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 …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …

Malware poses a significant threat to computer systems and networks. This necessitates the
development of effective detection mechanisms. Detection mechanisms dependent on …

Virtualized network slicing allows a multitude of logical networks to be created on a common
substrate infrastructure to support diverse services. A virtualized network slice is a logical …

Network slicing can partition a shared substrate network into multiple logically isolated
virtual networks to support diverse service requirements. However, one anomaly physical …

The growing subscriber Quality of Experience demands are posing significant challenges to
the mobile cellular network operators. One such challenge is the autonomic detection of …

Self-organizing network (SON) is a well-known approach to reduce the complexity and the
cost of cellular network management. It aims at replacing the manual configuration and …

In this paper, we suggest a new research direction and a future vision for Self-Healing (SH)
in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH …

Leveraging machine learning (ML) for the detection of network problems dates back to
handling call-dropping issues in telephony. However, troubleshooting cellular networks is …