A Machine Learning Model to Resource Allocation Service for Access Point on Wireless Network

Author(s)

Militani, Davi ; Vieira, Samuel ; Valadao, Everthon ; Neles, Katia ; Rosa, Renata ; Rodriguez, Demostenes Z. ; Begusic, D ; Rozic, N ; Radic, J ; Saric, M

• Document type: Journal article
• Source: 2019 27TH INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS (SOFTCOM); v. N/A, p. 6-pg., 2019-01-01.

Abstract

Currently, an access point (AP) is usually selected based on the signal strength parameter. However, the signal strength is not a guarantee of a good quality of service (QoS). Machine learning algorithms are used to automatically learn and improve some tasks and based on a network device characteristics is possible to select the most important input for a better network coverage. Thus, in this paper is implemented a Resource Allocation service for wireless networks based on machine learning algorithms. In this research, the Random Forest algorithm was implemented to automatically determine the AP selection strategy (SS). The results of the RF algorithm applied to heterogeneous network technologies showed an improvement of the channel condition, in relation to the throughput. In the validation tests phase, the experimental results demonstrated that our proposed AP SS based on Random Forest algorithm outperforms an existing AP SS based on signal strength. (AU)

• FAPESP's process: 15/24496-0 - Evaluation of the service of communication operators using the voice Quality Index
• Grantee: Demostenes Zegarra Rodriguez
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/26455-8 - Audio-Visual Speech Processing by Machine Learning
• Grantee: Miguel Arjona Ramírez
• Support Opportunities: Regular Research Grants