Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
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 wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/23/7899 |
| _version_ | 1797507251652526080 |
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| author | Iago Diógenes do Rego Vicente A. de Sousa |
| author_facet | Iago Diógenes do Rego Vicente A. de Sousa |
| author_sort | Iago Diógenes do Rego |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:45:53Z |
| format | Article |
| id | doaj.art-ddae57a5dead42af94d4a61e98fc71a1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:45:53Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ddae57a5dead42af94d4a61e98fc71a12023-11-23T03:00:58ZengMDPI AGSensors1424-82202021-11-012123789910.3390/s21237899Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC TechniquesIago Diógenes do Rego0Vicente A. de Sousa1Department of Communications Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, BrazilDepartment of Communications Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, BrazilThis 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.https://www.mdpi.com/1424-8220/21/23/7899ICICFFRhotspotns-3Q-Learningmachine learning |
| spellingShingle | Iago Diógenes do Rego Vicente A. de Sousa Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques Sensors ICIC FFR hotspot ns-3 Q-Learning machine learning |
| title | Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques |
| title_full | Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques |
| title_fullStr | Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques |
| title_full_unstemmed | Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques |
| title_short | Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques |
| title_sort | solution for interference in hotspot scenarios applying q learning on ffr based icic techniques |
| topic | ICIC FFR hotspot ns-3 Q-Learning machine learning |
| url | https://www.mdpi.com/1424-8220/21/23/7899 |
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