## 3 Conventional Positioning Solutions in 5G Networks
### Pure 5G Network-Based Positioning  
In this section, we briefly present positioning solutions that require input data based only on 5G signals. As the following solutions show, the data types mentioned in Section 2 can be utilized in various ways for indoor or outdoor positioning. The reference architectures, signals, and protocol-related peculiarities are well summarized by [PERSON] et al. [32] based on Release 16, as this set of recommendations provides the foundations for 5G positioning. In addition to illustrating the capabilities of the standardized components, they also provide simulation results based on key scenarios with certain assumptions. [PERSON] et al. [33] investigated the actual realization of a TDoA-based indoor positioning system on existing 5G small cell networks. Their primary focus was on indoor signal propagation and to overcome the challenging effects related to such scenarios. Their approach was to create a channel model based on real 5G measurements and to use the novel model obtained by using that data for the creation of a realistic simulation framework. This framework was then utilized to analyze the performance of various algorithms. They found that using some of these algorithms can significantly reduce the impact of NLOS propagation. Consequently, a positioning error under 3 m is achievable in certain cases. Furthermore, accuracy could be improved not only by increasing the number of ARPs (Antenna Reference Points, radio dots), but further enhancing the RWLS (Robust Weighted Least Squares) method, the power-delay-profile-based (PDP) NLOS algorithms, or by combining TDoA measurements with the results from built-in inertial measurement unit sensors or with the angular measurement data coming from MIMO antenna systems. [PERSON] et al. [34] proposed a solution for 3D positioning in a simulated indoor 5G ultra-dense network. In their work, they proposed a 3D dynamic reconstruction fingerprint matching algorithm whose first step is to reconstruct the complete fingerprint matrix from partial data. Then, to simplify the fingerprint data, the suboptimal service base stations are eliminated from the dataset. Finally, the k-nearest neighbor matching algorithm is used to estimate the 3D coordinates. The positioning errors are evaluated at several Signal-to-Noise Ratio (SNR) levels. The mean error at \(\text{SNR}=2\) dB is 0.31 m and 0.16 m at \(\text{SNR}=20\) dB. [PERSON] et al. [35] performed their research using a realistic outdoor 5G network testbed. They utilized a two-stage Extended Kalman Filter (EKF) based positioning engine. In the first stage, AoA estimation is performed by local EKF computing engines at each Transmission-Reception Point (TRP). Then, all AoA estimates are fused by a global EKF to obtain the position estimate. According to their results, a sub-meter 2D error is achievable with a probability of 95%. Similarly, [PERSON] et al. [36; 37] also used a cascaded EKF in a two-stage manner for an unsynchronized 5G ultra-dense network. In their solution, the computations are performed on the network side to minimize the power consumption and computational requirements at the UEs. In the first stage, the ToA and the Direction of Arrival (DoA) of the user nodes are estimated. Then, in the second EKF stage, the individual ToA/DoA estimates are fused to estimate the user node position. Their results show that sub-meter accuracy for positioning and tracking is feasible. The simulations in their work are performed based on the METIS Madrid map model [38]. In their extended research, sub-meter error was also achievable; however, in this case, a realistic clock model with clock skews was used. [PERSON] et al. [39] focused their experiments on positioning a single cell (base station) with a wideband 5G signal and a vector antenna (VA). Consequently, this solution is free from the complications of multi-cell approaches, such as synchronization between base stations and increased deployment costs due to the complexity of the system.