Paying Deformable Attention to Sparse Spatial Observations for Deep Radio Map Estimation

Next-generation communication systems, such as 6G, require real-time, high-resolution modeling of electromagnetic signal propagation to meet the challenges of ultra-high data rates, dynamic environments, and sparse spatial observations. Radio map estimation (RME) plays a critical role in spectrum management by providing spatial visualization of signal distributions. However, existing methods struggle with the complexities of sparse sampling and nonlinear propagation dynamics, often failing to adapt to the demands of dynamic and unpredictable environments. To overcome these challenges, this paper introduces DAT-Unet, a deformable attention transformer-based framework specifically designed for RME under extreme sparsity. DAT-Unet utilizes deformable attention to dynamically adjust receptive fields, enabling it to capture intricate spatial correlations and adapt to complex channel variations. Its geometry-aware modeling integrates spatial priors, while multi-scale contextual fusion enhances feature representation across diverse propagation patterns. Extensive experiments on public datasets demonstrate the superiority of DAT-Unet, achieving state-of-the-art performance in RME tasks, even with spatial sampling rates several orders of magnitude lower than those required by existing methods. By addressing the challenges of sparse sampling and dynamic environmental complexities, this work highlights the potential of deformable attention mechanisms to enhance RME for next-generation communication systems.