… machine learning-based Network Slicing. Some studies recommended ML approaches for
… Our survey mainly emphasizes Machine Learning-based Network Slicing and new blend-up …

… (QoS) due to fluctuations in the network demand. To address this issue… network slicing and
propose a dynamic resource adjustment algorithm based on reinforcement learning approach …

… approach based on deep reinforcement learning (DRL) to solve the resource allocation
problem in MEC-enabled IoV networks based on network slicing. … allocation, the slice selection, …

… In network slicing, physical infrastructure is sliced into multiple isolated logical networks, with
… iRSS) for RAN slicing, which is embedded in a collaborative learning framework. Both deep …

… reinforcement learning algorithms for network slicing under … network slicing. Specifically, to
deal with cumulative constraints, we propose our adaptive constrained reinforcement learning …

… with the incumbent network slicing solutions, we demonstrate the efficacy of the proposed
learning augmented optimization approach for the targeted network slicing problem. The rest …

… characteristics of network slice, we modified the learning rules including … learning-based
algorithm, “LESS is more”. Importantly, the idea of exploiting traffic similarity of a network slice …

… a framework for RAN slicing by using two machine-learning approaches: (i) Long short-term
memory (LSTM) is used to predict the resources that should be allocated to a slice in a large …

… Section 2 discusses the architecture of Network Slicing in B5G, the current state and …
approach to adaptive learning in the future 6G ecosystem. We discuss our previous network slicing …

… we tries to predict the current network slice that would be classified into streaming, messaging,
searching, and cloud classes. We compared the four deep learning models namely MLP, …