As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …

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-organization as applied to cellular networks is usually referred to Selforganizing
Networks (SONs), and it is a key driver for improving Operations, Administration, and …

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

As heterogeneous networks (HetNets) emerge as one of the most promising developments
toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced …

With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …

Radio resource and its management is one of the key areas of research where technologies,
infrastructure and challenges are rapidly changing as 5G system architecture demands a …

Future 6G wireless communication systems are expected to feature intelligence and
automation. Knowledge-defined networking (KDN) is an evolutionary step toward …

The context of this study examines the requirements of Future Intelligent Networks (FIN),
solutions, and current research directions through a survey technique. The background of …

Cell range expansion (CRE) is a technique to expand a pico cell range virtually by adding a
bias value to the pico received power, instead of increasing transmit power of pico base …