A Physics-Informed Deep Ray Tracing Network for Regional Channel Impulse Response Estimation

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.