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 …
In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks
J Gong, A Rezaeipanah - Multimedia Tools and Applications, 2023 - Springer
Video conferencing is one of the advanced technologies for users that allows online
communication despite long distances. High quality communication and ongoing support for …
communication despite long distances. High quality communication and ongoing support for …
DRL-M4MR: An intelligent multicast routing approach based on DQN deep reinforcement learning in SDN
C Zhao, M Ye, X Xue, J Lv, Q Jiang, Y Wang - Physical Communication, 2022 - Elsevier
Traditional multicast routing methods have some problems in constructing a multicast tree.
These problems include limited access to network state information, poor adaptability to …
These problems include limited access to network state information, poor adaptability to …
Intelligent routing method based on Dueling DQN reinforcement learning and network traffic state prediction in SDN
L Huang, M Ye, X Xue, Y Wang, H Qiu, X Deng - Wireless Networks, 2022 - Springer
The traditional routing method makes use of limited information on the network links to make
routing decisions, which makes it difficult to adapt to the dynamic and complex network and …
routing decisions, which makes it difficult to adapt to the dynamic and complex network and …
GRL-PS: Graph embedding-based DRL approach for adaptive path selection
W Wei, L Fu, H Gu, Y Zhang, T Zou… - … on Network and …, 2023 - ieeexplore.ieee.org
Forwarding path selection for data traffic is one of the most fundamental operations in
computer networks, whose performance drastically impacts both transmission efficiency and …
computer networks, whose performance drastically impacts both transmission efficiency and …
Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
Abstract Software Defined Network (SDN) has been used in many organizations due to its
efficiency in transmission. Machine learning techniques have been applied in SDN to …
efficiency in transmission. Machine learning techniques have been applied in SDN to …
A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches
This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
From classical to quantum machine learning: Survey on routing optimization in 6G software defined networking
O Bouchmal, B Cimoli, R Stabile… - Frontiers in …, 2023 - frontiersin.org
The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to
fulfill simultaneously stringent key performance indicators and overall optimization of usage …
fulfill simultaneously stringent key performance indicators and overall optimization of usage …
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN
H Hu, M Ye, C Zhao, Q Jiang, Y Wang, H Qiu… - arXiv preprint arXiv …, 2023 - arxiv.org
Multicast communication technology is widely applied in wireless environments with a high
device density. Traditional wireless network architectures have difficulty flexibly obtaining …
device density. Traditional wireless network architectures have difficulty flexibly obtaining …