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In application verticals that rely on mission-critical control, such as cooperative intelligent transport systems (C-ITS), 5G-Advanced networks must be able to provide dynamic positioning with accuracy down to the centimeter level. To achieve this level of precision, technology enablers, such as massive multiple-input multiple-output (mMIMO), millimeter waves (mmWave), machine learning and cooperation are of paramount importance. In this paper, we propose a cooperative deep learning (DL)-based positioning methodology that combines these key technologies into a new promising solution for precise 5G positioning. Sparse channel impulse response (CIR) data are used by the positioning infrastructure to extract position-dependent features. We model the problem as a joint task composed of non-line-of-sight (NLOS) identification and position estimation which permits to suitably handle geometrical location …