Deep Learning-Based Radio Coverage Using Ray Tracing Simulations in Urban Environments
Accurate wireless channel prediction in urban environments is essential for network planning and optimization, but traditional ray tracing (RT) simulations are computationally expensive. This paper presents a deep learning approach that learns from sparse RT simulations to predict received power for unseen transmitter locations. We propose a spatial attention convolutional neural network with an encoder-decoder structure incorporating convolutional block attention modules to prioritize critical regions, such as propagation boundaries, complemented by a distance-aware loss emphasizing accuracy near transmitters and borders. Trained on 80 heatmaps from a $500 mathrm{m} \times 500 mathrm{m}$ urban area, each on a $\mathbf{3 4} \times \mathbf{3 4}$ receiver grid (1,156 positions), the model achieves RMSE of \~{}22 dB and MAE of \~{}12 dB, outperforming nearest-heatmap averaging by \~{}5 dB, with an $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 9 2}$. Results demonstrate effective capture of multipath, diffraction, and shadowing, offering a computationally efficient alternative to full RT for urban channel prediction.