A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges

J **e, FR Yu, T Huang, R **e, J Liu… - … Surveys & Tutorials, 2018 - ieeexplore.ieee.org
In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …

A very brief introduction to machine learning with applications to communication systems

O Simeone - IEEE Transactions on Cognitive Communications …, 2018 - ieeexplore.ieee.org
Given the unprecedented availability of data and computing resources, there is widespread
renewed interest in applying data-driven machine learning methods to problems for which …

Machine learning tips and tricks for power line communications

AM Tonello, NA Letizia, D Righini, F Marcuzzi - IEEE Access, 2019 - ieeexplore.ieee.org
A great deal of attention has been recently given to Machine Learning (ML) techniques in
many different application fields. This paper provides a vision of what ML can do in Power …

Adaptive ML-based frame length optimisation in enterprise SD-WLANs

E Coronado, A Thomas, R Riggio - Journal of Network and Systems …, 2020 - Springer
Abstract Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems
with several practical implementations and numerous proposals being made. Despite …

Wireless LAN performance enhancement using double deep Q-networks

K Asaf, B Khan, GY Kim - Applied Sciences, 2022 - mdpi.com
Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …

Cognitive software-defined networking using fuzzy cognitive maps

G Baggio, R Bassoli, F Granelli - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Future networks are expected to provide improved support for several different kinds of
applications and services. All these services will have diverse characteristics and …

[HTML][HTML] Q-learning based fair and efficient coexistence of LTE in unlicensed band

R Bajracharya, R Shrestha, SW Kim - Sensors, 2019 - mdpi.com
The increased demand for spectrum resources for multimedia communications and a limited
licensed spectrum have led to widespread concern regarding the operation of long term …

ML-based handover prediction and AP selection in cognitive Wi-Fi networks

MA Khan, R Hamila, A Gastli, S Kiranyaz… - Journal of Network and …, 2022 - Springer
Device mobility in dense Wi-Fi networks offers several challenges. Two well-known
problems related to device mobility are handover prediction and access point selection. Due …

Machine learning based obstacle detection for Automatic Train Pairing

R Sattiraju, J Kochems… - 2017 IEEE 13th …, 2017 - ieeexplore.ieee.org
Short Range wireless devices are becoming more and more popular for ubiquitous sensor
and actuator connectivity in industrial communication scenarios. Apart from communication …

A machine learning-based ETA estimator for Wi-Fi transmissions

D Del Testa, M Danieletto… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Recent advancements related to device to device (D2D) communication make it possible for
a transmitting node to dynamically select the interface to be used for data transfers locally …