In wireless networks, radio-frequency (RF) maps are critical for tasks such as capacity planning, coverage estimation, and localization. Traditional approaches for obtaining RF maps, including site surveys and ray-tracing simulations, are labor-intensive or computationally expensive, especially at high frequencies and dense network deployments. Generative AI offers a promising alternative for RF map synthesis. However, supervised methods are often infeasible due to the lack of reliable labeled training data, while purely unsupervised methods typically lack explicit control over the synthesis process. To address these challenges, we propose SAIL (Spatial-Angular Interpretable Feature Learning), a generative adversarial network (GAN)-based framework that learns interpretable and controllable latent variables directly from unlabeled RF maps and enables targeted RF map synthesis at inference time through latent-variable manipulation. SAIL builds on the information-maximizing GAN (InfoGAN) principle to learn a structured representation comprising: (i) a categorical latent variable that captures discrete floor-plan regions associated with Tx location and (ii) a continuous latent variable that captures angular variations corresponding to the Tx boresight angle, without requiring any location or orientation supervision during training. We further adopt a Wasserstein GAN objective with a gradient penalty to improve training stability and synthesis quality. Our results using ray-tracing-based RF maps indicate that SAIL learns physically meaningful spatial-angular factors and enables fast controlled RF map synthesis, achieving an average SSIM of 0.8576 and an average PSNR of 23.33 dB relative to ray-tracing simulations.

翻译：在无线网络中，射频（RF）地图对于容量规划、覆盖估计和定位等任务至关重要。获取射频地图的传统方法，包括现场勘测和射线追踪仿真，是劳动密集型或计算成本高昂的，尤其是在高频和密集网络部署场景下。生成式人工智能为射频地图合成提供了一种有前景的替代方案。然而，由于缺乏可靠的标注训练数据，监督方法通常不可行，而纯粹的无监督方法通常缺乏对合成过程的显式控制。为了解决这些挑战，我们提出了SAIL（空间-角度可解释特征学习），这是一个基于生成对抗网络（GAN）的框架，它直接从无标注的射频地图中学习可解释且可控的潜在变量，并通过在推理时操纵潜在变量来实现有针对性的射频地图合成。SAIL建立在信息最大化GAN（InfoGAN）原理之上，以学习一种结构化表示，该表示包含：（i）一个捕获与发射机（Tx）位置相关的离散楼层平面图区域的分类潜在变量，以及（ii）一个捕获对应于Tx波束指向角的角度变化的连续潜在变量，而训练期间无需任何位置或方向监督。我们进一步采用了带有梯度惩罚的Wasserstein GAN目标，以提高训练稳定性和合成质量。我们使用基于射线追踪的射频地图进行实验的结果表明，SAIL学习了具有物理意义的空间-角度因子，并实现了快速可控的射频地图合成，相对于射线追踪仿真，平均结构相似性指数（SSIM）达到0.8576，平均峰值信噪比（PSNR）达到23.33 dB。