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

Research output: Preprint/documentation/reportPreprint



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.


Original languageEnglish
Publication statusPublished - 11 Sept 2023
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External IDs

ORCID /0000-0002-3434-3488/work/142660203



  • eess.SP