In this paper, a survey of the literature of the past 15 years involving machine learning (ML)
algorithms applied to self-organizing cellular networks is performed. In order for future …

Self-organizing networks (SON) aim at simplifying network management (NM) and
optimizing network capital and operational expenditure through automation. Most SON …

Due to the diversity of mobile services and rising user expectations, mobile network
management has changed its focus from Quality of Service (QoS) to Quality of Experience …

The optimization of handover (HO) parameters for in-building systems is investigated in this
paper. We proposed a novel methodology that provides in-building base stations with the …

To satisfy the demand for higher data rate while maintaining the quality of service, a dense
long-term evolution (LTE) cells environment is required. This imposes a big challenge to the …

In ultra-dense networks, an enormous number of small base stations (BSs) are required to
be deployed, and the number of handovers (HOs) in heterogeneous networks (HetNets) …

Optimal settings for Handover parameters (Hysteresis and Time-to-Trigger) depend on user
velocities in the network. The Self-Organization Networks (SON) standard defines the …

Mobility Robustness Optimization (MRO) is proposed in Long Term Evolution (LTE)
networks to improve handover performance by 3GPP (3rd Generation Partnership Project) …

Mobility Robustness optimization (MRO) is widely considered as an important self-
organizing network (SON) usecase for tackling mobility management problems in …

Handover optimization is an important task for the load balancing and mobility robustness in
cellular networks. However, the cells in a cellular network often overlap and present strong …