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 …
Machine learning for large-scale optimization in 6g wireless networks
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 …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
AI-enabled future wireless networks: Challenges, opportunities, and open issues
M Elsayed, M Erol-Kantarci - IEEE Vehicular Technology …, 2019 - ieeexplore.ieee.org
An expected plethora of demanding services and use cases mandates a revolutionary shift
in the way future wireless network resources are managed. Indeed, when application …
in the way future wireless network resources are managed. Indeed, when application …
On softwarization of intelligence in 6G networks for ultra-fast optimal policy selection: Challenges and opportunities
The emerging Sixth Generation (6G) communication networks promising 100 to 1000 Gb/s
rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial …
rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial …
A machine-learning-based framework for optimizing the operation of future networks
5G and beyond are not only sophisticated and difficult to manage, but must also satisfy a
wide range of stringent performance requirements and adapt quickly to changes in traffic …
wide range of stringent performance requirements and adapt quickly to changes in traffic …
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 …
storage, and analysis closer to the end user. The increased speeds and reduced delay …
AI in 6G: Energy-efficient distributed machine learning for multilayer heterogeneous networks
MA Hossain, AR Hossain, N Ansari - IEEE Network, 2022 - ieeexplore.ieee.org
Adept network management is key for supporting extremely heterogeneous applications
with stringent quality of service (QoS) requirements; this is more so when envisioning the …
with stringent quality of service (QoS) requirements; this is more so when envisioning the …
A data-driven multiobjective optimization framework for hyperdense 5G network planning
BB Haile, E Mutafungwa, J Hämäläinen - Ieee Access, 2020 - ieeexplore.ieee.org
The trials and rollout of the fifth generation (5G) network technologies are gradually
intensifying as 5G is positioned as a platform that not only accommodates exploding data …
intensifying as 5G is positioned as a platform that not only accommodates exploding data …
A federated deep learning empowered resource management method to optimize 5G and 6G quality of services (QoS)
H Alsulami, SH Serbaya… - Wireless …, 2022 - Wiley Online Library
The quality of service (QoS) in 5G/6G communication enormously depends upon the
mobility and agility of the network architecture. An increase in the possible uses of 5G …
mobility and agility of the network architecture. An increase in the possible uses of 5G …
Machine learning for 5G and beyond: From model-based to data-driven mobile wireless networks
T Wang, S Wang, ZH Zhou - China Communications, 2019 - ieeexplore.ieee.org
During the past few decades, mobile wireless communications have experienced four
generations of technological revolution, namely from 1G to 4G, and the deployment of the …
generations of technological revolution, namely from 1G to 4G, and the deployment of the …