# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 3 Simulation Results  
_Performance Evaluation_  
This section describes the simulation results evaluating the positioning performance of the proposed method under LOS and NLOS conditions. The obtained results illustrate the impact of signal attenuation on positioning performance, particularly in NLOS scenarios. The positioning error, derived from the deviation between the estimated and actual UE positions, highlights the effectiveness of UL-SRS and DL-PRS. Furthermore, we analyzed the positioning error in both LOS and NLOS environments, along with a comprehensive evaluation of the ranging error and overall UE positioning accuracy. The correlation of the SRS and PRS signals is also performed to extract precise timing estimates, which play a critical role in enhancing the accuracy of the proposed positioning technique. As shown in Figure 8, the variations in PRS correlations cause each gNB to select a distinct PRS ID, ensuring that different PRS sequences are transmitted. The PRS correlations fluctuate over time, thereby demonstrating their utility in tracking UE movement. The PRS correlation between gNB1 and gNB2 is notably high and undergoes a gradual decrease over time. A higher PRS correlation indicates a greater similarity between the PRS signals transmitted by the respective gNBs. The precise PRS correlation values depend on the 5G NR network configuration and the spatial arrangement of the gNBs. These correlations are essential for enhancing the precision of UE positioning within the network, providing valuable insights into the dynamic behavior of signal propagation and the interactions among gNBs. Figure 9 illustrates distinct peaks at specific time intervals, corresponding to significant SRS correlation values and indicating moments when the UE had a strong communication link with the respective gNBs. To avoid interference, each gNB allocates a distinct SRS ID, thereby facilitating the transmission of unique SRS sequences. For instance, gNB1 identified by Ncell ID 134, displays two prominent peaks, suggesting a strong correlation at those specific times. Similarly, analysis of the data reveals a peak pattern exhibited by gNB2 (Ncell ID 953) that corresponds to gNB1, possibly indicating the primary serving cell. Conversely, gNB3 (Ncell ID 938) exhibits a different peak pattern, reflecting a distinct interaction with the UE, potentially influenced by variations in distance. This analysis  
Figure 8: PRS correlation detection at the UE. highlights the importance of SRS in enhancing handover strategies, resource allocation, and overall network optimization. As illustrated in Figure 10, the ranging error represents the error in estimating the distance between the UE and the gNB. The ranging errors are observed under varying comb sizes within DL-PRS and UL-SRS signal configurations, particularly in NLOS conditions. According to 3 GPP TS 138.211 V16.2.0, PRS is compatible with comb sizes of 2, 4, and 6. Similarly, according to 3 GPP TS 138 211 V17.1.0, SRS employs comb sizes of 2, 4, and 8, following similar principles of subcarrier allocation. A smaller comb size, such as comb-2, transmits on every second subcarrier. This increases the number of subcarriers per symbol, enhances signal density, and improves TOA accuracy, thereby reducing positioning error. Conversely, a larger comb size, such as comb-8, transmits on every eighth subcarrier, thereby reducing the number of occupied subcarriers. The results emphasize the importance of selecting the appropriate signal and comb size based on environmental conditions to optimize positioning accuracy. The optimal comb size must be selected based on deployment conditions, balancing the need for improved localization precision with maintaining spectral efficiency. Figure 11 illustrates the cumulative distribution function (CDF) of the positioning error to evaluate the performance of the proposed method.