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Wen Chen

Publications and source records attributed to Wen Chen.

At least 181 records · Page 10Linked to original sources
arXiv2025

DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing

Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL.

cs.LG↗
arXiv2025

Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of Reconfigurable Intelligent Surfaces (RIS), which provide alternative communication pathways to assist vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider a RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient (TD3) and some typical stochastic schemes.

cs.LG↗
arXiv2025

DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV

In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to Base Station (BS), which poses a risk of privacy leakage. Simplified Contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the Central Processing Unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

cs.CV↗
arXiv2025

Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies.

cs.CR↗
arXiv2025

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.

cs.LG↗
arXiv2025

Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

cs.LG↗
arXiv2025

PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X

On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging.

cs.NI↗
arXiv2025

Beamforming Design for Intelligent Reffecting Surface Aided Near-Field THz Communications

Intelligent reflecting surface (IRS) operating in the terahertz (THz) band has recently gained considerable interest due to its high spectrum bandwidth. Due to the exploitation of large scale of IRS, there is a high probability that the transceivers will be situated within the near-field region of the IRS. Thus, the near-field beam split effect poses a major challenge for the design of wideband IRS beamforming, which causes the radiation beam to deviate from its intended location, leading to significant gain losses and limiting the efficient use of available bandwidths. While delay-based IRS has emerged as a potential solution, current beamforming schemes generally assume unbounded range time delays (TDs). In this letter, we first investigate the near-field beam split issue at the IRS. Then, we extend the piece-wise far-field model to the IRS, based on which, a double-layer delta-delay (DLDD) IRS beamforming scheme is proposed. Specifically, we employ an element-grouping strategy and the TD imposed on each sub-surface of IRS is achieved by a series of TD modules. This method significantly reduces the required range of TDs. Numerical results show that the proposed DLDD IRS beamforming scheme can effectively mitigate the near-field beam split and achieve near-optimal performance.

eess.SP↗
arXiv2025

Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy challenges in 6G networks. In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first time, aiming to achieve efficient management of communication resources in a dynamic environment. Firstly, we construct a mathematical model for semantic communication in platoon systems, in which the DeepSC model and MU-DeepSC model are used to semantically encode and decode unimodal and multi-modal data, respectively. Then, we propose the quality of experience (QoE) metric based on semantic similarity and semantic rate. Meanwhile, we consider the success rate of semantic information transmission (SRS) metric to ensure the fairness of channel resource allocation. Next, the optimization problem is posed with the aim of maximizing the QoE in vehicle-to-vehicle (V2V) links while improving SRS. To solve this mixed integer nonlinear programming problem (MINLP) and adapt to time-varying channel conditions, the paper proposes a distributed semantic-aware multi-modal resource allocation (SAMRA) algorithm based on multi-agent reinforcement learning (MARL), referred to as SAMRAMARL. The algorithm can dynamically allocate channels and power and determine semantic symbol length based on the contextual importance of the transmitted information, ensuring efficient resource utilization. Finally, extensive simulations have demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE, SRS, and communication delay in C-V2X platooning scenarios.

cs.LG↗
arXiv2025

Non-side-to-side tilings of the sphere by congruent triangles with any irrational angle

We develop the basic and new tools for classifying non-side-to-side tilings of the sphere by congruent triangles. Then we prove that, if the triangle has any irrational angle in degree, such tilings are: a sequence of 1-parameter families of triangles each admitting many 2-layer earth map tilings with $2n$($n\geq3$) tiles, together with rotational modifications for even $n$; a 1-parameter family of triangles each admitting a unique tiling with $8$ tiles; and a sporadic triangle admitting a unique tiling with $16$ tiles. Then a scheme is outlined to classify the case with all angles being rational in degree, justified by some known and new examples.

math.CO↗
arXiv2025

Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends

As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.

cs.CE↗
arXiv2025

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation

Drug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in this paper, we demonstrate that the results may be misleading and cannot be well generalized to real practice. The core observation is that the canonical randomized split of a test set in conventional evaluation leaves the test set dominated by samples with high similarity to the training set. The performance of models is severely degraded on samples with lower similarity to the training set but the drawback is highly overlooked in current evaluation. As a result, the performance can hardly be trusted when the model meets low-similarity samples in real practice. To address this problem, we propose a framework of similarity aware evaluation in which a novel split methodology is proposed to adapt to any desired distribution. This is achieved by a formulation of optimization problems which are approximately and efficiently solved by gradient descent. We perform extensive experiments across five representative methods in four datasets for two typical target evaluations and compare them with various counterpart methods. Results demonstrate that the proposed split methodology can significantly better fit desired distributions and guide the development of models. Code is released at https://github.com/Amshoreline/SAE/tree/main.

cs.LG↗
arXiv2025

Semi-automatic Calculations of Multi-loop Feynman Amplitudes with AmpRed

We present a Mathematica package AmpRed for the semi-automatic calculations of multi-loop Feynman amplitudes with high efficiency and precision. AmpRed implements the methods of integration by parts and differential equations in the Feynman-parameter representation. It allows for the calculations of general parametric integrals (which may not have momentum-space correspondences). Various user-friendly tools for multi-loop calculations, such as those to construct and solve differential equations for Feynman integrals, are provided. It can also deal with tensor algebras in non-relativistic field theories. Interfaces to some packages, like QGRAF and FORM, are also provided.

hep-ph↗
arXiv2025

High-efficiency broadband active metasurfaces via reversible metal electrodeposition

Realizing active metasurfaces with substantial tunability is important for many applications but remains challenging due to difficulties in dynamically tuning light-matter interactions at subwavelength scales. Here, we introduce reversible metal electrodeposition as a versatile approach for enabling active metasurfaces with exceptional tunability across a broad bandwidth. As a proof of concept, we demonstrate a dynamic beam-steering device by performing reversible copper (Cu) electrodeposition on a reflective gradient metasurface composed of metal-insulator-metal resonators. By applying different voltages, the Cu atoms can be uniformly and reversibly electrodeposited and stripped around the resonators, effectively controlling the gap-surface plasmon resonances and steering the reflected light. This process experimentally achieved >90% diffraction efficiencies and >60% reflection efficiencies in both specular and anomalous modes, even after thousands of cycles. Moreover, these high efficiencies can be extended from the visible to the near- and mid-infrared regimes, demonstrating the broad versatility of this approach in enabling various active optical and thermal devices with different working wavelengths and bandwidths.

physics.optics↗
arXiv2025

The flat tail in the burst energy distribution of FRB 20240114A

Fast Radio Bursts (FRBs) are enigmatic millisecond-duration radio transients of extra-galactic origin, whose underlying mechanisms and progenitors remain poorly understood. FRBs are broadly classified into two categories: repeating FRBs, which emit multiple bursts over time, and one-off FRBs, which are detected as single events. A central question in FRB research is whether these two classes share a common origin. In this study, we present observations of FRB 20240114A, a repeating FRB that entered a hyperactive phase in January 2024. We conducted a 318-hour monitoring campaign using the Kunming 40-Meter Radio Telescope (KM40M) in the S-band (2.187-2.311 GHz), during which we detected eight radio bursts. We analyzed their properties, including dispersion measure (DM), bandwidth, pulse width, flux, fluence, and energy. Additionally, we searched for counterparts in overlapping data from the Five-hundred-meter Aperture Spherical Telescope (FAST) in the L-band (1.0-1.5 GHz). While no bursts were temporally aligned between the two telescopes, we identified one FAST burst that arrived approximately 6 ms after one of the KM40M bursts. The absence of FAST counterparts for the KM40M bursts suggests that individual bursts from FRB 20240114A are likely narrow-band, with fractional bandwidths less than 10%. By comparing the cumulative event rates from KM40M and FAST observations, we found that the two measurements are compatible, indicating a possible flattening of the event rate at higher energies. This feature aligns with observations of one-off FRBs, supporting the hypothesis that repeating and one-off FRBs may share a common origin.

astro-ph.HE↗
arXiv2025

PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks

The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveraging AI have catalyzed transformative advancements, their development demands large-scale datasets, considerable storage capacity, and substantial computational resources. Furthermore, ensuring their clinical applicability and generalizability requires rigorous validation across a broad spectrum of clinical tasks. Here, we present PathOrchestra, a versatile pathology foundation model trained via self-supervised learning on a dataset comprising 300K pathological slides from 20 tissue and organ types across multiple centers. The model was rigorously evaluated on 112 clinical tasks using a combination of 61 private and 51 public datasets. These tasks encompass digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and the generation of structured reports. PathOrchestra demonstrated exceptional performance across 27,755 WSIs and 9,415,729 ROIs, achieving over 0.950 accuracy in 47 tasks, including pan-cancer classification across various organs, lymphoma subtype diagnosis, and bladder cancer screening. Notably, it is the first model to generate structured reports for high-incidence colorectal cancer and diagnostically complex lymphoma-areas that are infrequently addressed by foundational models but hold immense clinical potential. Overall, PathOrchestra exemplifies the feasibility and efficacy of a large-scale, self-supervised pathology foundation model, validated across a broad range of clinical-grade tasks. Its high accuracy and reduced reliance on extensive data annotation underline its potential for clinical integration, offering a pathway toward more efficient and high-quality medical services.

cs.CV↗
arXiv2025

Intelligent Reflecting Surfaces for Wireless Networks: Deployment Architectures, Key Solutions, and Field Trials

Intelligent reflecting surfaces (IRSs) have emerged as a transformative technology for wireless networks by improving coverage, capacity, and energy efficiency through intelligent manipulation of wireless propagation environments. This paper provides a comprehensive study on the deployment and coordination of IRSs for wireless networks. By addressing both single- and multi-reflection IRS architectures, we examine their deployment strategies across diverse scenarios, including point-to-point, point-to-multipoint, and point-to-area setups. For the single-reflection case, we highlight the trade-offs between passive and active IRS architectures in terms of beamforming gain, coverage extension, and spatial multiplexing. For the multi-reflection case, we discuss practical strategies to optimize IRS deployment and element allocation, balancing cooperative beamforming gains and path loss. The paper further discusses practical challenges in IRS implementation, including environmental conditions, system compatibility, and hardware limitations. Numerical results and field tests validate the effectiveness of IRS-aided wireless networks and demonstrate their capacity and coverage improvements. Lastly, promising research directions, including movable IRSs, near-field deployments, and network-level optimization, are outlined to guide future investigations.

eess.SP↗