Interpolation Techniques for Fast Channel Estimation in Ray Tracing
Ruibin Chen, Jayadev Joy, Yaqi Hu, Mingsheng Yin, Marco Mezzavilla, Sundeep Rangan
TL;DR
The paper tackles the computational burden of high-resolution ray tracing for wireless channel estimation in complex environments by introducing an interpolation framework that uses mirror-image (LOS) representations of reflected paths via a Reflection Model (RM). It generates ray-traced data at a coarse reference grid, clusters paths by image coordinates, selects relevant clusters with an RBF kernel-based score, and applies kernel regression to estimate channel parameters at new receiver locations. The method demonstrates improved accuracy over naive interpolation and standard plane-wave approximations in LOS and partial-LOS scenarios, with reduced runtime, though NLOS conditions require finer reference grids or advanced modeling. Overall, this approach enables fast, scalable, near-field channel prediction for LOS MIMO and wide-aperture systems, with practical implications for site planning, digital twins, and real-time simulations.
Abstract
Ray tracing is increasingly utilized in wireless system simulations to estimate channel paths. In large-scale simulations with complex environments, ray tracing at high resolution can be computationally demanding. To reduce the computation, this paper presents a novel method for conducting ray tracing at a coarse set of reference points and interpolating the channels at other locations. The key insight is to interpolate the images of reflected points. In addition to the computational savings, the method directly captures the spherical nature of each wavefront enabling fast and accurate computation of channels using line-of-sight MIMO and other wide aperture techniques. Through empirical validation and comparison with exhaustive ray tracing, we demonstrate the efficacy and practicality of our approach in achieving high-fidelity channel predictions with reduced computational resources.