Estudo de Algoritmos de Escalonamento em Redes 5G Utilizando Simulações

Abstract

5G mobile communication networks require efficient solutions to meet Quality of Service (QoS) requirements in scenarios with high user density and different types of traffic. This work investigates the performance of scheduling algorithms in 5G networks, using systemlevel simulations to analyze the effectiveness of the Round Robin (RR), Best Channel Quality Indicator (Best CQI), and Quality of Service Aware Scheduler (QAS) algorithms. The research is based on scenarios considering variations in the number of users distributed among Real-Time (RT) and Non-Real-Time (NRT) services. The simulation results showed that the QAS stood out for its ability to adapt to the specific demands of RT traffic, ensuring lower latency and lower Block Error Rates (BLER), even under high network load conditions. The Best CQI performed well in situations where channel quality favored NRT services, while the RR provided a uniform distribution of resources but had limitations in adaptive efficiency for different types of traffic. The analyses indicate that the QAS is the best option for scenarios that require high QoS, with the capacity to maximize overall throughput and provide a satisfactory user experience for different traffic profiles. This study contributes to understanding how different scheduling algorithms can be applied to optimize the performance of 5G networks, assisting in choosing the most suitable solution to meet the growing demands of these networks.