# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 6 Performance Evaluation  
We evaluated the performance of our proposed approach on the basis of the GeoLife GPS Trajectories dataset. The GeoLife GPS Trajectories dataset was assembled from the Microsoft Research Asia Geolife project by 182 users during a period of over three years (from April 2007 to August 2012). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which containing the information of latitude, longitude, and altitude. Of the trajectories, 91% are logged in a dense representation, e.g., every 1 to 5 s or every 5 to 10 m per point. This dataset recorded a broad range of users' outdoor movements, including not only life routines such as going home and to work, but also some entertainment and sports activities, such as shopping, sightseeing, dining,  
Figure 1: Overall flow of Algorithm 3.  
hiking, and cycling. Most of the data were created in Beijing, China. Figure 2 plots the trajectories of user [004] from 23 October 2008 to 5 July 2009. Figure 2a shows that the user's track coverage was relatively large, but in fact, according to the analysis of the track data and Figure 2b, the user spent most of the time within a small area in the center and had rarely traveled far away. The reason for this difference is that Figure 2a is a two-dimensional space map that only considers the spatial scale, and ignores information in the time scale. Our algorithm considers both space and time information to estimate the density. We conducted comparative simulations from two aspects. Simulation 1 compares the difference of the density estimation effect for 2-dimensional kernel density estimation and time-space density algorithms under different sampling frequency conditions, while Simulation 2 compares the reliabilities of RSSI- and distance-based selection strategies. Both simulations were conducted in a Python environment. Comparative Simulations between Two-Dimensional Kernel Density Estimation and Time-Space Density Estimation  
In order to verify the superiority of our time-space density estimation method, we conducted two comparative simulations. First, we compared our time-space density estimation method with the two-dimensional kernel density estimation method. Comparative simulations were carried out on a trajectory dataset with the same sampling time interval, together with passing the same point, and on a trajectory dataset with different sampling time intervals, together with passing the same point. The variable between the above comparative simulations was the sampling frequency condition. We set up two simulation scenarios, A and B, which corresponded to the same sampling frequency and different sampling frequencies. Under both scenarios, we assumed that RSSI followed path-loss model \(PL(d)=27+22.7\log_{10}(d)+20\log_{10}(f_{c})\) in the WINNERII C2 model [33]. The setups of this comparative simulation are shown in Table 2.  
Figure 2: Trajectories of user [004] from 23 October 2008 to 5 July 2009. (**a**) Two-dimensional trajectory; (**b**) three-dimensional trajectory. The calculated 2-dimensional kernel density and time-space density at (40.01, 116.31) were 2.75 and 2.77 respectively, which are very similar. In Scenario B with different sampling time intervals, we selected the trajectory data of five users [132; 163; 167; 168; 135]. Their data collection was rather chaotic, and the sampling time interval was not fixed. Users 135, 163, and 167 all passed through point (39.96, 116.40). The specific trajectory is shown in Figure 4. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Path Loss** & \multicolumn{2}{c}{\(PL(d)=27+22.7\log_{10}(d)+20\log_{10}(f_{c})\)} \\ \hline
**Scenarios** & **Point** & **Users** & **Sampling Interval** \\ \hline Scenario A & (40.01, 116.31) & [000, 001, 002, 003, 004] & Same \\ Scenario B & (39.96, 116.40) & [132; 135; 163; 167; 168] & Different \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation setups for simulation under different sampling scenarios.