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 …

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 …

The next generation wireless networks are expected to operate in fully automated fashion to
meet the burgeoning capacity demand and to serve users with superior quality of …

In this paper, we present an auto-encoder-based machine learning framework for self
organizing networks (SON). Traditional machine learning approaches, for example, K …

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 …

Unusual Spatio-Temporal fluctuations in cellular network traffic may lead to drastic network
management misbehaviors and at least abnormal drops in quality of experience. It is also …

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 …

The increasing amount of network elements in the current deployments of cellular networks
is leading to an enormous complexity of the operation and maintenance. Self-organizing …

In this paper, we present an overview of Nearest neighbour (NN) methods, which are
frequently employed for solving classification problems using supervised learning. The …

The rapid increase of data traffic has brought great challenges to the maintenance and
optimization of 5G and beyond, and some smart critical infrastructures, eg, small base …