RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction

• URL: http://arxiv.org/abs/2408.08593v2
• Date: Sat, 2 Nov 2024 04:52:54 GMT
• Title: RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
• Authors: Xiucheng Wang, Keda Tao, Nan Cheng, Zhisheng Yin, Zan Li, Yuan Zhang, Xuemin Shen,
• Abstract summary: Radio map (RM) is a promising technology that can obtain pathloss based on only location. In this paper, a 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. 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.
• Score: 42.596399621642234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks [0.0]
  We propose FedVaccine, a novel AMC model aimed at improving generalizability across signals with varying noise levels. FedVaccine overcomes the limitations of existing FL-based AMC models' linear aggregation by employing a split-learning strategy. These findings highlight FedVaccine's potential to enhance the reliability and performance of AMC systems in practical wireless network environments.
  arXiv  Detail & Related papers  (2024-10-16T17:48:47Z)
• RCDM: Enabling Robustness for Conditional Diffusion Model [2.4915590770454035]
  Conditional diffusion model (CDM) enhances the standard diffusion model by providing more control. Inaccurate conditional inputs in the inverse process of CDM can easily lead to generating fixed errors in the neural network. We propose a lightweight solution, the Robust Conditional Diffusion Model (RCDM)
  arXiv  Detail & Related papers  (2024-08-05T13:12:57Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Radio Generation Using Generative Adversarial Networks with An Unrolled Design [18.049453261384013]
  We develop a novel GAN framework for radio generation called "Radio GAN" The first is learning based on sampling points, which aims to model an underlying sampling distribution of radio signals. The second is an unrolled generator design, combined with an estimated pure signal distribution as a prior, which can greatly reduce learning difficulty.
  arXiv  Detail & Related papers  (2023-06-24T07:47:22Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals [104.11663769306566]
  We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
  arXiv  Detail & Related papers  (2023-03-11T16:29:13Z)
• Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems [2.596903831934905]
  We propose a hybrid approach where Laplace-based Bayesian inference is combined with an ANN architecture for obtaining approximations to the ODE trajectories. The effectiveness of our proposed methods is demonstrated using an epidemiological system with non-analytical solutions, the Susceptible-Infectious-Removed (SIR) model for infectious diseases.
  arXiv  Detail & Related papers  (2022-10-17T09:02:41Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.