Channel Modeling

Channel modeling aims to create accurate mathematical representations of wireless communication channels, crucial for designing and optimizing communication systems. Current research heavily utilizes machine learning, employing architectures like Generative Adversarial Networks (GANs), diffusion models, and physics-informed neural networks (PINNs) to learn channel characteristics from data, often incorporating ray tracing or other physical models for improved accuracy and generalizability. This work is significant because accurate channel models are essential for improving the performance and efficiency of wireless networks, particularly in emerging areas like 6G and non-terrestrial networks, enabling better resource allocation and network planning.