## 4 Machine-Learning-Aided Positioning in 5G Networks
### Advances in Positioning Aided by Machine Learning  
The simplicity is the only advantage of the cell-identity-based techniques; therefore, no experiments were investigated from a machine learning perspective. Even though angle-based methods are one of the main winners of the massive MIMO introduced in 5G, the positioning in the LOS scenario does not require an ML approach [47; 48]. On the other hand, even if the angle information cannot be used for accurate positioning (e.g., in a NLOS scenario), it can be utilized to enhance the accuracy of other approaches [49; 50; 51]. In most cases, the AoA/AoD is used as a fingerprint feature, which can seriously decrease the positioning error in an LOS scenario. Most of the machine learning approaches are attempting either to predict the environmental parameters (e.g., LOS/NLOS) or signal parameters [52]. [PERSON] et al. [49] have shown that knowledge of the antenna is critical for accurate position estimation, and that it is possible to position UE within 10 m with 80% accuracy using an \(8\times 8\) antenna array. Their work explores the use of two ML methods--neural network (NN) and random forest (RF)--to estimate the position of UE in an urban area using antenna beam data. The radio measurements used are Beam Reference Signal Received Power (BRSRP) and AoD from a set of beams from an antenna. In order to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Algorithm** & **Input Data Type** & **Simulation?** & **Environment** & **Error** \\ \hline
[33] &
\begin{tabular}{c} Robust Weighted Least Squares + \\ RANSAC and IDD combined \\ \end{tabular} & TDoA & realistic & indoor & \textless{}3 m \\ \hline
[34] & \
\begin{tabular}{c} Dynamic reconstruction fingerprint \\ matching algorithm \\ \end{tabular} &
\begin{tabular}{c} Received signal strength \\ indicator \\ \end{tabular} & simulated & indoor & \textless{}1 m \\ \hline
[35] & Extended Kalman filter & AoA & realistic & outdoor & \textless{}1 m \\ \hline
[36; 37] & Extended Kalman filter & Uplink reference signal & simulated & outdoor & \textless{}1 m \\ \hline
[39] &
\begin{tabular}{c} Expectation maximization, \\ subspace-spaced algorithm \\ \end{tabular} & Uplink reference signal & simulated & indoor & \textless{}1 m \\ \hline
[40] & Unscented Kalman filter & AoA, ToA & simulated & indoor & \textless{}1 m \\ \hline
[41] & Deriving Cramer–Rao bound & AoA, TDoA & realistic &
\begin{tabular}{c} outdoor \\ (vehicle) \\ \end{tabular} & \textless{}1 m \\ \hline
[43] & Taylor series least-square method & GNSS-TOA, 5G-AoA & simulated & outdoor & \textless{}10 m (95\%) \\ \hline
[44] &
\begin{tabular}{c} Deriving Ficher information of 5G \\ and GNSS signals \\ \end{tabular} & Simulated GNSS, & simulated & outdoor & \textless{}1 m \\ \hline
[45] & Particle filter &
\begin{tabular}{c} Real GNSS, simulated 5G \\ signals \\ \end{tabular} & simulated & outdoor & \textless{}3 m (RMSE) \\ \hline
[46] & OFDMA-based VLCP & Light signals, RSS & simulated & indoor & \textless{}1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the conventional positioning techniques in 5G networks. have different position models for LOS and NLOS conditions, the authors evaluated two Generalized Likelihood Ratio Test (GLRT) detectors to detect NLOS conditions. The first used the difference between the BRSRP measurements as input, whereas the second used the difference between the AoD measurements. The solution based on the BRSRP difference achieved a detection probability of 88%, whereas the solution based on the AoD difference achieved only 76%. The false positive probability was 5% in both cases. In the NLOS cases, the random-forest-based positioning outperformed the NN-based one (8.4 m vs. 9.2 m); however, under LOS conditions, the NN performed much better (2.1 m vs. 8.4 m). [PERSON] et al. [53] reported experimental results on using a learning-based approach to infer the location of a mobile user of a cellular network within a cell for a 5G-type Massive MIMO system.