# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 7 Preliminary Analysis A: Performance Comparison of ICIC Algorithms
### Results and Discussions  
For Scenario 1 (concentrated users), Figures 7 and 8 present the 10 th percentile throughput and the SINR Cumulative distribution function (CDF), respectively. The curves with dotted lines represent cell-edge users and the continuous represent cell-center users.  
Regarding throughput, Figure 7 shows that the NoOp algorithm presents the best performance for cell-center users. All cells operate on the same frequency band; thus, more bandwidth is available, and a higher data rate is obtained. However, this is also the reason why NoOp has the worst performance for edge users, considering these UEs are more affected by interference.  
The SFR obtains good performance for cell-center users but poor performance for cell-edge users. The high number or RBs reserved for cell-edge users was not enough to balance Inter-Cell Interference (ICI), since the cell-edge bandwidth is shared with cell-center regions from adjacent cells.  
Figure 8 indicates that cell-edge users have lower SINR for all algorithms, and the worst results for both sets of users are from SFR and NoOp. HFR has the best performance for cell-center users, but Strict FR and SFFR have better performance for cell-edge users while also maintaining good performance on cell-center, similar to HFR. Moreover, considering the lower spectral efficiency, as discussed before, the HFR algorithm is not able to provide good throughput levels.  
Figures 9 and 10 show the results for Scenario 2, with less concentrated users. Concerning throughput (Figure 9), there is a significant performance loss for NoOp and SFR, mainly because cell-center UEs can interfere with cell-edge UEs. As a result, spreading the users can increase interference, due to higher occupation of the cell-center region. Additionally, Strict FR and SFFR still have the best throughput levels for cell-edge users.  
Regarding SINR results, Figure 10 shows that NoOp and SFR still have the worst performance in all cases and the Strict FR and SFFR algorithms maintain good performance  
Figure 8: Scenario 1 from preliminary analysis A: SINR CDF for concentrated users (R = 100 m). Each curve represents a different ICIC algorithm.  
Figure 9: Scenario 2 from preliminary analysis A: 10 th percentile throughput for less concentrated users (R = 500 m). Each curve represents a different ICIC algorithm.  
on cell-edge. Nevertheless, all algorithms present lower SINR on cell-edge, since, in this case, spreading the users led to more populated cell-edge regions.  
Some important conclusions can be outlined:  
* FFR-based ICIC techniques can improve the performance of a mobile network, given the high interference suffered by its users. It is possible to enhance cell-edge performance without compromising cell-center users;
* Simply changing the user density can strongly impact on system performance, especially if the bandwidth is divided into sub-bands. The concentration or spreading of the users can result in different occupation of the sub-bands, leading to overloaded or nearly empty sub-bands;
* There is no scheme that performs best in every situation. However, the Strict FR and the SFFR schemes have a good performance in different scenarios, and they are both efficient at reducing ICI, especially for cell-edge users;
* The Strict Frequency Reuse (Strict FR) algorithm has the best compromise between performance and complexity, considering SFFR has a higher number of sub-bands, which leads to more complexity when adjusting the parameters. Therefore, the Strict FR is the object of the study presented in the following sections.