Received May 3, 2022, accepted May 22, 2022, date of publication May 25, 2022, date of current version June 1, 2022. Digital Object Identifier 10.1109/ACCESS.2022.3177837 Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks SIBREN DE BAST 1 , EVGENII VINOGRADOV 1 , (Member, IEEE), AND SOFIE POLLIN 1,2 1 Wavecore, ESAT, KU Leuven, 3001 Leuven, Belgium 2 Imec, 3001 Leuven, Belgium Corresponding author: Sibren De Bast (sibren.debast@kuleuven.be) This work was supported in part by the Research Foundation Flanders (FWO) Strategic Basic (SB) Ph.D. Fellowship, under Grant 1SA1619N; and in part by the European Union’s Horizon 2020 Research and Innovation Program, MARSAL Project, under Agreement 101017171. ABSTRACT 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. INDEX TERMS Cell-free, massive MIMO, localization. I. INTRODUCTION With the introduction of 5G, massive Multiple Input Multi- ple Output (MaMIMO) enabled communication systems will be deployed all around the world. Such MaMIMO systems are characterised by employing a large number of anten- nas at the base station (BS) to beamform the signal power towards the intended user [1]. In this way, multiple users can be served using the same time and frequency resource as they are multiplexed in the spatial domain. In order to effectively beamform towards the users, the channel state information (CSI) for each user has to be measured. This CSI The associate editor coordinating the review of this manuscript and approving it for publication was Yiming Huo . contains all information about the wireless channel between the user and the BS and can be used to localise users. The challenge is now to estimate the position in the most effective way. In [3], the authors envision that future distributed massive MIMO networks will enable six-dimensional localisation. in this vision, not only the 3D location will be estimated and provided by the cellular network, but also the orientation of the user. This opens interesting opportunities for unmanned aerial vehicles (UAVs), ground robots in industrial settings, autonomous cars, but as well other applications in the world of augmented and virtual reality. Ubiquitous six-dimensional localisation services will be a key technology linking the metaverse with the physical world. VOLUME 10, 2022 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 56427