# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 5 Related Works  
Metropolitan areas around the world have been growing significantly, alongside the number of users on wireless networks. This growth has encouraged the literature to widely discuss scenarios with a high density of users. Hence, the densification of the network is indicated as an efficient approach to better serve urban areas, including 5G networks [15]. Consequently, various works introduce ways of using small cells to boost the performance of scenarios with hotspots. The authors of [28] propose a scenario where macrocells are populated with picocells centered on hotspots. The paper shows that the small coverage offered by the picocells causes an imbalance in user allocation, overloading the macrocells. Therefore, a dense ring of macrocell users is formed around the picocells. These users experience high interference coming from the picocells, and they are considered as victim users. They  
Figure 4: Bandwidth distribution and power allocation for the soft fractional frequency reuse algorithm in a cluster of three cells. propose two dynamic techniques of eICIC using two different metrics to improve the Quality of Service (QoS) of the victim users while muting picocell users. A realistic dense scenario was evaluated in [29]. It simulated a wide urban area with 300 active users per km\({}^{2}\). Half of the users were uniformly distributed without the scenario, and the other half were uniformly distributed inside circular hotspots with a 40 m radius. For this scenario, the simulation results show that it is possible to achieve an average of 1 Gbps per user. However, they also noted that, given the year of publishing, the solution was not applicable due to its high cost and low energy efficiency. The authors of [30] evaluated a scenario that deployed macrocells with three sectors each and four picocells per sector. Moreover, a fraction of the users were allocated in hotspots centered in the picocells. They proposed the use of time-domain ICIC, using Almost Blank Subframes (ABS) and Cell Range Extension (CRE) to improve performance and alleviate the macrocell load. The results show that the imbalance between cells is effectively countered with an offset value of 10 dB for the CRE. Besides, for each CRE offset, there was an optimum value for the ratio of protected subframes. Thus, if both parameters are properly configured within a certain range, the performance is almost the same. Another technique used to improve the performance of hotspot scenarios is the use of multiple antennas. The authors of [8] proposed a HetNet scenario with small cells operating in the same frequency as macrocells. All users were allocated in hotspots and the small cells were located at the center of some of these hotspots, representing an intentional deployment of small cells. The solution focuses on mitigating the interference coming from the small cells through a beamforming scheme proposed in [7]. This scheme concentrates the transmission energy to the hotspots while creating transmission opportunities for users in other directions. A scheduling algorithm that combines frequency allocation and beamforming (beams width and direction) is proposed in [31]. A homogeneous and a hotspot scenario were considered. The paper focused on maximizing throughput according to QoS requirements, and it compared the results to other approaches. The results show that the hotspot scenario is more challenging for all the algorithms. However, the proposed solution has better performance in terms of complexity and throughput. The authors of [32] proposed a solution that served UEs in a hotspot scenario using virtual MIMO, i.e., user cooperation to enable spatial multiplexing. Compared to the traditional use of MIMO, this approach can avoid the costs of new antennas or access points dedicated to the hotspots. Furthermore, the signal processing was done in the mobile station, avoiding any new processing units.