13 citations · 22 across the 56 of their papers we have counts for
17 papers · 1 filter
RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction
Zixuan Guo, Xiucheng Wang, Nan Cheng
Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps…
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
Haozhe Jia, Pengyu Yin, Wenshuo Chen +6
Physics-informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and prone to shortcut learning under s…
RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse
Xiucheng Wang, Peilin Zheng, Honggang Jia +4
Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed net…
iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization
Xiucheng Wang, Tingwei Yuan, Yang Cao +3
Radio maps (RMs) serve as environment-aware electromagnetic (EM) representations that connect scenario geometry and material properties to the spatial distribution of signal streng…
Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Yuhao Pan, Xiucheng Wang, Fushuo Huo +2
Radio frequency fingerprint identification (RFFI) is a key technique for wireless network security, leveraging intrinsic hardware imperfections to enable transmitter identification…
UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks
Honggang Jia, Xiucheng Wang, Nan Cheng +2
Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RM…
Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application
Xiucheng Wang, Honggang Jia, Nan Cheng
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…
RadioDiff-3D: A 3D3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng +5
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics.…
RadioDiff-: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction
Xiucheng Wang, Qiming Zhang, Nan Cheng +4
In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-, for accurate and efficient multipath-aware radio map (RM) construction. As f…
RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
Xiucheng Wang, Keda Tao, Nan Cheng +4
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 pat…
Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization
Xiucheng Wang, Qi Qiu, Nan Cheng
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-conve…
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Xiucheng Wang, Nan Cheng, Longfei Ma +3
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 UAV…
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
Ruijin Sun, Xiao Yang, Nan Cheng +2
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 bu…
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach
Jinglong Shen, Xiucheng Wang, Nan Cheng +3
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 avoid…
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
Longfei Ma, Nan Cheng, Xiucheng Wang +3
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-…
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Xiucheng Wang, Nan Cheng, Longfei Ma +3
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 se…
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
Xiucheng Wang, Longfei Ma, Haocheng Li +3
Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmi…