Precise positioning will play a key role in future 5G/6G services. The upcoming location-based services drive the necessity of high-precision positioning to indoors. In fingerprinting, which is the most commonly used indoor location algorithm, comprehensive radio maps are essential for a precise localization service and highly influence on the result of the final position of the user. A robust algorithm that supports missing information from the map may improve the robustness and reliability of the localization service. In this work, we compare the performance of fingerprinting and different decision tree (DTR) and Adaboost (DTA and LTA) based regressors in a real 5G scenario with missing information. Additionally, we demonstrate the robustness of the LTA method, which had the highest performance among the tested approaches.Este trabajo se ha realizado en el marco del proyecto Maori (acuerdo de subvención número TSI-063000-2021-53) financiado por la Unión Europea- NextGenerationEU. Además, también ha sido parcialmente financiado por la Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech
