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 …

Mobile cellular network operators spend nearly a quarter of their revenue on network
management and maintenance. Incidentally, a significant proportion of that budget is spent …

This paper surveys the literature relating to the application of machine learning to fault
management in cellular networks from an operational perspective. We summarise the main …

This letter presents a novel cell outage detection algorithm based on incoming handovers
statistics. The main advantage of the proposed algorithm is that it uses neighbor …

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 …

With the advent of IoT and the seamless advent of the ubiquitous network using the 5G
technologies, the ability to provide a continuous service is the requirement of every Internet …

Anomaly Detection (AD) is a promising new approach for quality control in eg operational
telecommunications and data networks. In this paper we have applied Supervised Machine …

In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect,
diagnose and correct degraded states in the managed network functions or other resources …

The ever-increasing amount of data in cellular networks poses challenges for network
operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs) …