Publicação: A Dynamic GPF scheduler proposal backed by Differential Evolution and Neural Network on 5G HetNet simulated scenarios
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To achieve the desired 5G goals, a network must be efficient and fair simultaneously,
allowing high data rates for regular users while also equally serving low-power users. Static
schedulers, as the name implies, don’t change their scheduling policy regardless of the
number of users, giving rise to the need for dynamic schedulers that can shift this paradigm
by sensing the number of network users and other parameters like Signal-to-Interferenceplus-
Noise Ratio (SINR) and changing the scheduling policy accordingly, raising network
efficiency and fairness. Thus, this work presents an optimization technique that uses the
Differential Evolution (DE) algorithm and, later, trains a feedforward Neural Network
(NN) to mimic the DE’s decision-making to act as a dynamic Generalized Proportional Fair
(GPF), adapting its two internal parameters, α and β, assuring a threshold Jain’s fairness
index value while seeking a throughput maximization in a simulated 5G Heterogeneous
Network (HetNet). Results show that the method achieves a minimal 0.7 Jain’s fairness
index for most numbers of users, from 40 to 200 users, while having a better average
throughput when compared to a related scheduler. It was also noted that the implemented
DE algorithm took an impractical runtime to work as an online optimizer; in contrast,
using the created dynamic NN scheduler added only 0.03% to 0.57% in simulation runtime
compared to GPF.
Abstract
To achieve the desired 5G goals, a network must be efficient and fair simultaneously,
allowing high data rates for regular users while also equally serving low-power users. Static
schedulers, as the name implies, don’t change their scheduling policy regardless of the
number of users, giving rise to the need for dynamic schedulers that can shift this paradigm
by sensing the number of network users and other parameters like Signal-to-Interferenceplus-
Noise Ratio (SINR) and changing the scheduling policy accordingly, raising network
efficiency and fairness. Thus, this work presents an optimization technique that uses the
Differential Evolution (DE) algorithm and, later, trains a feedforward Neural Network
(NN) to mimic the DE’s decision-making to act as a dynamic Generalized Proportional Fair
(GPF), adapting its two internal parameters, α and β, assuring a threshold Jain’s fairness
index value while seeking a throughput maximization in a simulated 5G Heterogeneous
Network (HetNet). Results show that the method achieves a minimal 0.7 Jain’s fairness
index for most numbers of users, from 40 to 200 users, while having a better average
throughput when compared to a related scheduler. It was also noted that the implemented
DE algorithm took an impractical runtime to work as an online optimizer; in contrast,
using the created dynamic NN scheduler added only 0.03% to 0.57% in simulation runtime
compared to GPF.
Palavras-chave
5G, Differential Evolution (DE), Generalized Proportional Fair (GPF) scheduler, Heterogeneous Network (HetNet), Neural Network, Proportional Fair (PF) scheduler, Engenharia elétrica
Citação
RESENDE, Getúlio Martins. A Dynamic GPF scheduler proposal backed by Differential Evolution
and Neural Network on 5G HetNet simulated scenarios. 2025. 77 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2026. DOI http://doi.org/10.14393/ufu.di.2025.731.
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