Please use this identifier to cite or link to this item:
metadata.dc.type: Tese
metadata.dc.rights: Acesso Aberto
Title: A new genetic algorithm based scheduling algorithm for the LTE Uplink
metadata.dc.creator: Mata, Saulo Henrique da
metadata.dc.contributor.advisor1: Guardieiro, Paulo Roberto
metadata.dc.contributor.referee1: Borin, Juliana Freitag
metadata.dc.contributor.referee2: Teixeira, Márcio Andrey
metadata.dc.contributor.referee3: Silva, Éderson Rosa da
metadata.dc.contributor.referee4: Cunha, Marcio José da
metadata.dc.description.resumo: Long Term Evolution has become the de facto technology for the 4G networks. It aims to deliver unprecedented data transmission rates and low latency for several types of applications and services. In this context, this thesis investigates the resource allocation in the LTE uplink. From the principle that resource allocation in the uplink is a complex optimization problem, the main contribution of this thesis is a novel scheduling algorithm based on Genetic Algorithms (GA). This algorithm introduces new operations of initialization, crossover, mutation and a QoS-aware fitness function. The algorithm is evaluated in a mixed traffic environment and its performance is compared with relevant algorithms from the literature. Simulations were carried out in ns-3 and the results show that the proposed algorithm is able to meet the Quality of Service (QoS) requirements of the applications, while presenting a satisfactory execution time.
Keywords: Engenharia elétrica
Long-Term Evolution (Telecomunicações)
Algoritmos genéticos
Genetic Algorithms
metadata.dc.language: por Brasil
Publisher: Universidade Federal de Uberlândia
metadata.dc.publisher.program: Programa de Pós-graduação em Engenharia Elétrica
Citation: MATA, Saulo Henrique da. A new genetic algorithm based scheduling algorithm for the LTE Uplink. 2017. 120 f. Dissertação (Mestrado em Ciências) - Universidade Federal de Uberlândia, Uberlândia, 2017.
Issue Date: 22-May-2017
Appears in Collections:TESE - Engenharia Elétrica

Files in This Item:
File Description SizeFormat 
NewGeneticAlgorithm.pdf17.08 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.