标题： 用于生成式无线网络中无线地图估计的去噪扩散概率模型 显示英文标题

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

摘要： 随着对高速和可靠无线网络需求的增加，推动了毫米波和5G无线电等技术的进步，这需要高效规划和及时部署无线接入点。 此过程中的关键工具是无线电图，这是一种表示射频信号强度的图形，在优化整体网络性能中起着至关重要的作用。 然而，现有的估计无线电图的方法由于需要大量真实世界数据收集或计算密集型射线追踪分析而面临挑战，这成本高且耗时。 受生成式AI技术在大型语言模型和图像生成中成功应用的启发，我们探索其在无线网络领域的潜在应用。 在本工作中，我们提出 RM-Gen，一种新颖的生成框架，利用条件去噪扩散概率模型，使用最少且易于收集的数据合成无线电图。 然后，我们引入一种环境感知的方法来选择关键数据片段，增强生成模型的适用性和可用性。 全面评估表明，RM-Gen在60 GHz和sub-6GHz频段运行的网络中生成无线电图的准确率超过95%，优于基线GAN和pix2pix模型。 这种方法为各种下游网络优化任务提供了一种成本效益高、适应性强的解决方案。 显示英文摘要

摘要： 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.