A survey of machine learning techniques applied to self-organizing cellular networks
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 …
algorithms applied to self-organizing cellular networks is performed. In order for future …
Machine learning-based load balancing algorithms in future heterogeneous networks: A survey
The massive growth of mobile users and the essential need for high communication service
quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) …
quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) …
A survey of online data-driven proactive 5G network optimisation using machine learning
In the fifth-generation (5G) mobile networks, proactive network optimisation plays an
important role in meeting the exponential traffic growth, more stringent service requirements …
important role in meeting the exponential traffic growth, more stringent service requirements …
Cognitive cellular networks: A Q-learning framework for self-organizing networks
SS Mwanje, LC Schmelz… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Self-organizing networks (SON) aim at simplifying network management (NM) and
optimizing network capital and operational expenditure through automation. Most SON …
optimizing network capital and operational expenditure through automation. Most SON …
On the potential of handover parameter optimization for self-organizing networks
Self-organizing networks (SONs) aim to raise the level of automated operation in next-
generation networks. One of the use cases defined in this field is the optimization of the …
generation networks. One of the use cases defined in this field is the optimization of the …
Design and exploration of load balancers for fog computing using fuzzy logic
This research work presents an exploratory study to find answers for managing traffic and
payloads in edge/fog zone of the networks. Initial deployments have shown that edge …
payloads in edge/fog zone of the networks. Initial deployments have shown that edge …
Fuzzy rule-based reinforcement learning for load balancing techniques in enterprise LTE femtocells
Mobile-broadband traffic has experienced a large increase over the past few years.
Femtocells are envisioned to cope with such a demand of capacity in indoor environments …
Femtocells are envisioned to cope with such a demand of capacity in indoor environments …
Optimization of load balancing using fuzzy Q-learning for next generation wireless networks
Load balancing is considered by the 3GPP as an important issue in Self-Organizing
Networks due to its effectiveness to increase network capacity. In next generation wireless …
Networks due to its effectiveness to increase network capacity. In next generation wireless …
A survey on requirements of future intelligent networks: solutions and future research directions
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 …
solutions, and current research directions through a survey technique. The background of …
Handover Decision Making for Dense HetNets: A Reinforcement Learning Approach
In this paper, we consider the problem of decision making in the context of a dense
heterogeneous network with a macro base station and multiple small base stations. We …
heterogeneous network with a macro base station and multiple small base stations. We …