# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results
### Proof-of-Concept Simulation Results  
The results are presented using SINR and throughput. Moreover, users are split in three groups: all users, users in hotspots, and users outside hotspots. Figures 17 and 18 present the SINR percentage gains for all groups of users. Figures 19 and 20 present throughput results in Mbps and Figures 21 and 22 present the throughput percentage gain. Given the wide range that SINR is reported in linear scale, it is more practical to calculate the gain on a logarithmic scale. Hence, it is executed using dB values, as follows:  
\[Gain=\left(\frac{SINR_{QL}}{SINR_{noQL}}-1\right)\times 100\% \tag{6}\]  
where \(SINR_{QL}\) is the system average \(SINR\) in dB when our solution is applied, and \(SINR_{noQL}\) is the system average \(SINR\) without it. Thus, the \(Gain\) measures how our proposal improves the SINR in the dB scale. According to Figure 17, all intervals and groups of users present relevant gain, even when there are no active hotspots (0 to 10 s). Additionally, as the number of active hotspots increases, the SINR degrades more severely, and the gain obtained tends to grow, since QL operates to improve the SINR. Hence, the two intervals with more active hotspots present over 80% gain. In addition, for most intervals, the gain is greater for hotspot users, which is interesting, considering the algorithm was not configured to act based on the performance of these users. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Interval** & **Scenario 1 (UEs)** & **Scenario 2 (UEs)** \\ \hline
0 to 10 s & 60 + 0 on HS & 40 + 0 on HS \\ \hline
10 to 20 s & 60 + 10 on HS & 40 + 20 on HS \\ \hline
30 to 40 s & 60 + 30 on HS & 40 + 60 on HS \\ \hline
40 to 50 s & 60 + 40 on HS & 40 + 80 on HS \\ \hline
50 to 60 s & 60 + 50 on HS & 40 + 100 on HS \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Active users at each simulation interval. Figure 19 presents throughput results for the same scenario. When the QL is not active, RsrqThreshold is fixed on 32. In this plot, three important results are combined: the throughput of users outside the hotspots (bottom bars), the throughput of hotspot users (top bars), and the average throughput of all users (combination of both bars). Each group of two parallel bars corresponds to a 10 s interval during the simulation (Table 11), and the bars on the left present the results when the QL is not active. The plot shows that the SINR gain resulted in throughput gain for all groups of users. However, the gain for users outside the hotspots is smaller when compared to hotspot users. Figure 17: Scenario 1 of the proposed solution: SINR Gain (%) for each group of users. Figure 18: Scenario 2 of the proposed solution: SINR Gain (%) for each group of users. Furthermore, Scenario 2 investigates the algorithm's performance with more users allocated in the hotspots and with different configurations for the _BandwidthDistribution_ parameter, which also has a significant impact on the evaluated scenario (Section 8). This scenario allocates less bandwidth for the common sub-band and more bandwidth for the private sub-band. Figure 18 presents the SINR gain for Scenario 2. Similar to Scenario 1, all groups of users have significant gain, and it is approximately doubled for the last three intervals, compared to the previous scenario. This is mainly due to the double amount of users on each hotspot of Scenario 2, which degrades the SINR more severely. Since the SINR is the target metric of the proposed algorithm, the gain is also doubled, showing that the QL is efficient in improving the SINR, even if the sub-bands are configured differently. Figure 20 presents throughput results for Scenario 2. There is a relevant gain for the average throughput and for hotspot users. However, the gain is minimal to users outside  
Figure 19: Scenario 1 of the proposed solution: throughput results for each group of users, with and without Q-Learning. Figure 20: Scenario 2 of the proposed solution: throughput results for each group of users, with and without Q-Learning. of hotspots.