## 4 Machine-Learning-Aided Positioning in 5G Networks
### Advances in Positioning Aided by Machine Learning
They investigated the extent to which uplink Channel State Information (CSI) can be used for UE positioning. They chose the normalized covariance of the instantaneous uplink propagation channel vector as the input to the learning algorithms, since according to them, the second-order channel statistics capture most of the location-related characteristics of the CSI. They argued that statistical learning is a viable way to deal with the complexity of the relationship between the spatial scattering environment surrounding a user and the CSI measured by the BS. Two ML approaches were considered: a feedforward Neural Network (NN) and a k-Nearest Neighbor algorithm (kNN). The training and testing data were collected on the Huawei 5G testbed of a university campus. The covered area contains mostly Line-Of-Sight (LOS) locations. The CSI data were measured for a single-antenna UE on the uplink of a massive-MIMO system with a 32 dual-polarized antenna array at the BS. To evaluate the sensitivity of the localization \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **ML Method** & **Measurement Type** & **Simulation/ Realistic** & **Environment** & **Error** \\ \hline
[49] & NN, RF & BRSRP & realistic & outdoor & \textless{}10 m (80\%) \\ \hline
[53] & kNN, ELM & CSI & realistic & outdoor & 8.2m \\ \hline
[54] & \
\begin{tabular}{c} NN, TDNN \\ (time-delay neural \\ network) \\ \end{tabular} & TOA, code phase & realistic & outdoor &
\begin{tabular}{c} 4.9 m (ranging \\ RMSE) \\ \end{tabular} \\ \hline
[55] & NN & AoA & hybrid & both & 0.4 m \\ \hline
[56] &
\begin{tabular}{c} Densely connected \\ Neural Network \\ \end{tabular} & RSS, GNSS signal & simulation & outdoor & 0.74 m \\ \hline
[57] & NN, DT & BRSRP & simulation & outdoor & 1.4 m \\ \hline
[50] & CNN, LSTM, TCN &
\begin{tabular}{c} Beamformed \\ fingerprint \\ \end{tabular} & simulation & outdoor & 1.78 m \\ \hline
[58] & weighted kNN & CSI & simulation & outdoor & 2 m (90\%) \\ \hline
[59] &
\begin{tabular}{c} Deep convolutional \\ Gaussian process \\ \end{tabular} & Beamforming images & simulation & outdoor & 2.79 m \\ \hline
[27] &
\begin{tabular}{c} 13 ML models \\ including NN, kNN, \\ RF \\ \end{tabular} & RSRP & simulation & outdoor & 3.3 m (kNN) \\ \hline
[60,61] & GPR, kNN, SVM & RSRP & simulation & outdoor & 3.5 m \\ \hline
[62] & Gaussian Processes & RSRP & simulation & outdoor & 10 m \\ \hline
[63] & NN, kNN, SVM & RSRP & simulation & indoor & 1.6 m \\ \hline
[64] & DNN & RSS & simulation & indoor & 1.6 m \\ \hline
[65] & Gaussian Processes & RSRP & simulation & indoor & \textless{}2 m \\ \hline
[66] & kNN & RSS & realistic & indoor & \textless{}2 m \\ \hline
[67] & NN & CSI & simulation & both & \textless{}1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the machine-learning-aided positioning techniques in 5G networks. error to the number of antennas, simulations were performed with a varying number of BS antennas (\(N\)) by discarding part of the data. The results show that the localization error is small and exhibits little sensitivity to \(N\) as long as \(N\geq 12\) (mean error \(\leq 14\) m), while for \(N\leq 8\) the localization accuracy decreases significantly (mean error \(\geq 18\)m). Moreover, the kNN approach (\(k=3\)) outperformed the NN-based approach in terms of average accuracy (8.16 m vs. 9.6 m). [PERSON] et al. [54] evaluated the ability of different neural networks to mitigate multipath effects in 5G downlink signals, in particular to learn multipath-induced errors in code phase estimation of a 5G receiver. The authors studied two types of neural networks, namely Feed-Forward Neural Networks (FFNNs) and Time-Delay Neural Networks (TDNNs). The NNs use inputs from the AutoCorrelation Function (ACF) to learn the errors in the code phase estimate of a conventional Delay-Locked Loop (DLL). A ray-tracing algorithm is used to model the effects of the Non-Line-Of-Sight (NLOS) components on the Line-Of-Sight (LOS) component and to generate training data.