Handover Baseado em Aprendizado de Máquina para Redes LTE com Falhas de Cobertura

Tarciana Cabral de Brito Guerra, Vicente A. de Sousa Jr., Ycaro Dantas

DOI: 10.14209/sbrt.2019.1570557393

Evento: XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2019)

Keywords:

Abstract

The availability of information regarding the network's performance and the evolution of the capacity of big data's real time processing have enabled new approaches based on machine learning techniques to enhance classical features of mobile networks, like handover. This paper proposes and analyse the performance of handover strategies based on machine learning that privileges user's Quality of Service (QoS) in LTE networks where specific eNBs witness coverage holes, a common situation in hierarchical cell structures or the so called Overlay Network Architecture, regarding the 5G nomenclature. Our proposed solutions perform better than existing ones with reduced computational complexity.

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