Using Machine Learning (ML) techniques for the next generation wireless networks have
shown promising results in the recent years, due to high learning and adaptation capability …

Using Machine Learning (ML) techniques for the next generation wireless networks have
shown promising results in the recent years, due to high learning and adaptation capability …

The association of mobile devices with network resources (eg, base stations, frequency
bands/channels), known as load balancing, is critical to reduce communication traffic …

Within a cellular network, load balancing between different cells is of critical importance to
network performance and quality of service. Most existing load balancing algorithms are …

This paper proposes an Attention-based Multi-Agent Cooperation (AMAC) approach to
reduce message exchange overhead in Multi-Agent Reinforcement Learning-based smart …

As next generation cellular networks become denser, associating users with the optimal
base stations at each time while ensuring no base station is overloaded becomes critical for …

Resource management is a key challenge in multiagent systems. It is especially important in
dynamic environments where decisions need to be made quickly and when decisions can …

A smart Load Balancing (LB) policy based on Graph Convolutional Multi-Agent
Reinforcement Learning (GC-MARL) is proposed to improve load balancing in networks …

The amount of cellular communication network traffic has increased dramatically in recent
years, and this increase has led to a demand for enhanced network performance …

Distributed learning can lead to effective user association with low overhead, but faces
significant challenges in incorporating load balancing at all base stations (BS) because of …