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

Xiucheng Wang, Keda Tao, Nan Cheng, Zhisheng Yin, Zan Li, Yuan Zhang, Xuemin Shen

arXiv:2408.08593v3cs.LGeess.SY

TL;DR

The paper addresses sampling-free radio-map construction, where traditional methods are costly or computationally intensive and existing neural networks remain limited by discriminative modeling. It proposes RadioDiff, a conditional diffusion-based generative model with attention U-Net, adaptive Fourier-transform features, and decoupled diffusion. RadioDiff achieves state-of-the-art performance across accuracy, structural similarity, and peak signal-to-noise ratio, while requiring more resources and longer inference.

  • Problem

    Sampling-free radio-map construction remains limited because traditional methods require costly measurements or heavy computation, while existing neural-network methods use discriminative modeling despite the problem's generative characteristics.

  • Method

    RadioDiff models sampling-free radio-map construction as a conditional generative problem using diffusion, with attention U-Net, adaptive Fourier-transform modules, and decoupled diffusion.

  • Results

    RadioDiff achieves state-of-the-art performance across accuracy, structural similarity, and peak signal-to-noise ratio.

  • Takeaways & Limitations

    RadioDiff provides a diffusion-based approach for constructing accurate and sharp radio maps, including in dynamic-environment settings.

  • Takeaways & Limitations

    The diffusion model requires more resources and has longer inference time.

Abstract

from arXiv · show

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.

I. INTRODUCTION

Radio map construction is important for obtaining pathloss from location information, but traditional and existing neural-network approaches struggle with measurement cost, computation, dynamic environments, and detailed RM textures. RadioDiff addresses this mismatch by modeling sampling-free RM construction as a conditional generative problem using diffusion.

  • Radio maps obtain pathloss from location information, reducing reliance on pilot transmission and signal processing for pathloss estimation.
  • Sampling-based construction requires costly measurements and cannot construct maps in never-to-reach regions, while electromagnetic ray tracing is computationally intensive.Too few or inaccurate measurements reduce construction quality, whereas many high-precision measurements increase costs.
  • Traditional methods are restricted to static maps and cannot reflect real-time pathloss changes caused by moving vehicles or other environmental alterations.Time-series measurements occur at different times, while ray tracing relies on a static 3D scene and can take several minutes or longer.
  • Existing neural-network methods mainly target static environments and perform poorly on dynamic maps with multiple obstacles and detailed textures.MSE-based discriminative training can speed convergence but struggles to capture detailed features and produces blurred edges.
  • RM construction has generative characteristics in both its data features and neural-network training requirements, unlike existing discriminative sampling-free methods.The paper identifies this misalignment as the fundamental reason existing methods have limited construction performance.
  • RadioDiff models sampling-free RM construction as a conditional generative problem and uses diffusion to construct RMs from base-station location and environmental features.The paper also provides a theoretical analysis of the generative nature of RM construction from data-feature and training-method perspectives.
  • RadioDiff achieves state-of-the-art performance across accuracy, structural similarity, and peak signal-to-noise ratio.

A. Radio Map Construction

Radio-map construction has evolved from sampling-based methods requiring pathloss measurements to sampling-free neural methods using environmental features and base-station information. Existing neural approaches generally treat construction as discriminative supervised learning, motivating RadioDiff’s conditional generative formulation.

  • Sampling-based and sampling-free construction: Sampling-based methods require pathloss measurements, while sampling-free neural methods construct radio maps from environmental features and base-station positions.Sampling-based approaches include nearest-neighbor and local regression methods based on sparse measurements.
  • Sampling-free neural methods: RadioUNet applies a U-Net with mean-squared-error training for sampling-free radio-map construction.Related work also includes transformer- and graph-neural-network-based approaches.
  • Limitations of prior methods: Existing neural methods generally model sampling-free radio-map construction as a discriminative supervised-learning task.The paper identifies this modeling choice as a limitation of prior approaches.
  • Limitations of prior methods: RME-GAN introduces generative-adversarial methods but remains non-sampling-free because it relies on sparse pathloss measurements.This distinguishes it from the proposed sampling-free formulation.
  • RadioDiff: RadioDiff models sampling-free radio-map construction as a conditional generative problem using a diffusion-based method.The paper reports that this approach significantly improves radio-map construction performance.

B. Diffusion Model

Diffusion models generate data by progressively adding noise and then learning a reverse denoising process. In RadioDiff, this generative framework is used to reconstruct radio maps from noisy representations conditioned on relevant inputs.

  • Diffusion-model principles: Diffusion models use a forward process that converts data into noise and a reverse process that reconstructs data through learned denoising.The reverse process uses a neural network to remove noise and generate data from noisy inputs.
  • Diffusion-model principles: Diffusion models have been applied to generative, perception, reinforcement-learning, and optimization tasks.The cited applications include computer vision, natural-language processing, segmentation, detection, and model-based reinforcement learning.
  • Reverse denoising: DDPM generation begins with noise and iteratively denoises it through a learnable reverse Markov chain.The reverse process operates in temporal order from t = T to t = 1.
  • Forward diffusion: The forward process produces increasingly noisy latent variables by combining the original input with Gaussian noise over diffusion steps.As t increases, the noise contribution grows and the data becomes progressively noisier.
  • Forward diffusion: In the noisy representation, √ᾱ_t x_0 preserves the scaled original data while √(1−ᾱ_t)ϵ determines the added noise contribution.The paper explains that the balance shifts toward noise as the diffusion step increases.

2) Reversed Denoise Procedure:

Radio-map construction predicts a pathloss matrix from environmental features and base-station location. The paper argues that this is a conditional generative problem because pathloss values are generated for masked or absent input elements through self-supervised-style recovery.

  • Reversed denoise procedure: The reverse diffusion process starts from unstructured noise and removes it through a learnable reverse Markov chain.The reverse procedure iteratively denoises x_t from t = T to t = 1 while preserving the variance associated with forward diffusion.
  • Reversed denoise procedure: The reverse process retains a stochastic term so its output matches the forward process in variance rather than only its mean.Removing the second term would make the denoised result equal to the forward-process mean.
  • Problem formulation: The task predicts an N × N pathloss matrix from environmental features and base-station location by minimizing a criterion between prediction and ground truth.The environmental setting includes static and dynamic obstacles and a single-antenna base station.
  • Generative characterization: Pathloss values occupy elements absent from the environmental matrices, so the network must generate continuous values rather than classify nodes with finite hyperplanes.This data structure is presented as evidence against a purely discriminative formulation.
  • Generative characterization: The paper concludes that radio-map construction is a conditional generative problem rather than a discriminative one.This conclusion combines the data-feature and training-method perspectives.
  • Generative characterization: The environmental information acts as unmasked data, while pathloss elements are masked targets recovered through self-supervised learning.The base-station location conditions the pathloss distribution, making the task conditional.

IV. DIFFUSION-BASED RM CONSTRUCTION

RadioDiff uses a diffusion backbone to construct radio maps in latent space, conditioning denoising on environmental and base-station information. Its design combines VAE compression, attention-based U-Net processing, adaptive FFT filtering, and decoupled diffusion.

  • Framework: RadioDiff uses a generative diffusion model as the backbone for radio-map construction.The choice follows the paper’s characterization of radio-map construction as a generative problem.
  • Initial processing: A VAE encodes the pathloss matrix into a latent vector, reducing the diffusion model’s output-space dimension and improving training efficiency.The decoder reconstructs the radio map from the predicted latent representation.
  • Initial processing: The prompt combines static obstacles, dynamic obstacles, and base-station location as a three-channel tensor.The base-station channel marks the access-point location, while the obstacle channels encode environmental features.
  • Initial processing: The VAE is trained independently before diffusion training, and its parameters remain fixed during subsequent diffusion-model training.The VAE encoder and decoder are trained in an autoencoder style using the pathloss matrix.
  • Decoupled diffusion: The decoupled diffusion model separates data attenuation from noise addition, reducing early-step variance and improving training and inference stability.The paper also associates the decoupled structure with more efficient generation by mitigating prolonged inference time.
  • Conditional denoising: RadioDiff uses an attention-based conditional architecture to correlate generated radio maps with environmental and base-station prompts.The prompt is embedded and projected into intermediate U-Net layers through cross-attention.

C. Adaptive FFT Filter for DRM Enhancement

Conventional convolutional layers can miss high-frequency components in radio maps, producing overly smooth outputs. RadioDiff addresses this with an adaptive FFT filter that learns frequency weights and restores filtered features through inverse transformation and residual connection.

  • Conventional convolutional layers struggle to capture high-frequency components, causing blurred and suboptimal radio-map outputs in dynamic environments.
  • The adaptive FFT filter transforms encoder feature maps from the spatial domain into the frequency domain using FFT.
  • A learnable weight matrix applies Hadamard-product filtering to emphasize relevant frequencies and attenuate irrelevant ones.
  • The filtering process enhances high-frequency features while suppressing less critical frequencies through data-driven global frequency adjustment.
  • Filtered frequency-domain features are converted back with IFFT, while a residual connection preserves information and mitigates filtering losses.

V. EXPERIMENTS

The experiments use RadioMapSeer to evaluate radio-map construction under static and dynamic environments. The dataset provides geographically distinct maps, transmitter conditions, and Maxwell-equation-based ground-truth radio maps for training and testing.

  • The dataset uses 500 maps for training and 200 maps for testing, with no overlapping terrain information between the two splits.
  • Each map is a 256 × 256 binary morphological image in which each pixel represents one square meter and marks building occupancy.
  • Transmitters use 23 dBm power and 5.9 GHz carrier frequency, with transmitter and receiver heights of 1.5 meters and building heights of 25 meters.
  • Static radio maps use building effects for ground truth, whereas dynamic radio maps additionally include randomly generated vehicles along roads.

B. Metrics

The evaluation combines error, structural, and fidelity measures to assess radio-map reconstruction. SSIM and PSNR are added because overall error metrics do not directly capture structural details and edge-signal fidelity.

  • NMSE and RMSE are adopted as conventional metrics for evaluating radio-map reconstruction accuracy.
  • SSIM and PSNR are introduced because MSE-based measures do not directly address structural information and detail reconstruction.
  • SSIM evaluates preservation of structural information, including brightness, contrast, and structural detail in reconstructed radio maps.
  • PSNR measures signal-to-noise fidelity and is used to assess overall reconstruction quality and edge-detail quality.
  • Table II marks the highest results in bold red and the second highest in underlined blue, with upward arrows indicating metrics where higher values are preferred.

3) PSNR:

PSNR is used to quantify reconstruction fidelity and edge-detail quality, while the experiments implement RadioDiff and compare it with CNN-, GAN-, and SSM-based radio-map methods under aligned data settings.

  • 3) PSNR:: PSNR is expressed in decibels and provides an approximate measure of reconstructed image quality.
  • 3) PSNR:: For radio maps, PSNR assesses both overall image quality and the accuracy of reconstructed edge signals.
  • RadioDiff training uses a two-phase process that first trains an autoencoder and then trains the denoising diffusion U-Net.
  • The implementation uses latent-space processing through a VAE to reduce video-memory consumption during diffusion-model training.
  • RadioDiff is compared with CNN-, GAN-, and SSM-based methods using aligned training and test data.

1) Comparisons for SRM:

On the RadioMapSeer-Test dataset, RadioDiff outperforms competing methods across error and structural metrics for SRM and maintains the best results under dynamic conditions. Its generated maps show sharper edges and better handling of overlapping signals.

  • SRM comparison: RadioDiff outperforms other methods on NMSE, RMSE, SSIM, and PSNR for SRM scenarios.The reported results indicate more accurate predictions and generated radio maps.
  • SRM comparison: RadioDiff achieves especially strong PSNR performance, producing clearer and sharper structural edges than other methods.
  • Qualitative comparison: RadioDiff maps closely resemble ground truth with well-defined edge features, unlike competing outputs affected by positioning inaccuracies, edge blurring, or ambiguity.RME-GAN performs poorly in sampling-free construction because it relies on sampling-position measurements.
  • DRM comparison: RadioDiff consistently delivers the best results across all indicators in DRM scenarios despite the general performance decline caused by dynamic environmental factors.
  • DRM comparison: RadioDiff is more sensitive to vehicles and other dynamic factors, while competing models often produce substantial blurring and distortion in complex, overlapping-signal environments.

E. Ablation Study

The ablation and discussion results show that AFT improves edge detection and performance, while RadioDiff’s main trade-off is higher resource use and longer inference than other neural methods. The paper also identifies separate-training requirements and X2X construction as important scope considerations.

  • Ablation Study: AFT improves RadioDiff’s detection and representation of edge signals, especially when signals are superposed.With AFT, generated images exhibit increasingly sharper edges, and quantitative comparisons report better performance.
  • Efficiency: RadioDiff requires more memory and longer inference time than alternative models, although its inference delay remains below one second.
  • Training considerations: VAE training is currently performed separately before RadioDiff training, but reusable pretrained VAE weights could streamline subsequent research.
  • Efficiency: The paper identifies a performance–efficiency trade-off and suggests NN compression and DDIM-based efficient inference as possible directions.
  • Scope and future directions: RadioDiff concentrates on one-to-any RM construction, while any-to-any construction remains a distinct challenge addressed as future scope.The paper explains that adjusting the BS-position prompt could enable X2X prediction in the sampling-free framework.
  • Conclusion: RadioDiff combines AFTs with decoupled diffusion to construct accurate, sharp radio maps and achieves qualitative and quantitative superiority in extensive experiments.
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