In this article, we propose a deep reinforcement learning (DRL)-based mobility load
balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load …

In a highly dense heterogeneous cellular network, the loads across cells are uneven due to
random deployment of cells and the mobility of user equipments (UEs). Such unbalanced …

The massive growth of mobile users and the essential need for high communication service
quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) …

Recently, deep learning has emerged as a state-of-the-art machine learning technique with
promising potential to drive significant breakthroughs in a wide range of research areas. The …

Load balancing has become a key technique to handle the increasing traffic demand and
improve the user experience. It evenly distributes the traffic across network resources by …

Abstract Self-Organizing Networks (SON) is a collection of functions for automatic
configuration, optimization, and healing of networks and mobility optimization is one of the …

With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has
become an important solution in supporting dense scenarios such as smart cities. The …

Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely
deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to …

Due to the rapid development of network communication technology and the significant
increase in network terminal equipment, the application of new network architecture …

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile
users a high transmission rate and efficient radio resource management. However, UDNs …