Radio Propagation Modeling in an Urban Scenario using Generative Ray Path Sampling
Radio Propagation Modeling in an Urban Scenario using Generative Ray Path Sampling
Radio propagation modeling remains essential for telecommunications research, where Ray Tracing provides a robust tool for simulating electromagnetic interactions in complex environments. In our previous work, we proposed a Machine Learning-assisted Ray Tracing pipeline that uses a generative approach to efficiently sample valid ray paths, significantly reducing computational complexity. In this paper, we extend this work by validating the proposed model through a comparative analysis of coverage maps. Specifically, we evaluate the accuracy of Machine Learning-generated ray paths by comparing the derived coverage maps with those produced by exhaustive Ray Tracing simulations. The results show that our approach could achieve significant computational savings while maintaining high fidelity to the ground truth, demonstrating its potential for scalable applications in wireless network design.