Taming the Radio Maze: Machine Learning for Robust and Precise Wireless Localization

The imperative need for precise positioning across urban navigation and industrial automation domains has prompted exploration into various locating methods, prominently leveraging wireless positioning systems. However, pervasive multi-path signal propagation, especially in indoor and industrial settings, complicates the estimation of the signal propagation time, the accurate estimation of which is crucial for muti-lateration. This talk focuses on the application of ML methods for wireless positioning, illustrating how channel information from measurements allows to extract valuable insights from the radio environment. This includes aspect of sim2real and transfer learning, manifold learning for self-supervised fingerprinting, and task-agnostic radio foundation models. These insights facilitate robust and precise localization of mobile objects in challenging environments. Furthermore, the discussion extends to the future landscape of 6G applications where AI could be used for a variety of new and enhanced features, e.g. ISAC sensing, enhanced localization, and further improved beam management.