LAUSR

Massive Multiple-Input Multiple-Output (MaMIMO) communication networks are recently being investigated for hltheir high potential for localisation services. This is enabled by the high-dimensional channel state information (CSI) captured by the many antennas in the system. Previously, it has been shown that these systems can achieve a very high localisation accuracy. However, many challenges still remain, we identified two of them. First, the recent trend towards cell-free MaMIMO with many highly distributed Access Points (AP), leads to the question of how this impacts the localisation methods. Current localisation methods process the signals in a central processing unit (CPU), resulting in a high fronthaul requirement when deploying these algorithms in a distributed network, limiting the deployment and scalability. Second, there exists a trade-off between using model-driven and data-driven localisation methods. In this work, we propose two new localisation methods which employ a distributed processing strategy and compare them against two centralised localisation methods. In addition, the four analysed methods explore the trade-off between being model- and data-driven. Moreover, the proposed ML-MUSIC method blurs the lines between the two by combining Machine Learning and traditional signal processing. Next to comparing the localisation accuracy, we evaluate the performance in a dynamic setting, the scalability and fronthaul requirement of the methods. The proposed Machine Learning-enhanced Multiple Signals Classification method, ML-MUSIC, reaches a median error of 34.2 mm on the test set while only using 500 training samples. Due to ML-MUSICs distributed design, the fronthaul throughput requirement is reduced 1200-fold in comparison to the centralised methods. Furthermore, ML-MUSIC has the lowest computational complexity of all analysed methods, making it an ideal method to localise users in upcoming distributed cell-free MaMIMO networks.