Zero touch management: A survey of network automation solutions for 5G and 6G networks

E Coronado, R Behravesh… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Mobile networks are facing an unprecedented demand for high-speed connectivity
originating from novel mobile applications and services and, in general, from the adoption …

Deep reinforcement learning for resource management on network slicing: A survey

JA Hurtado Sánchez, K Casilimas… - Sensors, 2022 - mdpi.com
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …

Deep federated Q-learning-based network slicing for industrial IoT

S Messaoud, A Bradai, OB Ahmed… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Fifth generation and beyond networks are envisioned to support multi industrial Internet of
Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network …

[HTML][HTML] Machine learning-based zero-touch network and service management: A survey

J Gallego-Madrid, R Sanchez-Iborra, PM Ruiz… - Digital Communications …, 2022 - Elsevier
The exponential growth of mobile applications and services during the last years has
challenged the existing network infrastructures. Consequently, the arrival of multiple …

Energy-efficient UAV-enabled data collection via wireless charging: A reinforcement learning approach

S Fu, Y Tang, Y Wu, N Zhang, H Gu… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
In this article, we study the application of unmanned aerial vehicle (UAV) for data collection
with wireless charging, which is crucial for providing seamless coverage and improving …

[HTML][HTML] A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization

C Ssengonzi, OP Kogeda, TO Olwal - Array, 2022 - Elsevier
Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks

J Rischke, P Sossalla, H Salah, FHP Fitzek… - IEEE …, 2020 - ieeexplore.ieee.org
Flow routing can achieve fine-grained network performance optimizations by routing distinct
packet traffic flows over different network paths. While the centralized control of Software …

A survey on XAI for 5G and beyond security: Technical aspects, challenges and research directions

T Senevirathna, VH La, S Marchal… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
With the advent of 5G commercialization, the need for more reliable, faster, and intelligent
telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio …

Slicing the core network and radio access network domains through intent-based networking for 5G networks

K Abbas, M Afaq, T Ahmed Khan, A Rafiq, WC Song - Electronics, 2020 - mdpi.com
The fifth-generation mobile network presents a wide range of services which have different
requirements in terms of performance, bandwidth, reliability, and latency. The legacy …

Coordinated slicing and admission control using multi-agent deep reinforcement learning

M Sulaiman, A Moayyedi, M Ahmadi… - … on Network and …, 2022 - ieeexplore.ieee.org
5G Cloud Radio Access Networks (C-RANs) facilitate new forms of flexible resource
management as dynamic RAN function splitting and placement. Virtualized RAN functions …