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Zero touch management: A survey of network automation solutions for 5G and 6G networks
Mobile networks are facing an unprecedented demand for high-speed connectivity
originating from novel mobile applications and services and, in general, from the adoption …
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 …
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
[HTML][HTML] Machine learning-based zero-touch network and service management: A survey
The exponential growth of mobile applications and services during the last years has
challenged the existing network infrastructures. Consequently, the arrival of multiple …
challenged the existing network infrastructures. Consequently, the arrival of multiple …
Energy-efficient UAV-enabled data collection via wireless charging: A reinforcement learning approach
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 …
with wireless charging, which is crucial for providing seamless coverage and improving …
Deep federated Q-learning-based network slicing for industrial IoT
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 …
Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network …
[HTML][HTML] A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization
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 …
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
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 …
packet traffic flows over different network paths. While the centralized control of Software …
Survey on machine learning-enabled network slicing: Covering the entire life cycle
Network slicing (NS) is becoming an essential element of service management and
orchestration in communication networks, starting from mobile cellular networks and …
orchestration in communication networks, starting from mobile cellular networks and …
Beyond the edge: An advanced exploration of reinforcement learning for mobile edge computing, its applications, and future research trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …
central network, incorporating edge nodes close to end devices. This expansion facilitates …
A survey on XAI for 5G and beyond security: Technical aspects, challenges and research directions
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 …
telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio …