arXiv subjects

Nan Cheng

Publications and source records attributed to Nan Cheng.

At least 91 records · Page 5Linked to original sources

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

Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM

Edge caching is an emerging technology that empowers caching units at edge nodes, allowing users to fetch contents of interest that have been pre-cached at the edge nodes. The key to pre-caching is to maximize the cache hit percentage for cached content without compromising users' privacy. In this letter, we propose a federated learning (FL) assisted edge caching scheme based on lightweight architecture denoising diffusion probabilistic model (LDPM). Our simulation results verify that our proposed scheme achieves a higher cache hit percentage compared to existing FL-based methods and baseline methods.

cs.NI↗

arXiv2025

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies

The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision positioning, intelligent aircraft-to-everything communication, dynamic airspace management, and UAV operational services. UTICN introduces key technologies such as integrated sensing and communication (ISAC), passive and active positioning, intelligent machine communication, swarm coordination, and control-data decoupled management frameworks. We demonstrate UTICN's feasibility through two use cases, i.e., a city-level LAE management platform and a multi-frequency collaborative ISAC system. This work provides a fundamental reference for building a unified operational foundation and airspace management architecture for the LAE.

eess.SY↗

arXiv2025

Conceal Truth while Show Fake: T/F Frequency Multiplexing based Anti-Intercepting Transmission

In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands using non-orthogonal frequency division multiplexing. The decoy signals are used to deceive non-cooperative eavesdropper, while the true signals are hidden to counter interception threats. Definitions for the interception and deception probabilities are provided, and the mechanism of CTSF is discussed. To improve the secrecy performance of true signals while ensuring decoy signals achieve their deceptive purpose, we model the problem as maximizing the sum secrecy rate of true signals, with constraint on the decoy effect. Furthermore, we propose a bi-stage alternating dual-domain optimization approach for joint optimization of both power allocation and correlation coefficients among multiple sources, and a Newton's method is proposed for fitting the T/F frequency multiplexing factor. In addition, simulation results verify the efficiency of anti-intercepting performance of our proposed CTSF scheme.

eess.SY↗

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

RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental structure perception, such as building outlines, and location of BS just relay on pathloss, through integrated sensing and communication (ISAC). Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

cs.AI↗

arXiv2025

Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach

Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Despite advances in medical imaging technologies, transforming static or limited dynamic imaging data into comprehensive dynamic cardiac models remains challenging. Existing training systems lack accurate simulation of cardiac physiological dynamics. This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA, integrating digital twin technology, computer vision, and physical model manufacturing to provide precise, personalized tools for interventional cardiology. Methods: Using 4D-CTA data from a 60-year-old female with three-vessel coronary stenosis, we segmented cardiac chambers and coronary arteries, constructed dynamic models, and implemented skeletal skinning weight computation to simulate vessel deformation across 20 cardiac phases. Transparent vascular physical models were manufactured using medical-grade silicone. We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications. Results: Morphological consistency between virtual and real angiography reached 80.9%. Dice similarity coefficients for guidewire motion ranged from 0.741-0.812, with mean trajectory errors below 1.1 mm. The transparent model demonstrated advantages in CABG training, allowing direct visualization while simulating beating heart challenges. Conclusion: Our patient-specific digital-physical twin approach effectively reproduces both anatomical structures and dynamic characteristics of coronary vasculature, offering a dynamic environment with visual and tactile feedback valuable for education and clinical planning.

cs.CV↗

arXiv2025

Anti-Intercept OFDM Waveform Design with Secure Coding for Satellite Networks

Low Earth Orbit (LEO) satellite networks are integral to next-generation communication systems, providing global coverage, low latency, and minimal signal loss. However, their unique characteristics, such as constrained onboard resources, Line-of-Sight (LoS) propagation, and vulnerability to eavesdropping over wide coverage areas, present significant challenges to physical layer security. To address these challenges, this paper focuses on the design of anti-intercept waveforms for satellite-ground links within Orthogonal Frequency Division Multiplexing (OFDM) systems, aiming to enhance security against eavesdropping threats. We formulate a secrecy rate maximization problem that aims to balance secrecy performance and communication reliability under eavesdropping constraints and sub-carrier power limitations. To solve this non-convex optimization problem, we propose a bisection search-activated neural network (BSA-Net) that integrates unsupervised learning for secure coding optimization and bisection search for dynamic power allocation. The proposed method is structured in two stages: the first optimizes secure coding under power constraints, while the second allocates power across sub-carriers under eavesdropping constraints. Extensive simulation results demonstrate the efficacy of our approach, showcasing significant improvements in secrecy rate performance.

eess.SP↗

arXiv2025

Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning

Background: Coronary artery bypass grafting (CABG) planning requires advanced spatial visualization and consideration of coronary artery depth, calcification, and pericardial adhesions. Objective: To develop and evaluate a dynamic cardiovascular holographic visualization tool for preoperative CABG planning. Methods: Using 4D cardiac computed tomography angiography data from 14 CABG candidates, we developed a semi-automated workflow for time-resolved segmentation of cardiac structures, epicardial adipose tissue (EAT), and coronary arteries with calcium scoring. The workflow incorporated methods for cardiac segmentation, coronary calcification quantification, visualization of coronary depth within EAT, and pericardial adhesion assessment through motion analysis. Dynamic cardiovascular holograms were displayed using the Looking Glass platform. Thirteen cardiac surgeons evaluated the tool using a Likert scale. Additionally, pericardial adhesion scores from holograms of 21 patients (including seven undergoing secondary cardiac surgeries) were compared with intraoperative findings. Results: Surgeons rated the visualization tool highly for preoperative planning utility (mean Likert score: 4.57/5.0). Hologram-based pericardial adhesion scoring strongly correlated with intraoperative findings (r=0.786, P<0.001). Conclusion: This study establishes a visualization framework for CABG planning that produces clinically relevant dynamic holograms from patient-specific data, with clinical feedback confirming its effectiveness for preoperative planning.

physics.med-ph↗

arXiv2025

Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model

We introduce a novel Multi-modal Guided Real-World Face Restoration (MGFR) technique designed to improve the quality of facial image restoration from low-quality inputs. Leveraging a blend of attribute text prompts, high-quality reference images, and identity information, MGFR can mitigate the generation of false facial attributes and identities often associated with generative face restoration methods. By incorporating a dual-control adapter and a two-stage training strategy, our method effectively utilizes multi-modal prior information for targeted restoration tasks. We also present the Reface-HQ dataset, comprising over 21,000 high-resolution facial images across 4800 identities, to address the need for reference face training images. Our approach achieves superior visual quality in restoring facial details under severe degradation and allows for controlled restoration processes, enhancing the accuracy of identity preservation and attribute correction. Including negative quality samples and attribute prompts in the training further refines the model's ability to generate detailed and perceptually accurate images.

cs.CV↗

arXiv2025

An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the ``value of information" as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6G networks.

cs.GT↗

arXiv2025

ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions

In Automatic Modulation Classification (AMC), deep learning methods have shown remarkable performance, offering significant advantages over traditional approaches and demonstrating their vast potential. Nevertheless, notable drawbacks, particularly in their high demands for storage, computational resources, and large-scale labeled data, which limit their practical application in real-world scenarios. To tackle this issue, this paper innovatively proposes an automatic modulation classification model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN). The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity. The MALWNN framework, using ALWNN as an encoder and incorporating prototype network technology, decreases the model's dependence on the quantity of samples. Simulation results indicate that this model performs remarkably well on mainstream datasets. Moreover, in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply - Accumulate Complexity (NMACC), ALWNN significantly reduces computational complexity compared to existing methods. This is further validated by real-world system tests on USRP and Raspberry Pi platforms. Experiments with MALWNN show its superior performance in few-shot learning scenarios compared to other algorithms.

cs.LG↗

arXiv2025

Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training

Purpose: This study proposes a novel anatomically-driven dynamic modeling framework for coronary arteries using skeletal skinning weights computation, aiming to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications. Methods: We developed a computational framework based on biharmonic energy minimization for skinning weight calculation, incorporating volumetric discretization through tetrahedral mesh generation. The method implements temporal sampling and interpolation for continuous vessel deformation throughout the cardiac cycle, with mechanical constraints and volume conservation enforcement. The framework was validated using clinical datasets from 5 patients, comparing interpolated deformation results against ground truth data obtained from frame-by-frame segmentation across cardiac phases. Results: The proposed framework effectively handled interactive vessel manipulation. Geometric accuracy evaluation showed mean Hausdorff distance of 4.96 +- 1.78 mm and mean surface distance of 1.78 +- 0.75 mm between interpolated meshes and ground truth models. The Branch Completeness Ratio achieved 1.82 +- 0.46, while Branch Continuity Score maintained 0.84 +- 0.06 (scale 0-1) across all datasets. The system demonstrated capability in supporting real-time guidewire-vessel collision detection and contrast medium flow simulation throughout the complete coronary tree structure. Conclusion: Our skinning weight-based methodology enhances model interactivity and applicability while maintaining geometric accuracy. The framework provides a more flexible technical foundation for virtual surgical training systems, demonstrating promising potential for both clinical practice and medical education applications. The code is available at https://github.com/ipoirot/DynamicArtery.

cs.GR↗

arXiv2025

QoE-oriented Communication Service Provision for Annotation Rendering in Mobile Augmented Reality

As mobile augmented reality (MAR) continues to evolve, future 6G networks will play a pivotal role in supporting immersive and personalized user experiences. In this paper, we address the communication service provision problem for annotation rendering in edge-assisted MAR, with the objective of optimizing spectrum resource utilization while ensuring the required quality of experience (QoE) for MAR users. To overcome the challenges of user-specific uplink data traffic patterns and the complex operational mechanisms of annotation rendering, we propose a digital twin (DT)-based approach. We first design a DT specifically tailored for MAR applications to learn key annotation rendering mechanisms, enabling the network controller to access MAR application-specific information. Then, we develop a DT based QoE modeling approach to capture the unique relationship between individual user QoE and spectrum resource demands. Finally, we propose a QoE-oriented resource allocation algorithm that decreases resource utilization compared to conventional net work slicing-based approaches. Simulation results demonstrate that our DT-based approach outperforms benchmark approaches in the accuracy and granularity of QoE modeling.

eess.IV↗

arXiv2025

Observation of mechanical kink control and generation via phonons

Kinks (or domain walls) are localized transitions between distinct ground states associated with a topological invariant, and are central to many phenomena across physics, from condensed matter to cosmology. While phonon (i.e., small-amplitude vibration) wave packets have been theorized to deterministically interact with kinks and initiate their movement, this interaction has remained elusive in experiments, where only uncontrollable stochastic kink motion generated by thermal phonons or dislocation glide by low-frequency quasi-static loading have been observed. This is partly because all physical systems that support kinks are, at some level, discrete, making deterministic phonon control of kinks extremely challenging due to the existence of Peierls-Nabarro (PN) barrier. Here, we demonstrate, for the first time, experimental observation of phonon-mediated control and generation of mechanical kinks, which we enable using a topological metamaterial that constitutes an elastic realization of the Kane-Lubensky chain model. Our metamaterial overcomes the PN barrier by supporting a single, topologically protected kink that requires zero energy to form and move. Using simulations that show close agreement with our experimental observations, we also reveal unique dynamics of phonon interplay with highly discrete kinks, including long-duration motion and a continuous family of internal modes, features absent in other discrete nonlinear systems. This work introduces a new paradigm for topological kink control, with potential applications in material stiffness tuning, shape morphing, locomotion, and robust signal transmission.

nlin.PS↗

arXiv2025

Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming

This paper addresses a class of (non-)convex optimization problems subject to general convex constraints, which pose significant challenges for traditional methods due to their inherent non-convexity and diversity. Conventional convex optimization-based solvers often struggle to efficiently handle these problems in their most general form. While neural network (NN)-based approaches offer a promising alternative, ensuring the feasibility of NN-generated solutions and effectively training the NN remain key hurdles, largely because finite-capacity networks can produce infeasible outputs. To overcome these issues, we propose a projection-based method that projects any infeasible NN output onto the feasible domain, thus guaranteeing strict adherence to the constraints without compromising the NN's optimization capability. Furthermore, we derive the objective function values for both the raw NN outputs and their projected counterparts, along with the gradients of these values with respect to the NN parameters. This derivation enables label-free (unsupervised) training, reducing reliance on labeled data and improving scalability. Experimental results demonstrate that the proposed projection-based method consistently ensures feasibility.

eess.SY↗