Gerência de largura de banda do reúso fracionário de frequência em cenários dinâmicos com Hotspots utilizando aprendizado por reforço

Abstract

The present work approaches the use of Inter-Cell Interference Coordination (ICIC) techniques, based on techniques Fractional Frequency Reuse (FFR) aimed at mitigating one of the main challenges faced when designing mobile networks: co-channel interference (CCI). ICIC is one of the primary factors contributing to the degradation of mobile communication system performance, especially in dynamic scenarios with randomly distributed user clusters, known as hotspots. Initially, the problem is characterized by the exploration of the chosen ICIC technique without automated parameterization, as well as the system’s performance in the aforementioned scenario. Then, considering the dynamic nature of the environment, the proposed solution involves the use of machine learning techniques—more specifically, the Multi-Armed Bandit (MAB)—customized for optimizing bandwidth management in ICIC techniques for 3GPP systems. The objective of the proposal is to adapt the spatial allocation of bandwidth, using system load variation and hotspot dynamics as decision-making parameters. Proof-of-concept experiments were conducted using the ns-3 network simulator. The results demonstrate that the MAB-based approach proposed in this dissertation outperforms static bandwidth distribution configurations in terms of throughput, even under varying system load conditions, showing significant gains.