RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
Xidian University（西安电子科技大学）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；University of Waterloo（滑铁卢大学）
Xiucheng Wang, Keda Tao, Nan Cheng, Zhisheng Yin, Zan Li, Yuan Zhang, Xuemin Shen
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 traditional is either computationally intensive or depends on costly sampling-based pathloss measurements. Although the neural network (NN)-based method can efficiently construct the RM without sampling, its performance is still suboptimal. This is primarily due to the misalignment between the generative characteristics of the RM construction problem and the discrimination modeling exploited by existing NN-based methods. Thus, to enhance RM construction performance, in this paper, the sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction. In addition, to enhance the diffusion model's capability of extracting features from dynamic environments, an attention U-Net with an adaptive fast Fourier transform module is employed as the backbone network to improve the dynamic environmental features extracting capability. Meanwhile, the decoupled diffusion model is utilized to further enhance the construction performance of RMs. Moreover, a comprehensive theoretical analysis of why the RM construction is a generative problem is provided for the first time, from both perspectives of data features and NN training methods. Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio. The code is available at https://github.com/UNIC-Lab/RadioDiff.
RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信工程国家重点实验室和西安电子科技大学电信工程学院）；School of Physics, Xidian University（西安电子科技大学物理学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信国家重点实验室和西安电子科技大学通信工程学院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Science and Engineering (SSE), Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen)（科学与工程学院、深圳未来网络智能研究院、广东省未来网络智能重点实验室，香港中文大学（深圳））；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信工程国家重点实验室和西安电子科技大学电信工程学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信国家重点实验室和西安电子科技大学电信工程学院）；School of Artificial Intelligence, Xidian University（人工智能学院，西安电子科技大学）；School of Science and Engineering (SSE), Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen)（科学与工程学院（SSE）、深圳未来网络智能研究院（FNii-Shenzhen）及广东省级未来网络智能重点实验室，香港中文大学（深圳））；Department of Electrical and Computer Engineering, University of Waterloo（电气与计算机工程系，滑铁卢大学）
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
Else Kroener Fresenius Center for Digital Health (EKFZ)（埃尔泽·克勒纳·菲森纽斯数字健康中心（EKFZ））；Technical University Dresden（德累斯顿工业大学）；University Hospital Aachen（亚琛大学医院）；UC Berkeley（加州大学伯克利分校）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；Center for Artificial Intelligence in Medicine and Imaging (AIMI)（医学与成像人工智能中心（AIMI））；Stanford University（斯坦福大学）；University Hospital Dresden（德累斯顿大学医院）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks
Friedrich-Alexander Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡分校）；Technical University of Applied Sciences Amberg-Weiden（安贝格-魏登应用技术大学）；Siemens Healthcare GmbH（西门子医疗有限公司）
RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion
Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Huawei Technologies Co., Ltd（华为技术有限公司）
RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data
School of Science and Engineering (SSE)（科学与工程学院）；Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来网络智能研究所）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）；Chinese University of Hong Kong at Shenzhen（香港中文大学（深圳））；School of Advanced Engineering（先进工程学院）；Great Bay University（大亚湾大学）
Data Science Institute, University of Technology Sydney（悉尼科技大学数据科学学院）；COMSATS University Islamabad（COMSATS大学伊斯兰堡分校）；National Center of Artificial Intelligence（国家人工智能中心）；Macquarie University（麦考瑞大学）
STAN-CT: Standardizing CT Image using Generative Adversarial Network
University of Kentucky（肯塔基大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Generative imaging for radio interferometry with fast uncertainty quantification
Mullard Space Science Laboratory (MSSL)（穆拉德空间科学实验室）；University College London (UCL)（伦敦大学学院）；Leiden Observatory（莱顿天文台）；Leiden University（莱顿大学）；IRFU CEA Université Paris-Saclay（CEA巴黎-萨克雷大学IRFU）；Department of Computer Science（计算机科学系）；Alan Turing Institute（艾伦·图灵研究所）
