发表机构
School of Information and Communication Engineering, University of Electronic Science and Technology of China（信息与通信工程学院，电子科学与技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RadioTrace框架，无需部署时微调，将稀疏RSS测量与预训练扩散先验集成，通过纳入Tx位置估计、传播引导的K均值初始化及稳定性分析，在随机和受限区域采样下展现出良好性能，提升了无线电地图估计的适应性、鲁棒性和实用性。
AI 中文摘要
无线电地图（RM）估计旨在从稀疏测量中重建无线信号特征（如接收信号强度（RSS））的空间分布，这对现代无线网络的频谱管理、干扰缓解和定位至关重要。传统方法存在局限，基于先验的方法缺乏发射机感知集成。本文提出RadioTrace，一个无需部署时微调的RM估计框架，将稀疏RSS测量与预训练扩散先验紧密集成。它将发射机（Tx）位置估计直接纳入去噪循环，基于重建质量迭代细化Tx坐标以引导生成过程。引入传播引导的K均值初始化增强鲁棒性，并对Tx坐标细化组件进行随机稳定性分析。实验表明，RadioTrace在随机采样下与基于学习的先进方法性能相当，在受限区域采样下保持强大重建质量。
英文摘要
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.
CommentsIEEE Trans. Wireless Comm
