Generating MIMO Channels For 6G Virtual Worlds Using Ray-tracing Simulations

TL;DR

This paper introduces enhanced ray-tracing simulation datasets for 6G MIMO channels, enabling better environment modeling for augmented reality and hologram applications, with analysis of tradeoffs and performance in beam selection and channel estimation.

Contribution

It presents improved Raymobtime datasets with paired MIMO channels and multimodal data, and analyzes tradeoffs between simulation speed and accuracy for 6G channel generation.

Findings

Enhanced datasets improve environment modeling accuracy.

Tradeoffs between simulation speed and accuracy are characterized.

Simulation improvements impact beam selection and channel estimation performance.

Abstract

Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data. We also discuss tradeoffs between speed and accuracy when generating channels via ray-tracing. We finally provide results of beam selection and channel estimation to assess the impact of the improvements in the ray-tracing simulation methodology.