Banca de DEFESA: IAGO DIÓGENES DO RÊGO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : IAGO DIÓGENES DO RÊGO
DATE: 05/05/2025
TIME: 08:00
LOCAL: Remota via Google Meet
TITLE:

Multi-Agent Reinforcement Learning for Inter-Cell Interference Management in Hotspots Scenarios

 

KEY WORDS:

Multiagent RL; Inter-cell Interference; Multi-Armed Bandit; Q-learning; ICIC; ; ns-3; hotspot

PAGES: 90
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Telecomunicações
SPECIALTY: Sistemas de Telecomunicações
SUMMARY:

Inter-cell interference (ICI) remains a critical challenge in mobile networks. Although current and future standards are rapidly evolving, the constant increase in data demand, the emergence of new use cases, the coexistence of multiple technologies, and the dynamic aspect of urban environments intensifies the impact of interference on system performance. ICI becomes especially challenging in dense deployments and in scenarios with zones of high user densities, referred to as hotspots. Fractional Frequency Reuse (FFR) is a well-established technique to mitigate ICI in OFDMA-based networks such as LTE and 5G, but traditional static configurations often fail to adapt to dynamic interference patterns. This thesis proposes a dynamic interference coordination framework based on reinforcement learning, designed to enhance the adaptability and performance of FFR techniques. The solution consists of a hierarchical multi-agent architecture, where two reinforcement learning agents operate in coordination, without direct exchange of information, to jointly control the allocation of bandwidth and user classification via the RSRQ threshold. The proposed approach was evaluated through network simulations using ns-3, across two different scenarios representing dense urban environments and massive connection conditions. The results show that the proposed framework consistently outperforms static and single-agent baselines, achieving throughput gains of up to 99.4\%, particularly under high-interference conditions and for low-performing users. Furthermore, its modular design allows integration of different learning strategies. While Q-Learning agents delivered the highest performance, Multi-Armed Bandit (MAB) agents achieved comparable results with significantly lower computational complexity. By combining classical ICIC techniques with reinforcement learning, this work presents a flexible and low-overhead interference mitigation strategy that can adapt to diverse and evolving network conditions.

COMMITTEE MEMBERS:
Presidente - 1412682 - VICENTE ANGELO DE SOUSA JUNIOR
Interno - 2524053 - ANTONIO LUIZ PEREIRA DE SIQUEIRA CAMPOS
Externo ao Programa - 1699087 - AUGUSTO JOSE VENANCIO NETO - UFRNExterno à Instituição - ANDRÉ MENDES CAVALCANTE
Externo à Instituição - DARIO VIEIRA CONCEICAO
Externo à Instituição - YURI CARVALHO BARBOSA SILVA