# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 8 Preliminary Analysis B: Factorial Design using the Strict Frequency Reuse Algorithm
### 2\({}^{k}\) Factorial Design
This is due to how bandwidth is allocated in the Strict FR. The cell center of each cell does not share bandwidth with the cell-edge of any cell. Hence, users from different cells allocated to the same bandwidth might not be close enough to cause significant ICI. system performance is affected by the Strict FR's parameters, in order to identify the best configurations. Preliminary tests indicated that 100 repetitions are enough for the proposed scenario to provide a good confidence interval. Moreover, given that CenterPowerOffset represents a power level and does not impact on throughput, it is fixed on its minimum possible value, lowering energy consumption. Each evaluated parameter is detailed in Table 6.  
Figures 12 and 13 present the results for the average and 10 th percentile throughput, respectively. Both have a 95% confidence interval. Lower values of RsrqThreshold result in better performance if more bandwidth is allocated to cell-center users. Consequently, bandwidth distribution 1, which allocates 6 RBs for cell-center and 18 RBs for cell-edge users (6 RBs for each cell edge), has the worst performance. This is due to more users being allocated to the cell center as RsrqThreshold gets lower, causing the common sub-band to overload. Moreover, increasing the RsrqThreshold yields higher throughput for all distributions (Figure 12) until each curve reaches its peak. At this point, the distribution with more band allocated to the center still has the best performance. From that value, the private sub-band gets overloaded, which results in performance loss. This loss is more severe for distributions with less bandwidth allocated to the edge. Figure 13 has similar behavior to Figure 12, since increasing the RsrqThreshold results in higher throughput. However, each curve starts decreasing at different values. Distributions with less bandwidth to the edge sub-band have their peak at lower RsrqThreshold values. At RsrqThreshold = 33, Figure 12 indicates that throughput is better for distributions with more bandwidth allocated to the center. Figure 13 indicates the opposite, and bandwidth distributions 1 and 2 have similar performance on RsrqThreshold = 32. \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Values** \\ \hline RsrqThreshold & 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 \\ \hline Common/Private & Bandwidth Distribution 1: 6 / 18 \\ Sub-band (RBs) & Bandwidth Distribution 2: 12 / 12 \\  & Bandwidth Distribution 3: 18 / 6 \\ \hline \end{tabular}
\end{table} 
Table 6: Full factorial design: parameter configurations. Figure 12: Full factorial design using the strict frequency reuse: results for the average throughput. Each curve is a different bandwidth distribution. Lastly, Figure 14 presents the SINR CDF for \(\text{RsrqThreshold}=3\). At this value, more bandwidth allocated for cell-edge users result in better SINR. These results show a clear compromise between the average user performance and the performance of cell-edge users. Besides, in a scenario that hotspots appear unpredictably, certain regions can also become overloaded. Since the \(\text{RsrqThreshold}\) has direct impact in user allocation, its dynamic variation can efficiently mitigate the performance loss caused by the hotspots.