On Dynamic Ray Tracing and Anticipative Channel Prediction for Dynamic Environments

Ray tracing (RT) algorithms, which can simulate multipath radio propagation in the presence of geometric obstacles such as buildings, objects, or vehicles, are becoming quite popular, due to the increasing availability of digital environment databases and high-performance computation platforms, such as multicore computers and cloud computing services. When objects or vehicles are moving, which is the case of industrial or vehicular environments, multiple successive representations of the environment (snapshots) and multiple RT runs are often necessary, which require a great human effort and a great deal of computation resources. Recently, the dynamic RT (DRT) approach has been proposed to predict the multipath evolution within a given time lapse on the base of the current multipath geometry, assuming constant speeds and/or accelerations for moving objects, using analytical extrapolation formulas. This is done without rerunning a full RT for every snapshot of the environment, therefore with a great reduction in both human labor and computation time. When DRT is embedded in a mobile radio system and used in real time, ahead-of-time (or anticipative) field prediction is possible, which opens the way to interesting applications. In the present work, a full-3-D DRT algorithm is presented, which allows to account for multiple reflections, edge diffraction, and diffuse scattering for the general case where moving objects can translate and rotate. For the purpose of validation, the model is first applied to some ideal cases and then to realistic cases where results are compared with conventional RT simulation and measurements available in the literature.

Automated summary

Ray tracing algorithms are popular for simulating multipath radio propagation in environments with obstacles. The dynamic ray tracing approach can predict multipath evolution within a given time lapse without rerunning the simulation for every snapshot, reducing both human labor and computation time. It has the potential for real-time field prediction in mobile radio systems.