# 5G Positioning: An Analysis of Early Datasets
## 1 Introduction
In this frequency range, radio signals experience penetration and diffraction losses, resulting in a dominant line-of-sight (LOS) component and minimal multipath effects [17]. However, the use of millimeter-wave wireless signals also poses challenges, such as high path loss, which can be mitigated through the adoption of new specialized compensation techniques, such as beamforming and highly directional antennas. Wideband carriers: NR provides a significant bandwidth improvement over LTE; while LTE provides a maximum of 20 MHz, NR provides up to 100 MHz in Frequency Range 1 (FR1: 450 MHz to 7 GHz) and 400 MHz in Frequency Range 2 [18]. The variance in the delay estimation is inversely proportional to the bandwidth of the signals; this implies that as the signal bandwidth widens, the uncertainty in the delay estimation decreases as the main lobe of the correlation function becomes narrower. Narrower lobes are more easily discriminated by the receiver and, therefore, the distinction between direct and reflected paths improves [19]. Massive multiple-input multiple-output (MIMO) and beamforming: massive MIMO wireless communication refers to the idea of equipping cellular base stations with very large quantities of antennas. Such a massive number of antennas causes interference problems, which can be mitigated by deploying the beamforming technique: a process designed to produce the radiated beam patterns of the antennas by building up the processed signals in the direction of the desired terminals and canceling beams of interfering signals [20]. The benefits of using beamforming in massive MIMO systems include enhanced energy efficiency, improved spectral efficiency, increased system security, and applicability for mmWave bands. The main purpose of this paper is to implement the positioning using 5G time-of-arrival (\(ToA\)) observations and to assess the accuracy and the reliability of the estimated coordinates from experimental data. These real-world 5G ranging measurements have been provided by the ESA and were produced during the GINT05G project, described in the next section. Different strategies to process these data will be compared. In general,when time series of observations are processed to estimate trajectories, a proper filtering process like Kalman filtering [21; 22], allows the smoothing of the noise and blunders in individual epochs. On the contrary, we process the epochs independently, using the Least Squares method [23], because we are interested in the accuracy and reliability of the single-epoch solutions, not in the filtering results. In the rapidly evolving field of 5G positioning, the present research introduces an innovative approach in the use of the sounding reference signal. Distinct from the 3 GPP specifications for 5G positioning [24], our methodology makes use of the SRS in the downlink direction and proposes signal design changes that would allow it (see Section 2.4 for details). Furthermore, most of the previous research studies on 5G positioning, and in particular positioning using the SRS signal [12; 25], are based on simulations rather than real-world experiments carried out in a controlled measurement setup. This setup generates at least two major benefits: repeatability of measurement conditions, and more tangible results than purely theoretical studies. The paper is structured as follows: Section 2 describes the GINT05G experiment and data collection; Section 3 describes in detail the strategies applied to process the data; Section 4 discusses the results; and finally, Section 5 presents the conclusions.