A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

By Paulo Valente Klaine, Muhammad Ali Imran, Oluwakayode Onireti, Richard Demo Souza. 2017

414 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[223] have provided a survey of traditional machine learning approaches for self-organizing cellular networks.”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “FNN RNN DNN ESN SNN DA RL UAV VR CC SON Multi-RAT IoT PL CR DT [3] p p p [32] p p [50] p [51] p p p p [52] p p p [53] p p p p [54] p p p [63] p [55] p p [56] p p p p [57] p p p p p p [58] p p p p p p [59] p p p p p [60] p p p p p p p p [61] p p p p p p p p p [62] p p p p p p p Our tutorial p p p p p”
Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning (2018, background): “In this sense, algorithms that can adapt themselves, such as artificial intelligence and machine learning, should be deployed [1–4], to enable a fully autonomous network.”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “As far as communications are concerned, most previous surveys discuss general machine learning techniques [14], [17], [18], [38]–[40], without providing a dedicated analysis of deep learning.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “A number of important applications emerge in the computer network domain [57], such as Web mining [449], caching [450] and base station sleep strategies [184].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “of Things [57], or self-organizing networks (SONs) [59], and, hence, they do not comprehensively cover the broad range of applications that can adopt ML in future networks.”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “Reference [12] has surveyed the machine learning algorithms and their solutions to achieve the selfconfiguration, self-optimization and self-healing functions.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “Numerous effective ML practices were also used in selforganizing networks [34].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “Some of them focused their scope on a specific wireless scenario, such as WSNs [24], [25], cognitive radio networks (CRN) [26]–[28], Internet of Things (IoT) [29], wireless ad hoc networks (WANET) [30], self-organizing cellular networks [31], etc.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, neutral): “[14] IEEE COMST 2017 269 ML in SONs ≈ × × √”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “Particularly, other works focus primarily on the advances and needs of a single learning mechanism or modality [19], [109], or towards improvements in a single application [38], [67], [68], [98].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, background): “ML has actually been long exploited for self-organization [149].”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “Here, it is necessary to distinguish between two different time metrics, namely training time and response time as per [26].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Though there are various surveys on ML in networking [18, 61, 82, 142, 246, 339], this survey is purposefully different.”