Wireless localization via learned channel features in massive MIMO systems

Abstract

In this dissertation, advanced ML algorithms for enhanced wireless localization are proposed. Motivated by the capabilities of deep neural networks (DNNs) and the advancements in massive multiple-input-multiple-output (MIMO) systems, the dissertation aims to address some of the fundamental limitations of ML-based localization approaches related to dependability, generalization, and data scarcity.The dissertation has three main parts, each dedicated to addressing specific challenges and introducing new algorithms.