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 …

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

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 …

Abstract Self-Organizing Network (SON) stands for a key concept characterizing the
behavior of the future mobile networks. The evolution of telecom infrastructures towards 5G …

Leveraging high-performance software-defined networks (SDNs) to manage industrial
Internet of Things (IIoT) devices has become a promising trend; the SDN is expected to be …

The exponential rise in mobile traffic originating from mobile devices highlights the need for
making mobility management in future networks even more efficient and seamless than ever …

One of the fundamental goals of mobile networks is to enable uninterrupted access to
wireless services without compromising the expected quality of service (QoS). This paper …

Artificial intelligence (AI) controls are commonly used to save energy. However, excessive
diversity in technological development has resulted in the inability to provide consistent …

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