An Enhanced Dynamic Ray Tracing Architecture for Channel Prediction Based on Multipath Bidirectional Geometry and Field Extrapolation

TL;DR

This paper introduces an enhanced dynamic ray tracing (E-DRT) method that leverages bidirectional multipath extrapolation and field evolution regularity to significantly improve channel prediction accuracy and efficiency in 6G network scenarios.

Contribution

The paper proposes an innovative E-DRT architecture that utilizes all available environmental data and bidirectional extrapolation to improve channel prediction accuracy and computational efficiency.

Findings

Channel prediction accuracy improved from 68.3% to 94.8%.

Runtime reduced by 7.2% compared to traditional DRT.

Effective in Vehicle-to-Vehicle (V2V) scenarios.

Abstract

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs' electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT.