## 4 Machine-Learning-Aided Positioning in 5G Networks
### Advances in Positioning Aided by Machine Learning
[63] proposed the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radio map. The RSRP measurements were collected in an indoor environment with the following dimensions: 8 m width \(\times\)16 m depth \(\times\)2.75 m height. The positioning method begins with estimating the 2D position. Then, the output is used recursively to predict the 3D location of a mobile station. The DNN-based positioning has outperformed the kNN and SVM-based methods. The 2D average error of the DNN was only 1.6 m while the kNN's was over 2.5 m and the SVM's was over 5 m. The identification of the height translated into a classification problem where three classes were considered. The SVM had a misclassification rate of 66% while it was 22% in case of the kNN and only 11% in case of the DNN. [PERSON] et al. introduced BeamMaP in [60], which was later improved in [61]. BeamMaP uses Gaussian Process Regression (GPR) as a machine learning regression technique on the beamforming transmission patterns. The improved BeamMaP uses adaptive beamforming as a candidate for building the testing process since it can cover a larger area of MUs and provides more comprehensive interference rejection compared to switched beamforming. They argue that the improved BeamMaP not only provides more efficient coverage, but also reduces base station power consumption. The simulations took place in an outdoor environment with LOS and NLOS areas in different weather conditions, and the GPR model was compared with the kNN and SVM models. The improved adaptive BeamMaP shows better performance than the original BeamMaP in different weather conditions, although it achieves comparable performance to other machine learning methods such as kNN and SVM in dynamic environments. The obtained RMSE performances of BeamMaP are proved to be close to the Bayesian Cramer-Rao bounds. The proposed method outperformed the other models and achieved an MAE of 3.5 m at 1 dB shadowing noise in the simulated dynamic environment. BeamMaP outperformed the kNN and SVM models even at higher noise levels, but its accuracy decreased significantly as the noise level increased. [PERSON] et al. [64] proposed DNN models (one for horizontal and one for vertical positioning) for RSS fingerprint-based 3D positioning in a 5G IoT setup testbed. They compared the results of the DNN model with the performance of SVM and KNN models. In 2D (horizontal) positioning, the DNN model showed the best accuracy with a 1.6 m mean error. For the vertical positioning, the authors trained the models to classify the samples into three classes of height (0.25 m, 0.75 m, and 1.75 m). In this case also, the DNN achieved the best results by perfectly classifying the samples in the 1.75 m class, and more than 95% of the samples were accurately classified in the other two class. [PERSON] et al. [62; 65] proposed a supervised ML approach based on Gaussian Process (GP) regression to position users in a distributed massive MIMO system using uplink RSRP. They focused on the scenario where noise-free RSS is available for training but only noisy RSS is available for estimating the user's location. The authors consider the Conventional GP (CGP) and Numerical approximation GP (NaGP) in [65], while in [62], they use the Gaussian approximation GP (GaGP) and the reconstruction-cum-Gaussian approximation GP (RecGaGP) method. It is shown that the achieved RMSE of all GP is close to the Bayesian Cramer-Rao lower bound. In [65], the methods are compared to three baseline methods, which are as follows: the linear least squares, improved linear least-squares trilateration schemes, and the kNN. Both GP methods outperformed the baseline methods and achieved almost the same error in LOS and NLOS indoor scenarios. In [62], the GaGP and RecGaGP methods are compared to the NaGP and CGP methods in outdoor scenarios. The GaGP method had similar results as the CGP and NaGP methods but the RecGaGP outperformed all of them by a large margin. [PERSON] et al.