Potentials of Deterministic Radio Propagation Simulation for AI-Enabled Localization and Sensing

TL;DR

This paper proposes an integrated deterministic radio propagation simulation toolchain to generate data for AI-based localization and sensing, demonstrated in aircraft cabin scenarios to address data scarcity issues.

Contribution

It introduces a novel toolchain combining deterministic modeling and radio simulation to facilitate data generation for AI localization methods.

Findings

Effective scenario classification within aircraft cabins

Accurate localization-related channel parameter extraction

Potential to overcome data bottlenecks in AI localization

Abstract

Machine leaning (ML) and artificial intelligence (AI) enable new methods for localization and sensing in next-generation networks to fulfill a wide range of use cases. These approaches rely on learning approaches that require large amounts of training and validation data. This paper addresses the data generation bottleneck to develop and validate such methods by proposing an integrated toolchain based on deterministic channel modeling and radio propagation simulation. The toolchain is demonstrated exemplary for scenario classification to obtain localization-related channel parameters within an aircraft cabin environment.