RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

2026-07-23

AI Summary

Reconstructing wireless signal maps is highly challenging due to spatial complexity and generalization difficulties. This work proposes RadioTrace, a novel framework that, for the first time, integrates transmitter location estimation directly into the denoising loop of a pre-trained diffusion model, enabling high-fidelity signal map reconstruction without requiring fine-tuning at deployment. The approach leverages propagation-guided K-means initialization and stochastic stability analysis to iteratively refine both transmitter positions and signal distributions from sparse measurements. Experimental results demonstrate that RadioTrace achieves state-of-the-art performance among learning-based methods under both random and constrained-region sampling scenarios, exhibiting exceptional adaptability and robustness.

Abstract

Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks. Traditional approaches, including interpolation and deep learning, either struggle to capture complex propagation effects or require large-scale retraining for each new sampling pattern, which limits their generalization. More recently, prior-based methods have combined pre-trained generative models with measurements to reduce the need for deployment-time model fine-tuning, but they typically treat the prior as a simple regularizer and lack explicit transmitter-aware integration. In this paper, we propose RadioTrace, a novel RM estimation framework without deployment-time fine-tuning that tightly integrates sparse RSS measurements with a frozen pre-trained diffusion prior. RadioTrace incorporates transmitter (Tx) location estimation directly into the denoising loop, iteratively refining Tx coordinates based on reconstruction quality to guide the generative process. To further enhance robustness, we introduce a propagation-guided K-means initialization that mitigates poor local minima in the Tx update and provides a geometry-consistent starting point. Moreover, we provide a stochastic stability analysis for the Tx-coordinate refinement component, showing that the Tx update remains stable under perturbations induced by diffusion sampling and Tx-map relaxation. Extensive experiments demonstrate that RadioTrace achieves competitive performance with state-of-the-art learning-based methods under random sampling, and maintains strong reconstruction quality under restricted-area sampling, highlighting its adaptability, robustness, and practical relevance.

Problem

• Radio Map Estimation
• Transmitter-Aware
• Diffusion Model
• Sparse Measurements
• Deployment-Time Fine-Tuning

Innovation

• diffusion prior
• transmitter-aware estimation
• radio map reconstruction
• deployment-free adaptation
• propagation-guided initialization