# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 6 Performance Evaluation
### Comparative Simulations between RSSI-Based and Distance-Based Selection Strategy  
In order to verify the improvement effect of our algorithm, we compared it with optimization on the basis of only distance rather than RSSI. In order to minimize the overall connection distance between users and BSs, the optimization object of the user-BS selection strategy optimization based on distance was set to be as follows:  
\[\min_{s<\mathcal{S}}\sum_{i=1}^{N}s_{i}(x)\cdot d_{i}(x), \tag{38}\]  
and the constraint conditions were the same as those of the user-BS selection strategy optimization based on reliability. Comparative simulations were carried out under the conditions of known and unknown user distribution. In order to verify the versatility of our algorithm, we also verified RSSI on the basis of different path-loss models. Path loss is presented in decibels as a function of distance, and was calculated by summing the taps in SEN domain and averaging over the measurement snapshots along the measurement run. Path loss and shadow fading are given for the urban microcell scenario (UMi), suburban macrocell scenario (SMa), and urban macrocell scenario (UMa) [33] for the frequency range of 0.45-6.0 GHz in Table 4.  
In the case of the given user distribution, we considered a wireless network with 5 BSs and 10 users. The attributes of the 5 BSs were set as shown in Table 5. \begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario** & **Path Loss** \\ \hline UMi & \(PL(d)=27+22.7\log_{10}(d)+20\log_{10}(f_{c})\) \\ SMa & \(PL(d)=27.2+23.8\log_{10}(d)+20\log_{10}(f_{c})\) \\ UMa & \(PL(d)=25+26\log_{10}(d)+20\log_{10}(f_{c})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Path loss under different scenarios. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Scenarios** & **Two-Dimensional** & **Time-Space** & **Improvements in Density Estimation** \\  & **Kernel Estimation** & **Density Estimation** & **Density Estimation** \\  & **Estimation** & & **Accuracy** \\ \hline Scenario A & 2.75 & 2.77 & 0.67\% \\ Scenario B & 2.39 & 2.54 & 5.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results for density estimation under different sampling scenarios. We assumed that all users followed normal distribution and the maximal signal connection distance was 8, and the user-BS selection strategies at position [5; 5] are shown in Table 6.  
The communication signal quality for the selection strategies based on distance and on RSSI was 0.540 and 0.565, respectively, and our RSSI-based optimization result was 4.6% higher in communication signal quality than the non-RSSI optimization result. This demonstrates that the RSSI-based method could better ensure communication reliability when user distribution is known. In the case of unknown user distribution, we considered a wireless network with 3 BSs and 10 users. The attributes of the 3 BSs were set as shown in Table 7.  
We selected ten user trajectories [000, 001, 002, 003, 004, 005, 006, 007, 008, 009]. Simulation results at position [41; 116.33] are shown in Table 8 and Figure 5. \begin{table}
\begin{tabular}{l l l} \hline
**BS Serial Number** & **Position** & \(R_{BS}\) & \(N_{BS}\) \\ \hline
1 & 0.362 & 0.362 \\
2 & 0 & 0.155 \\
3 & 0 & 0.483 \\
4 & 0.603 & 0 \\
5 & 0.035 & 0 \\ \hline Reliability & 0.565 & 0.540 \\ \hline \end{tabular}
\end{table} 
Table 6: Simulation results for reliability under given user distribution. \begin{table}
\begin{tabular}{l l l l} \hline
**BS Serial Number** & **Position** & \(R_{BS}\) & \(N_{BS}\) \\ \hline
1 & [39; 116.5] & 0.9 & 3 \\
2 & [40; 116.3] & 0.95 & 5 \\
3 & [42; 116.4] & 0.85 & 4 \\
4 & [39; 116.3] & 0.7 & 5 \\
5 & [40; 116.5] & 0.9 & 4 \\ \hline \end{tabular}
\end{table} 
Table 7: Simulation setup for BSs with unknown user distribution. \begin{table}
\begin{tabular}{l l l l} \hline
**BS Serial Number** & **Position** & \(R_{BS}\) & \(N_{BS}\) \\ \hline
1 & [0, 0] & 0.8 & 3 \\
2 & [5; 1] & 0.8 & 5 \\
3 & [1; 7] & 0.7 & 4 \\
4 & [2; 3] & 0.8 & 5 \\
5 & [6; 2] & 0.9 & 4 \\ \hline \end{tabular}
\end{table}