. Importantly, unlike most semantic communication systems that focus solely on recovering the semantic meaning of transmitted messages, CL-SEC aims to reconstruct the original transmitted message verbatim, leveraging the semantic understanding capabilities of LMs for precise reconstruction.

cs.IT↗
arXiv2025

Topological Valley Transport in Bilayer Graphene Induced by Interlayer Sliding

Interlayer sliding, together with twist angle, is a crucial parameter that defines the atomic registry and thus determines the properties of two-dimensional (2D) material homobilayers. Here, we theoretically demonstrate that controlled interlayer sliding in bilayer graphene induces Berry curvature reversals, leading to topological states confined within a one-dimensional moir\'e channel. We experimentally realize interlayer sliding by bending the bilayer graphene geometry across a nanoridge. Systematic electronic transport measurements reveal topological valley transport when the Fermi energy resides within the band gap, consistent with theoretical predictions of eight topological channels. Our findings establish interlayer sliding as a powerful tool for tuning the electronic properties of bilayer graphene and underscore its potential for broad application across 2D material systems.

cond-mat.mes-hall↗
arXiv2025

A Hybrid TDMA/CSMA Protocol for Time-Sensitive Traffic in Robot Applications

Recent progress in robotics has underscored the demand for real-time control in applications such as manufacturing, healthcare, and autonomous systems, where the timely delivery of mission-critical commands under heterogeneous robotic traffic is paramount for operational efficacy and safety. In these scenarios, mission-critical traffic follows a strict deadline-constrained communication pattern: commands must arrive within defined QoS deadlines, otherwise late arrivals can degrade performance or destabilize control loops.In this work, we demonstrate on a real-time SDR platform that CSMA, widely adopted in robotic communications,suffers severe degradation under high robot traffic loads, with contention-induced collisions and delays disrupting the on-time arrival of mission-critical packets. To address this problem, we propose an IEEE 802.11-compatible hybrid TDMA/CSMA protocol that combines TDMA's deterministic slot scheduling with CSMA's adaptability for heterogeneous robot traffic.The protocol achieves collision-free, low-latency mission-critical command delivery and IEEE 802.11 compatibility through the synergistic integration of sub-microsecond PTP-based slot synchronization-essential for establishing precise timing for TDMA, a three-session superframe with dynamic TDMA allocation for structured and adaptable traffic management,and beacon-NAV protection to preemptively secure these critical communication sessions from interference. Emulation experiments on real-time SDR testbed and Robot Operating System (ROS) simulation show that the proposed protocol reduces missed-deadline errors by 93% compared to the CSMA baseline. In high-speed robot path-tracking ROS simulations, the protocol lowers Root Mean Square (RMS) trajectory error by up to 90% compared with a CSMA baseline, all while maintaining throughput for non-critical traffic within +-2%.

cs.RO↗
arXiv2025

RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting

In the 6G era, the demand for higher system throughput and the implementation of emerging 6G technologies require large-scale antenna arrays and accurate spatial channel state information (Spatial-CSI). Traditional channel modeling approaches, such as empirical models, ray tracing, and measurement-based methods, face challenges in spatial resolution, efficiency, and scalability. Radiance field-based methods have emerged as promising alternatives but still suffer from geometric inaccuracy and costly supervision. This paper proposes RF-PGS, a novel framework that reconstructs high-fidelity radio propagation paths from only sparse path loss spectra. By introducing Planar Gaussians as geometry primitives with certain RF-specific optimizations, RF-PGS achieves dense, surface-aligned scene reconstruction in the first geometry training stage. In the subsequent Radio Frequency (RF) training stage, the proposed fully-structured radio radiance, combined with a tailored multi-view loss, accurately models radio propagation behavior. Compared to prior radiance field methods, RF-PGS significantly improves reconstruction accuracy, reduces training costs, and enables efficient representation of wireless channels, offering a practical solution for scalable 6G Spatial-CSI modeling.

cs.CV↗
arXiv2025

VeriRAG: A Retrieval-Augmented Framework for Automated RTL Testability Repair

Large language models (LLMs) have demonstrated immense potential in computer-aided design (CAD), particularly for automated debugging and verification within electronic design automation (EDA) tools. However, Design for Testability (DFT) remains a relatively underexplored area. This paper presents VeriRAG, the first LLM-assisted DFT-EDA framework. VeriRAG leverages a Retrieval-Augmented Generation (RAG) approach to enable LLM to revise code to ensure DFT compliance. VeriRAG integrates (1) an autoencoder-based similarity measurement model for precise retrieval of reference RTL designs for the LLM, and (2) an iterative code revision pipeline that allows the LLM to ensure DFT compliance while maintaining synthesizability. To support VeriRAG, we introduce VeriDFT, a Verilog-based DFT dataset curated for DFT-aware RTL repairs. VeriRAG retrieves structurally similar RTL designs from VeriDFT, each paired with a rigorously validated correction, as references for code repair. With VeriRAG and VeriDFT, we achieve fully automated DFT correction -- resulting in a 7.72-fold improvement in successful repair rate compared to the zero-shot baseline (Fig. 5 in Section V). Ablation studies further confirm the contribution of each component of the VeriRAG framework. We open-source our data, models, and scripts at https://github.com/HarminChee/VeriRAG.

cs.AR↗
arXiv2025

Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field

Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly from lower-frequency bands due to severe free space path loss, minimal diffraction and specular reflection, and prominent scattering, making conventional channel modeling and pilot-based estimation approaches inefficient. In this work, we investigate the feasibility of applying radio radiance field (RRF) framework to the THz band. This method reconstructs a continuous RRF using visual-based geometry and sparse THz RF measurements, enabling efficient spatial channel state information (Spatial-CSI) modeling without dense sampling. We first build a fine simulated THz scenario, then we reconstruct the RRF and evaluate the performance in terms of both reconstruction quality and effectiveness in THz communication, showing that the reconstructed RRF captures key propagation paths with sparse training samples. Our findings demonstrate that RRF modeling remains effective in the THz regime and provides a promising direction for scalable, low-cost spatial channel reconstruction in future 6G networks.

eess.SP↗
arXiv2025

Bias voltage controlled inversions of tunneling magnetoresistance in van der Waals heterostructures Fe3GaTe2/hBN/Fe3GaTe2

We report the bias voltage controlled inversions of tunneling magnetoresistance (TMR) in magnetic tunnel junctions composed of Fe3GaTe2 electrodes and hBN tunneling barrier, observed at room temperature. The polarity reversal of TMR occurs consistently at around 0.625 V across multiple devices and temperatures, highlighting the robustness of the effect. To understand this behavior, we developed a theoretical model incorporating spin-resolved density of states (DOS) at high energy levels. By adjusting the DOS weighting at different k points to account for misalignment between the crystal structure of electrodes in experimental devices, we improved agreement between experimental and theoretical inversion voltages. Our results provide valuable insight into the voltage-controlled spin injection and detection in two-dimensional magnetic tunnel junctions, with implications for the development of energy-efficient spintronic devices.

cond-mat.mes-hall↗
arXiv2024

Bias Voltage Driven Tunneling Magnetoresistance Polarity Reversal in 2D Stripy Antiferromagnet CrOCl

Atomically thin materials with coupled magnetic and electric polarization are critical for developing energy-efficient and high-density spintronic devices, yet they remain scarce due to often conflicting requirements of stabilizing both magnetic and electric orders. The recent discovery of the magnetoelectric effect in the 2D stripy antiferromagnet CrOCl highlights this semiconductor as a promising platform to explore electric field effects on magnetoresistance. In this study, we systematically investigate the magnetoresistance in tunneling junctions of bilayer and monolayer CrOCl. We observe that the transition from antiferromagnetic to ferrimagnetic phases in both cases induces a positive magnetoresistance at low bias voltages, which reverses to a negative value at higher bias voltages. This polarity reversal is attributed to the additional electric dipoles present in the antiferromagnetic state, as supported by our theoretical calculations. These findings suggest a pathway for the electric control of spintronic devices and underscore the potential of 2D magnets like CrOCl in advancing energy-efficient spintronic applications.

cond-mat.mes-hall↗
arXiv2024

RF-3DGS: Wireless Channel Modeling with Radio Radiance Field and 3D Gaussian Splatting

Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challenges for real-time applications. Inspired by the newly proposed 3D Gaussian Splatting method in the computer vision domain, which outperforms other methods in reconstructing optical radiance fields, we propose RF-3DGS, a novel approach that enables precise site-specific reconstruction of radio radiance fields from sparse samples. RF-3DGS can render radio spatial spectra at arbitrary positions within 2 ms following a brief 3-minute training period, effectively identifying dominant propagation paths. Furthermore, RF-3DGS can provide fine-grained Spatial Channel State Information (Spatial-CSI) of these paths, including the channel gain, the delay, the angle of arrival (AoA), and the angle of departure (AoD). Our experiments, calibrated through real-world measurements, demonstrate that RF-3DGS not only significantly improves reconstruction quality, training efficiency, and rendering speed compared to state-of-the-art methods, but also holds great potential for supporting wireless communication and advanced applications such as Integrated Sensing and Communication (ISAC). Code and dataset will be available at https://github.com/SunLab-UGA/RF-3DGS.

cs.NI↗
arXiv2024

Magnetoresistance oscillations in vertical junctions of 2D antiferromagnetic semiconductor CrPS$_4$

Magnetoresistance (MR) oscillations serve as a hallmark of intrinsic quantum behavior, traditionally observed only in conducting systems. Here we report the discovery of MR oscillations in an insulating system, the vertical junctions of CrPS$_4$ which is a two dimensional (2D) A-type antiferromagnetic semiconductor. Systematic investigations of MR peaks under varying conditions, including electrode materials, magnetic field direction, temperature, voltage bias and layer number, elucidate a correlation between MR oscillations and spin-canted states in CrPS$_4$. Experimental data and analysis point out the important role of the in-gap electronic states in generating MR oscillations, and we proposed that spin selected interlayer hopping of localized defect states may be responsible for it. Our findings not only illuminate the unusual electronic transport in CrPS$_4$ but also underscore the potential of van der Waals magnets for exploring interesting phenomena.

cond-mat.mes-hall↗
arXiv2024

Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin

As 5G technology becomes increasingly established, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. However, efficient management method of the large-scale antenna arrays deployed by those radio technologies is crucial. Traditional management methods are mainly reactive, usually based on feedback from users to adapt to the dynamic wireless channel. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is an all-inclusive channel characterization and consists of all the feasible line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with the three-dimension (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further look into the possibility of holographic communication, which implies complete control over every aspect of the radio waves emitted. Based on the integration of holographic communication and digital twin, we proposed a new framework, digital radio twin, which takes advantages from both the digital world and deterministic control over radio waves, supporting a wide range of high-level applications. As a preliminary attempt towards this visionary direction, in this paper, we explore the use of generative artificial intelligence (AI) to pinpoint the valid paths in a given environment, demonstrating promising results, and highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies.

cs.IT↗
arXiv2024

Room-temperature intrinsic ferromagnetism of two-dimensional Na2Cl crystals originated by s- and p-orbitals

Ferromagnetism, as one of the most valuable properties of materials, has attracted sustained and widespread interest in basic and applied research from ancient compasses to modern electronic devices. Traditionally, intrinsic ferromagnetism has been attributed to the permanent magnetic moment induced by partially filled d- or f-orbitals. However, the development of ferromagnetic materials has been limited by this electronic structure convention. Thus, the identification of additional materials that are not constrained by this conventional rule but also exhibit intrinsic ferromagnetism is highly expected and may impact all the fields based on ferromagnetism. Here, we report the direct observation of room-temperature intrinsic ferromagnetism in two-dimensional (2D) Na2Cl crystals, in which there are only partially filled s- and p-orbitals rather than d- or f-orbitals, using the superconducting quantum interference device (SQUID) and magnetic force microscope (MFM). These Na2Cl crystals formed in reduced graphene oxide (rGO) membranes have an unconventional stoichiometric structure leading to unique electron and spin distributions. And the structure of these 2D Na2Cl crystals, including the Na and Cl sites, is characterized in situ for the first time and directly observed by cryo-electron microscopy (cryo-EM) based on the observed difference in contrast between Na stacked with Cl and single Na. These findings break the conventional rule of intrinsic ferromagnetism and provide new insights into the design of novel magnetic and electronic devices and transistors with a size down to the atomic scale.

cond-mat.mtrl-sci↗
arXiv2023

WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differentiable Ray-Tracing

The accurate modeling of indoor radio propagation is crucial for localization, monitoring, and device coordination, yet remains a formidable challenge, due to the complex nature of indoor environments where radio can propagate along hundreds of paths. These paths are resulted from the room layout, furniture, appliances and even small objects like a glass cup. They are also influenced by the object material and surface roughness. Advanced machine learning (ML) techniques have the potential to take such non-linear and hard-to-model factors into consideration. However, extensive and fine-grained datasets are urgently required. This paper presents WiSegRT, an open-source dataset for indoor radio propagation modeling. Generated by a differentiable ray tracer within the segmented 3-dimensional (3D) indoor environments, WiSegRT provides site-specific channel impulse responses for each grid point relative to the given transmitter location. We expect WiSegRT to support a wide-range of applications, such as ML-based channel prediction, accurate indoor localization, radio-based object detection, wireless digital twin, and more.

cs.IT↗
arXiv2023

Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

cs.NI↗
arXiv2022

The Tensor Data Platform: Towards an AI-centric Database System

Database engines have historically absorbed many of the innovations in data processing, adding features to process graph data, XML, object oriented, and text among many others. In this paper, we make the case that it is time to do the same for AI -- but with a twist! While existing approaches have tried to achieve this by integrating databases with external ML tools, in this paper we claim that achieving a truly AI-centric database requires moving the DBMS engine, at its core, from a relational to a tensor abstraction. This allows us to: (1) support multi-modal data processing such as images, videos, audio, text as well as relational; (2) leverage the wellspring of innovation in HW and runtimes for tensor computation; and (3) exploit automatic differentiation to enable a novel class of "trainable" queries that can learn to perform a task. To support the above scenarios, we introduce TDP: a system that builds upon our prior work mapping relational queries to tensors. Thanks to a tighter integration with the tensor runtime, TDP is able to provide a broader coverage of new emerging scenarios requiring access to multi-modal data and automatic differentiation.

cs.DB↗
arXiv2022

A Just-In-Time Networking Framework for Minimizing Request-Response Latency of Wireless Time-Sensitive Applications

This paper puts forth a networking paradigm, referred to as just-in-time (JIT) communication, to support client-server applications with stringent request-response latency requirement. Of interest is not just the round-trip delay of the network, but the actual request-response latency experienced by the application. The JIT framework contains two salient features. At the client side, the communication layer will 'pull' a request from the client just when there is an upcoming transmission opportunity from the network. This ensures that the request contains information that is as fresh as possible (e.g., a sensor reading obtained just before the transmission opportunity). At the server side, the network ascertains that the server, after receiving and processing the request to generate a response (e.g., a control command to be sent to the client), will have a transmission opportunity at just this time. We realize the JIT system, including the protocol stack, over a Time-Division-Multiple-Access (TDMA) network implemented on a System-on-Chip (SoC) platform. We prove that a TDMA network with a power-of-2 time slots per superframe is optimal for realizing the server-side JIT function. Our experimental results validate that JIT networks can yield significantly lower request-response latency than networks without JIT support can.

cs.NI↗