Machine learning at the edge: A data-driven architecture with applications to 5G cellular networks

M Polese, R Jana, V Kounev, K Zhang… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy
the ultra-low latency demand of future applications. In this paper, we argue that such …

Automatic handover execution technique using machine learning algorithm for heterogeneous wireless networks

N Nayakwadi, R Fatima - International Journal of Information Technology, 2021 - Springer
Integrating LTE sub-6 GHz and millimeter wave (mmWave) bands brings great benefit in
increasing communication bandwidth, reliability, and better coverage of future smart …

Resource optimization-based network selection model for heterogeneous wireless networks

N Nayakwadi, R Fatima - IAES International Journal of …, 2023 - search.proquest.com
The internet of things (IoT) environment prerequisite seamless connectivity for meeting real-
time application requirements; thus, required efficient resource management techniques …

Mobility aware handover execution model for heterogeneous wireless networks

N Nayakwadi, R Fatima - 2021 International conference on …, 2021 - ieeexplore.ieee.org
Modern (Internet of Things) IoT applications requires seamless connectivity with high
reliability; thus the IoT device are connected to heterogeneous wireless network by …

End-to-End Design and Evaluation of mmWave Cellular Networks

M Polese - 2019 - research.unipd.it
The next generation of cellular networks (5G) is being designed to provide unprecedented
performance in mobile scenarios, with an increase in capacity, ultra-low latency and a …