面向生成式无线网络中无线电图估计的去噪扩散概率模型
Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks
AI总结：
针对现有无线电图估计方法成本高、耗时长的问题，提出基于条件去噪扩散概率模型的RM-Gen框架，结合环境感知数据筛选，仅用少量数据即可生成高精度无线电图，性能优于基线模型，为网络优化提供低成本方案。
摘要：
对高速可靠无线网络日益增长的需求推动了毫米波、5G 无线等技术的发展，这要求对无线接入点进行高效规划与及时部署。该过程中的核心工具是无线电图——一种射频信号强度的图形化表示，在优化整体网络性能中发挥着关键作用。然而，现有的无线电图估计方法面临挑战：要么需要大量的现实世界数据采集，要么需要计算密集的射线追踪分析，成本高且耗时长。受生成式AI技术在大语言模型和图像生成领域取得成功的启发，我们探索了其在无线网络领域的潜在应用。本研究中，我们提出了RM-Gen——一种新型生成式框架，它利用条件去噪扩散概率模型，仅用少量易采集的数据即可合成无线电图。随后我们引入了一种环境感知方法来筛选关键数据片段，提升了生成模型的适用性与易用性。综合评估表明，RM-Gen在生成工作于60 GHz和Sub-6 GHz频段的网络的无线电图时，准确率超过95%，性能优于基线GAN模型和pix2pix模型。该方法为各类下游网络优化任务提供了一种高性价比、适配性强的解决方案。
英文摘要：
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.
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