arXiv subjects

Xiaodong Xu

Publications and source records attributed to Xiaodong Xu.

At least 127 records · Page 7Linked to original sources

arXiv2024

Exciton Dressing by Extreme Nonlinear Magnons in a Layered Semiconductor

Collective excitations presenting nonlinear dynamics are fundamental phenomena with broad applications. A prime example is nonlinear optics, where diverse frequency mixing processes are central to communication, sensing, wavelength conversion, and attosecond physics. Leveraging recent progress in van der Waals magnetic semiconductors, we demonstrate nonlinear opto-magnonic coupling by presenting exciton states dressed by up to 20 harmonics of magnons, resulting from their nonlinearities, in the layered antiferromagnetic semiconductor CrSBr. We also create tunable optical side bands from sum- and difference-frequency generation between two optically bright magnon modes under symmetry breaking magnetic fields. Moreover, the observed difference-frequency generation mode can be continuously tuned into resonance with one of the fundamental magnons, resulting in parametric amplification of magnons. These findings realize the modulation of the optical frequency exciton with the extreme nonlinearity of magnons at microwave frequencies, which could find applications in magnonics and hybrid quantum systems, and provide new avenues for implementing opto-magnonic devices.

cond-mat.mes-hall↗

arXiv2024

Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission

In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we also design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (Deep JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. The proposed CLESC is also applicable to the transmission of other modal data. Simulation results demonstrate that the proposed CLESC effectively achieves compatibility and adaptation between semantic communication and traditional communication systems, improving both transmission efficiency and channel adaptability. Compared to traditional cross-layer transmission schemes, the CLESC framework can reduce bandwidth consumption by 85% while showing significant advantages under low signal-to-noise ratio (SNR) conditions.

eess.IV↗

arXiv2024

Reference Signal-Based Waveform Design for Integrated Sensing and Communications System

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We derive the closed-form expressions of Cramér-Rao Bound (CRB) for the distance and velocity estimations, and obtain the communication rate under the mean square error of channel estimation. Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values. To solve this problem, some numerical methods are typically adopted to obtain the optimal solutions, whose computational complexity grow exponentially with the number of symbols and subcarriers of OFDM. Therefore, we propose a relaxation and approximation method to transform the original discrete problem into a continuous convex one and obtain the sub-optimal solutions. Finally, our proposed scheme is compared with the exhaustive search method in numerical simulations, which show slight gap between the obtained sub-optimal and optimal solutions, and this gap further decreases with large weight factor.

eess.SP↗

arXiv2024

WDMoE: Wireless Distributed Mixture of Experts for Large Language Models

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but the role of wireless networks in supporting LLMs has not been thoroughly explored. In this paper, we propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks. Specifically, we decompose the MoE layer in LLMs by placing the gating network and the preceding neural network layer at BS, while distributing the expert networks among the devices. This deployment leverages the parallel inference capabilities of expert networks on mobile devices, effectively utilizing the limited computing and caching resources of these devices. Accordingly, we develop a performance metric for WDMoE-based LLMs, which accounts for both model capability and latency. To minimize the latency while maintaining accuracy, we jointly optimize expert selection and bandwidth allocation based on the performance metric. Moreover, we build a hardware testbed using NVIDIA Jetson kits to validate the effectiveness of WDMoE. Both theoretical simulations and practical hardware experiments demonstrate that the proposed method can significantly reduce the latency without compromising LLM performance.

cs.LG↗

arXiv2024

Intelligent acceleration adaptive control of linear $2\times2$ hyperbolic PDE systems

Traditional approaches to stabilizing hyperbolic PDEs, such as PDE backstepping, often encounter challenges when dealing with high-dimensional or complex nonlinear problems. Their solutions require high computational and analytical costs. Recently, neural operators (NOs) for the backstepping design of first-order hyperbolic partial differential equations (PDEs) have been introduced, which rapidly generate gain kernel without requiring online numerical solution. In this paper we apply neural operators to a more complex class of $2\times2$ hyperbolic PDE systems for adaptive stability control. Once the NO has been well-trained offline on a sufficient training set obtained using a numerical solver, the kernel equation no longer needs to be solved again, thereby avoiding the high computational cost during online operations.Specifically, we introduce the deep operator network (DeepONet), a neural network framework, to learn the nonlinear operator of the system parameters to the kernel gain. The approximate backstepping kernel is obtained by utilizing the network after learning, instead of numerically solving the kernel equations in the form of PDEs, to further derive the approximate controller and the target system. We analyze the existence and approximation of DeepONet operators and provide stability and convergence proofs for the closed-loop systems with NOs. Finally, the effectiveness of the proposed NN-adaptive control scheme is verified by comparative simulation, which shows that the NN operator achieved up to three orders of magnitude faster compared to conventional PDE solvers, significantly improving real-time control performance.

math.AP↗

arXiv2024

Optical Band Engineering of Monolayer WSe2 in a Scanning Tunneling Microscope

Intense electromagnetic fields can result in dramatic changes in the electronic properties of solids. These changes are commonly studied using optical probes of the modified electronic structure. Here we use optical-scanning tunneling microscopy (optical-STM) equipped with near-field continuous wave (CW) laser excitation to directly measure the electronic structure of light-dressed states in a monolayer transition metal dichalcogenide (TMD) semiconductor, WSe2. We find that the effective tunneling gap and tunneling density of states are strongly influenced by the intensity of the electromagnetic field when the applied field is resonant with the bandgap of the semiconductor. Our findings indicate that CW laser excitation can be used to generate light-dressed electronic states of quantum materials when confined strongly to the nanoscale.

cond-mat.mtrl-sci↗

arXiv2024

Revealing subterahertz atomic vibrations in quantum paraelectrics by surface-sensitive spintronic terahertz spectroscopy

Understanding surface collective dynamics in quantum materials is crucial for advancing quantum technologies. For example, surface phonon modes in quantum paraelectrics are thought to play an essential role in facilitating interfacial superconductivity. However, detecting these modes, especially below 1 terahertz (THz), is challenging due to limited sampling volumes and the need for high spectroscopic resolution. Here, we report surface soft transverse optical (TO1) phonon dynamics in KTaO3 and SrTiO3 by developing surface-sensitive spintronic THz spectroscopy that can sense the collective modes only a few nanometers deep from the surface. In KTaO3, the TO1 mode softens and sharpens with decreasing temperature, leveling off at 0.7 THz. In contrast, this mode in SrTiO3 broadens significantly below the quantum paraelectric crossover and coincides with the hardening of a sub-meV phonon mode related to the antiferrodistortive transition. These observations that deviate from their bulk properties may have implications for interfacial superconductivity and ferroelectricity. The developed technique opens opportunities for sensing low-energy surface excitations.

cond-mat.mtrl-sci↗

arXiv2024

Semantic Feature Decomposition based Semantic Communication System of Images with Large-scale Visual Generation Models

The end-to-end image communication system has been widely studied in the academic community. The escalating demands on image communication systems in terms of data volume, environmental complexity, and task precision require enhanced communication efficiency, anti-noise ability and semantic fidelity. Therefore, we proposed a novel paradigm based on Semantic Feature Decomposition (SeFD) for the integration of semantic communication and large-scale visual generation models to achieve high-performance, highly interpretable and controllable image communication. According to this paradigm, a Texture-Color based Semantic Communication system of Images TCSCI is proposed. TCSCI decomposing the images into their natural language description (text), texture and color semantic features at the transmitter. During the transmission, features are transmitted over the wireless channel, and at the receiver, a large-scale visual generation model is utilized to restore the image through received features. TCSCI can achieve extremely compressed, highly noise-resistant, and visually similar image semantic communication, while ensuring the interpretability and editability of the transmission process. The experiments demonstrate that the TCSCI outperforms traditional image communication systems and existing semantic communication systems under extreme compression with good anti-noise performance and interpretability.

cs.CV↗

arXiv2024

Receiver Selection and Transmit Beamforming for Multi-static Integrated Sensing and Communications

Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications. However, conventional mono-static ISAC systems, which simultaneously transmit and receive at the same equipment, may suffer from severe self-interference, and thus significantly degrade the system performance.To address this issue, this paper studies a multi-static ISAC system for cooperative target localization and communications, where the transmitter transmits ISAC signal to multiple receivers (REs) deployed at different positions. We derive the closed-form Cramér-Rao bound (CRB) on the joint estimations of both the transmission delay and Doppler shift for cooperative target localization, and the CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements for the REs. To solve this problem, we first decouple it into two subproblems for RE selection and transmit beamforming, respectively. Then, a minimax linkage-based method is proposed to solve the RE selection subproblem, and a successive convex approximation algorithm is adopted to deal with the transmit beamforming subproblem with non-convex constraints. Finally, numerical results validate our analysis and reveal that our proposed multi-static ISAC scheme achieves better ISAC performance than the conventional mono-static ones when the number of cooperative REs is large.

eess.SP↗

arXiv2024

Cooperative Semantic Knowledge Base Update Policy for Multiple Semantic Communication Pairs

Semantic communication has emerged as a promising communication paradigm and there have been extensive research focusing on its applications in the increasingly prevalent multi-user scenarios. However, the knowledge discrepancy among multiple users may lead to considerable disparities in their performance. To address this challenge, this paper proposes a novel multi-pair cooperative semantic knowledge base (SKB) update policy. Specifically, for each pair endowed with SKB-enabled semantic communication, its well-understood knowledge in the local SKB is selected out and uploaded to the server to establish a global SKB, via a score-based knowledge selection scheme. The knowledge selection scheme achieves a balance between the uplink transmission overhead and the completeness of the global SKB. Then, with the assistance of the global SKB, each pair's local SKB is refined and their performance is improved. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains with minimal transmission overhead, especially for the initially poor-performing pairs.

cs.IT↗

arXiv2024

On the origin of anomalous hysteresis in graphite/boron nitride transistors

Field-effect devices constructed by stacking flakes of van der Waals (vdW) materials, with hexagonal boron nitride (hBN) playing the role of gate dielectric, often exhibit virtually no hysteresis in their characteristics. This permits exquisitely detailed studies of diverse gate-voltage-tuned phenomena in vdW devices. Recently, however, a dramatic form of gate hysteresis, sometimes called the "gate doesn't work" (GDW) or "electron ratchet" effect, has been seen in certain individual vdW devices that seem otherwise unexceptional. When it occurs, this hysteresis phenomenon is striking and robust, yet it is difficult to reliably reproduce between devices and, largely as a result, its origin remains disputed. Most devices where it has been seen have a bilayer graphene channel and nominal rotational alignment between the graphene and hBN, which has engendered explanations based on properties of bilayer graphene combined with moiré effects. Here, we report our studies of the phenomenon observed in devices that have multilayer graphene channels. We find that the effect can occur in devices with graphite channels that have many more than two graphene layers, in which case it is unambiguously associated with just one surface of the graphite. It can also survive to room temperature, occur in the absence of intentional rotational alignment with hBN, persist when a monolayer of WSe2 is inserted between the graphene and hBN, and exhibit continuous relaxation on timescales of hours or longer. These observations impose strong constraints on the origin of this puzzling phenomenon, which has exciting potential applications if it can be mastered.

cond-mat.mes-hall↗

arXiv2024

MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communication

Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face challenges with conflicting requirements between codebook design and digital constellation modulation. Traditional codebooks need wide index ranges, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. In addition, a residual vector quantization (RVQ) mechanism is also integrated for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Finally, to simulate modern image transmission scenarios, we employ a diverse collection of high-resolution 2K images as the test set. The experimental results consistently demonstrate the superior performance of MOC-RVQ over conventional methods such as BPG or JPEG. Additionally, MOC-RVQ achieves comparable performance to an analog JSCC scheme, while needing only one-sixth of the channel bandwidth ratio (CBR) and being directly compatible with digital transmission systems.

cs.CV↗

arXiv2024

Semantic Model Component Implementation for Model-driven Semantic Communications

The key feature of model-driven semantic communication is the propagation of the model. The semantic model component (SMC) is designed to drive the intelligent model to transmit in the physical channel, allowing the intelligence to flow through the networks. According to the characteristics of neural networks with common and individual model parameters, this paper designs the cross-source-domain and cross-task semantic component model. Considering that the basic model is deployed on the edge node, the large server node updates the edge node by transmitting only the semantic component model to the edge node so that the edge node can handle different sources and different tasks. In addition, this paper also discusses how channel noise affects the performance of the model and proposes methods of injection noise and regularization to improve the noise resistance of the model. Experiments show that SMCs use smaller model parameters to achieve cross-source, cross-task functionality while maintaining performance and improving the model's tolerance to noise. Finally, a component transfer-based unmanned vehicle tracking prototype was implemented to verify the feasibility of model components in practical applications.

cs.AI↗

arXiv2024

MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves state-of-the-art performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay.

cs.IT↗

arXiv2024

Magneto-optics in a van der Waals magnet tuned by self-hybridized polaritons

Controlling quantum materials with light is of fundamental and technological importance. By utilizing the strong coupling of light and matter in optical cavities (1-3), recent studies were able to modify some of their most defining features (4-6). In this work, we study the magneto-optical properties of a van der Waals magnet that supports strong coupling of photons and excitons even in the absence of external cavity mirrors. In this material - the layered magnetic semiconductor CrSBr - emergent light-matter hybrids called polaritons are shown to significantly increase the spectral bandwidth of correlations between the magnetic, electronic, and optical properties, enabling largely tunable optical responses to applied magnetic fields and magnons. Our results highlight the importance of exciton-photon self-hybridization in van der Waals magnets and motivate novel directions for the manipulation of quantum material properties by strong light-matter coupling.

cond-mat.mes-hall↗

arXiv2024

Some constructive results on Disjoint Golomb Rulers

A set $\{a_i\:|\: 1\leq i \leq k\}$ of non-negative integers is a Golomb ruler if differences $a_i-a_j$, for any $i \neq j$, are all distinct.All finite Sidon sets are Golomb rulers, and vice versa. A set of $I$ disjoint Golomb rulers (DGR) each being a $J$-subset of $\{1,2,\cdots, n\}$ is called an $(I,J,n)$-DGR. Let $H(I, J)$ be the least positive integer $n$ such that there is an $(I,J,n)$-DGR. In this paper, we propose a series of conjectures on the constructions and structures of DGR. The main conjecture states that if $A$ is any set of positive integers such that $|A| = H(I, J)$, then there are $I$ disjoint Golomb rulers, each being a $J$-subset of $A$, which generalizes the conjecture proposed by Koml{ó}s, Sulyok and Szemer{é}di in 1975 on the special case $I = 1$. This main conjecture implies some interesting conjectures on disjoint Golomb rulers. We also prove some constructive results on DGR, which improve or generalize some basic inequalities on DGR proved by Kløve.

math.CO↗

arXiv2024

Interplay of electronic crystals with integer and fractional Chern insulators in moiré pentalayer graphene

The rapid development of moiré quantum matter has recently led to the remarkable discovery of the fractional quantum anomalous Hall effect, and sparked predictions of other novel correlation-driven topological states. Here, we investigate the interplay of electronic crystals with integer and fractional Chern insulators in a moiré lattice of rhomobohedral pentalayer graphene (RPG) aligned with hexagonal boron nitride. At a doping of one electron per moiré unit cell, we see a correlated insulator with a Chern number that can be tuned between $C=0$ and $+1$ by an electric displacement field, accompanied by an array of other such insulators formed at fractional band fillings, $ν$. Collectively, these states likely correspond to trivial and topological electronic crystals, some of which spontaneously break the discrete translational symmetry of the moiré lattice. Upon applying a modest magnetic field, a narrow region forms around $ν=2/3$ in which transport measurements imply the emergence of a fractional Chern insulator, along with hints of weaker states at other fractional $ν$. In the same sample, we also see a unique sequence of incipient Chern insulators arising over a broad range of incommensurate band filling near two holes per moiré unit cell. Our results establish moiré RPG as a fertile platform for studying the competition and potential intertwining of electronic crystallization and topological charge fractionalization.

cond-mat.mes-hall↗

arXiv2024

Strongly interacting Hofstadter states in magic-angle twisted bilayer graphene

Magic-angle twisted bilayer graphene (MATBG) hosts a multitude of strongly correlated states at partial fillings of its flat bands. In a magnetic field, these flat bands further evolve into a unique Hofstadter spectrum renormalized by strong Coulomb interactions. Here, we study the interacting Hofstadter states spontaneously formed within the topological magnetic subbands of an ultraclean MATBG device, notably including symmetry-broken Chern insulator (SBCI) states and fractional quantum Hall (FQH) states. The observed SBCI states form a cascade with their Chern numbers mimicking the main sequence correlated Chern insulators. The FQH states in MATBG form in Jain sequence; however, they disappear at high magnetic field, distinct from conventional FQH states which strengthen with increasing magnetic field. We reveal a unique magnetic field-driven phase transition from composite fermion phases to a dissipative Fermi liquid. Our theoretical analysis of the magnetic subbands hosting FQH states predicts non uniform quantum geometric properties far from the lowest Landau level. This points towards a more natural interpretation of these FQH states as in-field fractional Chern insulators of the magnetic subbands.

cond-mat.mes-hall↗