# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
###### Abstract  
This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (MIMO) and small cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case. ICIC; FFR; hotspot ns-3; Q-Learning; machine learning +
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definition  
management of co-channel interference by reusing the spectrum, and the implementation of more efficient modulation schemes. For example, Long Term Evolution (LTE) originally proposes a reuse factor of 1 in order to increase spectrum efficiency. A reuse factor of 1 means that all network cells operate in the same frequency band. A reuse factor of 3, on the other hand, decreases the band available to each cell by 3, decreasing the channel's capacity. However, a reuse factor of 1 may lead to low Signal-to-interference-plus-noise Ratio (SINR), especially to users located further away from the e-NodeB (eNB) (radio base station on LTE), due to interference from neighboring cells. These techniques aim at increasing channel capacity, but they also introduce new challenges, mainly related to the compromise between coverage and quality of service. For example, enhanced Mobile Broadband (eMBB) has been a widely discussed topic in the literature. It represents the evolution of mobile communications and it has been the focus in the first commercial deployments of 5G. One of its goals is to provide high data rates and wide coverage. However, as 5G operates in high frequencies, the coverage tends to decrease, as the signal attenuation is more severe. Consequently, the network will likely have to deploy more base stations, increasing the need to mitigate interference from adjacent cells. In addition, some aspects that have strong impacts on mobile network performances, such as the traffic demand or the amount and distribution of users, are often unpredictable or variant through time. Big cities usually host events that can last from a few hours to a couple of days and these can be either recurrent or a one time occasion. Events and locations, such as school fairs, football games, music festivals, school parades, food parks, and malls can suddenly increase the number and density of users at that location. The planning and deployment of a mobile network is traditionally associated with modeling the channel based on the specific demand for a location. This usually does not take into account some parameters that can vary in an unexpected way, as the ones mentioned above.