# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 5 Simulation Results  
In this section, simulation is performed to verify the performance of the proposed scheme. The parameters used in the simulation are described in Table 1.  
The simulation uses the indoor environment proposed by 3 GPP [14]. The size of the environment is defined as a space of 120 m \(\times\) 50 m. The number of APs deployed in the indoor environment is 12, the same as the environment proposed by 3 GPP. The power \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of iterations & 10,000  Room size & 120 m \(\times\) 50 m  Pathloss \(32.4+17.3\log_{10}d+20\log_{10}f\) \\ Shadow fading std (\(\sigma_{SF}\)) & 3 dB \\ Number of APs & 12 \\ Number of particles & 10 \\ Distance between SPs & 3, 6, 9 m  Transmit power of APs & 20 dBm \\ _c, r, w\({}_{min}\), w\({}_{max}\), T_ & 2, 0.3, 1, 0.4, 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. of each AP is set to 20 dBm. For N, the shadow fading standard deviation is used, and the value is \(\sigma_{SF}=3\) dB. If the N value is large, the error in the initial limited region will occur, which will result in a larger positioning error. The total number of iterations of the simulation is set to 10,000. At this time, the location is estimated using one UE. In addition, as the distance between SPs changes, the number of SPs also changes, and accordingly, the positioning accuracy is evaluated. The location of the Wi-Fi AP is shown in Figure 5.  
As shown in Figure 5, 12 APs are deployed in the indoor environment. In addition, to analyze the performance of the proposed method, simulations are performed while changing the distance between SPs to 3, 6, and 9 m. Table 2 shows the change of the number of SPs according to the change of the distance between SPs in the proposed environment. As can be seen in Table 2, the number of SPs significantly decreases as the distance between SPs increases. Table 3 is the result of comparing the positioning accuracy of the ToA, TDoA, AoA, RSSI, and RSSI + FP (Fingerprinting) schemes. As shown in Table 3, the positioning error is the largest when triangulating based on RSSI. However, when RSSI is used together with the FP technique in an indoor environment, the highest positioning accuracy can be achieved. Based on these results, in this paper, the RSSI and FP schemes are applied together. First, each AP builds a fingerprinting database by measuring RSSI values for all SPs in the offline phase. In the online positioning step, each AP measures the RSSI value for the actual user location. After that, the RSSI value of the actual user performs WFM with \begin{table}
\begin{tabular}{c c c c} \hline Distance between SPs [m] & 3 & 6 & 9 \\ \hline Number of SPs & 697 & 189 & 84 \\ \hline \end{tabular}
\end{table} 
Table 2: Number of SPs vs. distance between SPs. Figure 5: Location of Wi-Fi AP in indoor office environment. the built fingerprinting database. As a result of fuzzy matching, the four closest SPs can be derived from the actual UE location. The SPs derived through the simulation are shown in Figure 6.  
In Figure 6, the green circles, red circles, and blue triangles represent the SPs, the actual UE locations, and the Wi-Fi APs, respectively. The yellow circles represent the SPs closest to the actual UE locations. The distance between SPs is 3 m, and the total number of SPs is 697. Figure 7 shows the results of improving the performance through the PSO algorithm after performing the WFM algorithm. The simulation limits the region to the four SPs closest to the UE obtained through the WFM algorithm. This can further improve the average positioning accuracy and convergence speed by limiting the initial region of the PSO algorithm. Location accuracy can be obtained by calculating the difference between the actual UE location and the estimated location. As shown in Figure 7, it can be confirmed that the four SPs nearest to the UE are selected through the WFM algorithm.