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Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks

Xuanhao Luo, Zhizhen Li, Zhiyuan Peng, Mingzhe Chen, Yuchen Liu

TL;DR

RM-Gen introduces a conditional denoising diffusion probabilistic model to synthesize radio maps from sparse RSS fragments or transmitter locations, enabling cost-effective planning for mmWave and sub-6 GHz networks. It integrates two encoders for input conditions, an environment-aware RSS fragment selection mechanism, and a ray-tracing–based data-collection pipeline to train on diverse scenarios. Empirical results show RM-Gen achieves up to ~98% accuracy with limited inputs, consistently outperforming cGAN and pix2pix baselines across indoor and outdoor settings, and providing practical use cases for AP, BS, and UAV deployment. The approach offers a scalable, data-efficient tool for network planning, optimization, and digital twin applications in modern wireless ecosystems.

Abstract

The increasing demand for high-speed and reliable wireless networks has driven advancements in technologies such as millimeter-wave and 5G radios, which requires efficient planning and timely deployment of wireless access points. A critical tool in this process is the radio map, a graphical representation of radio-frequency signal strengths that plays a vital role in optimizing overall network performance. However, existing methods for estimating radio maps face challenges due to the need for extensive real-world data collection or computationally intensive ray-tracing analyses, which is costly and time-consuming. Inspired by the success of generative AI techniques in large language models and image generation, we explore their potential applications in the realm of wireless networks. In this work, we propose RM-Gen, a novel generative framework leveraging conditional denoising diffusion probabilistic models to synthesize radio maps using minimal and readily collected data. We then introduce an environment-aware method for selecting critical data pieces, enhancing the generative model's applicability and usability. Comprehensive evaluations demonstrate that RM-Gen achieves over 95% accuracy in generating radio maps for networks that operate at 60 GHz and sub-6GHz frequency bands, outperforming the baseline GAN and pix2pix models. This approach offers a cost-effective, adaptable solution for various downstream network optimization tasks.

Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks

TL;DR

RM-Gen introduces a conditional denoising diffusion probabilistic model to synthesize radio maps from sparse RSS fragments or transmitter locations, enabling cost-effective planning for mmWave and sub-6 GHz networks. It integrates two encoders for input conditions, an environment-aware RSS fragment selection mechanism, and a ray-tracing–based data-collection pipeline to train on diverse scenarios. Empirical results show RM-Gen achieves up to ~98% accuracy with limited inputs, consistently outperforming cGAN and pix2pix baselines across indoor and outdoor settings, and providing practical use cases for AP, BS, and UAV deployment. The approach offers a scalable, data-efficient tool for network planning, optimization, and digital twin applications in modern wireless ecosystems.

Abstract

The increasing demand for high-speed and reliable wireless networks has driven advancements in technologies such as millimeter-wave and 5G radios, which requires efficient planning and timely deployment of wireless access points. A critical tool in this process is the radio map, a graphical representation of radio-frequency signal strengths that plays a vital role in optimizing overall network performance. However, existing methods for estimating radio maps face challenges due to the need for extensive real-world data collection or computationally intensive ray-tracing analyses, which is costly and time-consuming. Inspired by the success of generative AI techniques in large language models and image generation, we explore their potential applications in the realm of wireless networks. In this work, we propose RM-Gen, a novel generative framework leveraging conditional denoising diffusion probabilistic models to synthesize radio maps using minimal and readily collected data. We then introduce an environment-aware method for selecting critical data pieces, enhancing the generative model's applicability and usability. Comprehensive evaluations demonstrate that RM-Gen achieves over 95% accuracy in generating radio maps for networks that operate at 60 GHz and sub-6GHz frequency bands, outperforming the baseline GAN and pix2pix models. This approach offers a cost-effective, adaptable solution for various downstream network optimization tasks.
Paper Structure (23 sections, 19 equations, 13 figures, 3 tables, 3 algorithms)

This paper contains 23 sections, 19 equations, 13 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Radio Map Prediction
  4. Generative Diffusion Models for Wireless Networks
  5. Problem Formulation and Preliminary on Denoising Diffusion Probabilistic Models
  6. Problem Formulation
  7. Denoising Diffusion Probabilistic Models
  8. Proposed Scheme for Radio Map Generation
  9. Conditional DDPM for Radio Map Generation
  10. Condition Encoders Design
  11. Environment-Aware RSS Fragments Selection
  12. Map-based Data Collection with Ray-tracing Analysis
  13. Performance Evaluation
  14. Performance of DDPM
  15. Radio Map Generation with RSS Fragments
  16. ...and 8 more sections

Figures (13)

  • Figure 1: Radio maps with different base station (BS)/AP locations (denoted by stars) in a mmWave network scenario.
  • Figure 2: Overview of our conditional diffusion model RM-Gen.
  • Figure 3: 3-D scenario layout of (a) an indoor scenario and (b) an outdoor scenario.
  • Figure 4: Map generation process over $T$ steps.
  • Figure 5: Model loss curve using (a) partial RSS fragments and (b) Tx locations as conditions.
  • ...and 8 more figures