Design and validation of an open source cloud native mobile network

N Apostolakis, M Gramaglia… - IEEE Communications …, 2022 - ieeexplore.ieee.org
Network technologies are embracing the cloud-native paradigm, following the current best
practices in cloud computing. Cloud-native technologies might be applied to different types …

Unsupervised machine learning in 6g networks-state-of-the-art and future trends

VP Rekkas, S Sotiroudis, P Sarigiannidis… - … on modern circuits …, 2021 - ieeexplore.ieee.org
Wireless communication systems play a very crucial role for business, commercial, health
and safety applications. With the commercial deployment of fifth generation (5G), academic …

Machine learning for large-scale optimization in 6g wireless networks

Y Shi, L Lian, Y Shi, Z Wang, Y Zhou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Edge intelligence in 6G systems

C Chaccour, W Saad - 6G Mobile Wireless Networks, 2021 - Springer
In this chapter, we provide a vision for edge intelligence as a key building block of 6G
wireless systems. As we evolve towards a new breed of wireless services, enabled by an …

Testbed for 5G connected artificial intelligence on virtualized networks

CV Nahum, LDNM Pinto, VB Tavares, P Batista… - IEEE …, 2020 - ieeexplore.ieee.org
The fifth-generation (5G) cellular networks incorporate a large variety of technologies in
order to address very distinct use cases. Assessing these technologies and investigating …

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 …

Deep learning at the mobile edge: Opportunities for 5G networks

M McClellan, C Cervelló-Pastor, S Sallent - Applied Sciences, 2020 - mdpi.com
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing,
storage, and analysis closer to the end user. The increased speeds and reduced delay …

Implementation and Evaluation of a Federated Learning Framework on Raspberry PI Platforms for IoT 6G Applications

L Ridolfi, D Naseh, SS Shinde, D Tarchi - Future Internet, 2023 - mdpi.com
With the advent of 6G technology, the proliferation of interconnected devices necessitates a
robust, fully connected intelligence network. Federated Learning (FL) stands as a key …

Artificial intelligence powered mobile networks: From cognition to decision

G Luo, Q Yuan, J Li, S Wang, F Yang - IEEE Network, 2022 - ieeexplore.ieee.org
Mobile networks (MNs) are anticipated to provide unprecedented opportunities to enable a
new world of connected experiences and radically shift the way people interact with …

Machine learning in the air

D Gündüz, P de Kerret, ND Sidiropoulos… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …