In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing—Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.

This work proposes a Mobile Station (MS) localization method for indoor environments using a single Base Station (BS) equipped with a massive Multiple‐Input Multiple‐Output (mMIMO) antenna array. The proposal can be divided into three main stages: (1) channel modeling, (2) estimation of localization parameters, and (3) estimation of MS position. We consider a millimeter‐Wave (mmWave) mMIMO channel model to estimate five localization parameters: Time of Arrival (ToA), two‐dimensional Angle of Departure (2D‐AoD)‐azimuth and elevation, and two‐dimensional Angle of Arrival (2D‐AoA)‐azimuth and elevation. Then, from AoA and AoD data, the proposed method can analyze the various transmission paths and identify whether there is a Line‐of‐Sight (LoS) path, allowing the automatic determination and application of the most suitable localization algorithm. The system model is comprehensive, approaching a 5G small cell with mmWave and mMIMO technologies transmitting in an indoor environment with LoS and Non‐Line‐of‐Sight (NLoS) conditions and multiple Scatterers. Simulations are carried out, and the results are compared to those of three related methods present in the literature. The obtained results demonstrate that the proposed method achieves sub‐metric accuracy under LoS conditions and outperforms related methods in NLoS conditions. Additionally, the proposed method is simpler, faster, and relies on a single BS for localization.