Simulation of machine learning-based 6G systems in virtual worlds

TL;DR

This paper presents a simulation framework for 6G systems utilizing 3D virtual environment representations, introducing new methods for channel modeling and beam selection to enhance performance evaluation.

Contribution

It introduces novel strategies for paired MIMO channel generation and multimodal data integration in 6G system simulations based on virtual worlds.

Findings

Ray tracing trade-offs between speed and accuracy analyzed

Beam selection simulation results demonstrate effectiveness of proposed methods

Enhanced channel modeling techniques improve simulation fidelity

Abstract

Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.