arXiv subjects

Yuxuan Shi

Publications and source records attributed to Yuxuan Shi.

At least 37 records · Page 2Linked to original sources

arXiv2026

Filter-and-Attend: Wireless Channel Foundation Model with Noise-Plus-Interference Suppression Structure

Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pre-trained to learn a universal channel representation for a wide range of communications and sensing tasks. While existing works on WCFM have demonstrated its great potentials in various downstream tasks, the models are all trained using perfect (i.e., error-free and complete) channel information state (CSI) data. In practical systems, however, only degraded CSI obtained from pilot-based channel estimation is accessible, leading to distorted channel representations and performance degradation in downstream tasks for some real-world environments with severe noise and interference. To address this issue, this paper proposes a new paradigm for WCFM, termed as Filter-and-Attend. In this paradigm, Filter refers to explicitly suppressing noise-plus-interference (NPI) in the received signals, while Attend means performing correlation-aware CSI completion and feature extraction using attention mechanism. Specifically, an enhanced WCFM architecture is developed. In this architecture, coarse estimates of the CSIs are first obtained and exploited to construct two projection matrices that extract NPI components in the received signals, which are further processed and removed by a subtraction module. The filtered signal is subsequently passed through a CSI completion network to get a clean CSI for feature extraction. Simulation results demonstrated that compared to the state-of-the-art solutions, WCFM with NPI suppression structure achieves improved performance on various downstream tasks including time-domain channel prediction, frequency-domain channel prediction, and localization.

eess.SP↗

arXiv2025

Accretion of matter of a new bumblebee black hole

We investigate how the newly obtained static black hole in bumblebee gravity affects the behavior of accreting matter and its observable signatures. The Lorentz-violating parameter that characterizes this geometry modifies photon trajectories and shifts the location of the critical curve that defines the shadow. Using ray tracing, we examine light deflection, the structure of direct emission, lensing rings, and photon rings, and we explore three thin-disk emission models--starting at the ISCO, at the photon sphere, and at the event horizon--together with static and infalling spherical accretions. Larger values of this parameter enlarge the shadow, move all optical features outward, and suppress the observed intensity through gravitational redshift, with additional dimming produced by Doppler effects for infalling matter

gr-qc↗

arXiv2025

Neutrino oscillations induced by a new bumblebee black hole

This work investigates neutrino propagation in the spacetime of a newly introduced black hole arising from spontaneous Lorentz-symmetry breaking in bumblebee gravity. The analysis focuses on three independent components: the rate at which neutrino-antineutrino annihilation deposits energy in the surrounding region, the geometric contribution to the phase accumulated by neutrino mass eigenstates, and the modifications to flavor conversion produced by weak gravitational lensing. Working with a two-flavor system, both mass orderings are examined, and the calculation incorporates the interference between distinct trajectories reaching the detector. The numerical results show that the Lorentz-violating deformation substantially increases the efficiency of the annihilation channel, produces characteristic shifts in the oscillation phase not present in earlier bumblebee configurations, and reshapes the angular dependence of the lensing-induced flavor transition pattern.

gr-qc↗

arXiv2025

Effects of bumblebee gravity on neutrino motion

This study explores how the spontaneous violation of Lorentz symmetry -- modeled through a black hole solution in the context of bumblebee gravity -- affects the propagation and dynamics of neutrinos. The investigation centers on three distinct aspects: the rate of energy deposition due to neutrino-antineutrino pair annihilation, modifications to the neutrino oscillation phase driven by the underlying spacetime structure, and the influence of gravitational lensing on flavor conversion probabilities. To support the theoretical considerations, numerical simulations are conducted for oscillation probabilities in a two-flavor framework, taking into account both normal and inverted mass orderings. For comparison, the outcomes are juxtaposed with those obtained in a different Lorentz-violating background, namely, a black hole solution within Kalb-Ramond gravity.

gr-qc↗

arXiv2025

Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.

cs.MM↗

arXiv2025

Influence of a Kalb-Ramond black hole on neutrino behavior

In this work, we investigate the consequences of Lorentz symmetry breaking induced by a black hole solution within Kalb-Ramond gravity on neutrino dynamics. The analysis is structured around three key phenomena: the energy output associated with neutrino-antineutrino annihilation, the impact of spacetime geometry on the oscillation phases of neutrinos, and the changes in flavor transition probabilities caused by gravitational lensing effects. To complement the theoretical framework, we also carry out a numerical evaluation of neutrino oscillation probabilities, comparing scenarios of normal and inverted mass orderings within a two-flavor approximation.

gr-qc↗

arXiv2025

The accretion disk and neutrino propagation of Barrow-modified Black Hole

This paper attempts to clarify the deep consequences of Barrow fractal black hole spacetime configurations caused by quantum gravity on neutrino pair annihilation and accretion disk dynamics. We systematically derive the analytical expression for the innermost stable circular orbit (ISCO) radius ($r_{\text{ISCO}}\propto M^{2/(2+Δ)}$) by building a Barrow-modified static spherically symmetric metric ($r\rightarrow r^{1+Δ/2}$), and we find that increasing $Δ$ significantly shifts the ISCO inward. We numerically solve the radiation flux, effective temperature, and differential luminosity distribution under the modified metric based on the Novikov-Thorne relativistic thin accretion disk model. For $Δ=1$, the results show that the temperature increases by $62.5\%$, the peak disk radiation flux increases by $22.5\%$, and the spectral radiance increases by around $50\%$. Fractal horizons enhance neutrino trajectory bending effects, according to further study of neutrino pair annihilation ($ν\barν\rightarrow e^+e^-$) energy deposition processes using local Lorentz transformations and null geodesic equations. The energy deposition rate for $Δ=1$ is $8-28$ times higher than classical estimates when the black hole radius is $R/M\sim3-4$. This work provides important theoretical insights into the influence of quantum spacetime geometry on high-energy astrophysical phenomena in extreme gravitational fields by establishing, for the first time, quantitative relationships between the Barrow parameter $Δ$ and neutrino pair annihilation energy and accretion disk radiative efficiency.

gr-qc↗

arXiv2025

The neutrino flavor oscillations in the static and spherically symmetric black-hole-like wormholes

We study the effects of neutrino lensing induced by a Damour-Solodukhin wormhole on the neutrino oscillation. We derive and calculate the flavour transition probabilities in the presence of Damour-Solodukhin factor $Λ$ as a shift in the massive source to show that the neutrino flavour oscillation is also sensitive not only to the sign of difference between the squared masses but also to the individual mass of neutrinos in both the two-flavour and the three-flavour cases, which is similar to the results for the black holes in the previous works mentioned here. As a consequence of parameter $Λ$ within a region, a series of curves of probability function versus the azimuthal angle $ϕ$ with definite masses of neutrino can be plotted and their shapes resemble each other in the case of two-flavoured neutrinos and of three-flavoured ones. In view of the probability functions due to the wormhole, we reveal that the contribution of the factor $Λ$ is novel. Based on our analytical and numerical discussions on the probability expressions, the difference of the neutrino flavour oscillation arising from the shift in the wormhole factor $Λ$ is detectable. It is crucial that the $Λ$ as deviation from the black holes can change the shapes of the curves greatly, in the case of three-flavoured neutrinos in particular. The detailed comparisons can be made among our estimations depicted in the figures for neutrino oscillations and the measurements from the detector, which open a new window for judging whether the remote star as lens is black-hole-like wormhole or just a spherically symmetric black hole and further the wormhole factor $Λ$ can be estimated.

gr-qc↗

arXiv2025

Learnable Residual-Based Latent Denoising in Semantic Communication

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images.

cs.LG↗

arXiv2025

Semantic-aided Parallel Image Transmission Compatible with Practical System

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems.

cs.IT↗

arXiv2025

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leverages large-scale wireless channel datasets for unsupervised pretraining and extracting universal channel representations, which captures complex spatiotemporal dependencies. In fact,this task-agnostic design adapts WirelessGPT seamlessly to a wide range of downstream tasks, using a unified representation with minimal fine-tuning. By unifying communication and sensing functionalities, WirelessGPT addresses the limitations of task-specific models, offering a scalable and efficient solution for integrated sensing and communication (ISAC). With an initial parameter size of around 80 million, WirelessGPT demonstrates significant improvements over conventional methods and smaller AI models, reducing reliance on large-scale labeled data. As the first foundation model capable of supporting diverse tasks across different domains, WirelessGPT establishes a new benchmark, paving the way for future advancements in multi-task wireless systems.

cs.LG↗

arXiv2025

RWZC: A Model-Driven Approach for Learning-based Robust Wyner-Ziv Coding

In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlapping information between image pairs varies. Specifically, we first model the affine relationship between correlated images and leverage this model for learnable mask generation and rate-adaptive joint source-channel coding. Moreover, we also provide a warping-prediction network to remove the distortion from channel interference and affine transform. Intuitively, the observed performance improvement is largely due to focusing on the simple geometric relationship, rather than the complex joint distribution between the sources. Numerical results show that our framework achieves a 1.5 dB gain in PSNR and a 0.2 improvement in MS-SSIM, along with a significant superiority in perceptual metrics, compared to state-of-the-art methods when applied to real-world samples with non-stationary correlations.

cs.IT↗

arXiv2025

SCSC: A Novel Standards-Compatible Semantic Communication Framework for Image Transmission

Joint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propose a novel standard-compatible JSCC framework for the transmission of images over multiple-input multiple-output (MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC schemes, our framework consists of learnable modules that enable communication using conventional separate source and channel codes (SSCC), which makes it amenable for easy deployment on legacy systems. Specifically, the learnable modules involve a preprocessing-empowered network (PPEN) for preserving essential semantic information, and a precoder \& combiner-enhanced network (PCEN) for efficient transmission over a resource-constrained MIMO channel. We treat existing compression and channel coding modules as non-trainable blocks. Since the parameters of these modules are non-differentiable, we employ a proxy network that mimics their operations when training the learnable modules. Numerical results demonstrate that our scheme can save more than 29\% of the channel bandwidth, and requires lower complexity compared to the constrained baselines. We also show its generalization capability to unseen datasets and tasks through extensive experiments.

cs.IT↗

arXiv2024

Indirect Lossy Source Coding with Observed Source Reconstruction: Nonasymptotic Bounds and Second-Order Asymptotics

This paper considers the joint compression of a pair of correlated sources, where the encoder is allowed to access only one of the sources. The objective is to recover both sources under separate distortion constraints for each source while minimizing the rate. This problem generalizes the indirect lossy source coding problem by also requiring the recovery of the observed source. In this paper, we aim to study the nonasymptotic and second-order asymptotic properties of this problem. Specifically, we begin by deriving nonasymptotic achievability and converse bounds valid for general sources and distortion measures. The source dispersion (Gaussian approximation) is then determined through asymptotic analysis of the nonasymptotic bounds. We further examine the case of erased fair coin flips (EFCF) and provide its specific nonasymptotic achievability and converse bounds. Numerical results under the EFCF case demonstrate that our second-order asymptotic approximation closely approximates the optimum rate at appropriately large blocklengths.

cs.IT↗

arXiv2024

Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation

Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to reduce memory overhead in fine-tuning from perspectives of activation function and layer normalization. To this end, we propose the Approximate Backpropagation (Approx-BP) theory, which provides the theoretical feasibility of decoupling the forward and backward passes. We apply our Approx-BP theory to backpropagation training and derive memory-efficient alternatives of GELU and SiLU activation functions, which use derivative functions of ReLUs in the backward pass while keeping their forward pass unchanged. In addition, we introduce a Memory-Sharing Backpropagation strategy, which enables the activation memory to be shared by two adjacent layers, thereby removing activation memory usage redundancy. Our method neither induces extra computation nor reduces training efficiency. We conduct extensive experiments with pretrained vision and language models, and the results demonstrate that our proposal can reduce up to $\sim$$30\%$ of the peak memory usage. Our code is released at https://github.com/yyyyychen/LowMemoryBP.

cs.LG↗

arXiv2024

Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels

Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fading is to utilize feedback MIMO channel state information (CSI). In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding. To avoid feature fusion due to abrupt combination of CSI with features, we present a non-invasive CSI fusion multi-head attention module inside the Swin Transformer. With the learned attention masking map determined by both source and channel states, more robust attention distribution could be generated. Furthermore, the percentage of mask elements could be flexibly adjusted by the learnable mask ratio, which is produced based on the conditional variational interference in an unsupervised manner. In this way, CSI-aware semantic coding is achieved through learnable CSI fusion masking. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks in MIMO fading channels.

cs.IT↗

arXiv2024

The shadow and gamma-ray bursts of a Schwarzschild black hole in asymptotic safety

We research on the neutrino pair annihilation $ν+\overlineν\longrightarrow e^{-}+e^{+}$ around a massive source in asymptotic safety. Since neutrinos and photons have the same geodesic equation around black holes, we can estimate the radius where the neutrinos will be released by obtaining a series of trajectory curves with various correction values $ξ$. The black hole shadow radius is influenced by the correction parameter $ξ$. The black hole shadow radius decreases with increasing the $ξ$. The ratio $\dfrac{\dot{Q}}{\dot{Q}_{Newt}}$ corresponding to the energy deposition per unit time over that in the Newtonian case is derived and calculated. We find that the quantum corrections to the black hole spacetime affect the emitted energy rate ratio for the annihilation. It is interesting that the more considerable quantum effect reduces the ratio value slightly. Although the energy conversion is damped because of the quantum correction, the energy deposition rate is enough during the neutrino-antineutrino annihilation. The corrected annihilation process can become a source of gamma ray burst. We also investigate the derivative $\dfrac{\mathrm{d}\dot{Q}}{\mathrm{d}r}$ relating to the star's radius $r$ to show that the quantum effect for the black hole will drop the ratio. The more manifest quantum gravity influence leads the weaker neutrino pair annihilation.

gr-qc↗

arXiv2024

DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization

Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.

cs.CV↗