Xiucheng Wang
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View article: UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks
UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks Open
Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are…
View article: RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication Open
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driv…
View article: On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach Open
The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming must be delivered on demand while minimizing network operatio…
View article: Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application
Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application Open
In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, to enhance the robustness against both channel noise and transmission data distribution shifts. A theoretical founda…
View article: RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction
RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction Open
View article: Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion Open
Understanding how neural networks learn and optimize remains a central point in machine learning, with implications for designing better models. While techniques like dropout and batch normalization are widely used, the underlying principl…
View article: RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction
RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction Open
In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-$k^2$, for accurate and efficient multipath-aware radio map (RM) construction. As future wireless communication evolves towards environment-aw…
View article: Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming
Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming Open
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 opti…
View article: Is AI Robust Enough for Scientific Research?
Is AI Robust Enough for Scientific Research? Open
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five div…
View article: Exploring the uncertainty principle in neural networks through binary classification
Exploring the uncertainty principle in neural networks through binary classification Open
Neural networks are reported to be vulnerable under minor and imperceptible attacks. The underlying mechanism and quantitative measure of the vulnerability still remains to be revealed. In this study, we explore the intrinsic trade-off bet…
View article: Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model
Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model Open
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 …
View article: Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion Open
Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles …
View article: GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network Open
Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without suffici…
View article: RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction Open
Radio map (RM) is a promising technology that can obtain pathloss based on only location, which is significant for 6G network applications to reduce the communication costs for pathloss estimation. However, the construction of RM in tradit…
View article: Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization
Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization Open
In this paper, we investigate a special class of quadratic-constrained quadratic programming (QCQP) with semi-definite constraints. Traditionally, since such a problem is non-convex and N-hard, the neural network (NN) is regarded as a prom…
View article: Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks
Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks Open
With the rapid development of artificial intelligence technology, the field of reinforcement learning has continuously achieved breakthroughs in both theory and practice. However, traditional reinforcement learning algorithms often entail …
View article: Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development
Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development Open
The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate an…
View article: Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges Open
This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when …
View article: Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks Open
Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to…
View article: Online Self-Help Acceptance and Commitment Therapy Module for College Students with Higher Gaming Disorder During COVID-19: A Pilot Study
Online Self-Help Acceptance and Commitment Therapy Module for College Students with Higher Gaming Disorder During COVID-19: A Pilot Study Open
During the COVID-19 pandemic, there was an increase in online gaming behaviour among college students. This study aimed to examine the impact of online self-help interventions consisting of different components within the Acceptance and Co…
View article: Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks
Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks Open
In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing am…
View article: Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach Open
As a promising paradigm federated Learning (FL) is widely used in privacy-preserving machine learning, which allows distributed devices to collaboratively train a model while avoiding data transmission among clients. Despite its immense po…
View article: Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach Open
As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the …
View article: Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles Open
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods…
View article: Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication Open
Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication. As a result, UAVs have been extensively used a…
View article: Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach Open
Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: …
View article: RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity
RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity Open
Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training effici…
View article: RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity
RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity Open
Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training effici…
View article: AI for UAV-Assisted IoT Applications: A Comprehensive Review
AI for UAV-Assisted IoT Applications: A Comprehensive Review Open
With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due …
View article: Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning Open
In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowled…