… control over slice acceptance in continuous environments, we propose an approximate Q-learning
method that addresses the weaknesses of the Q algorithm. This method achieves fast …

… iv) the cost of implementing a new network slice to maintain the QoS of the user handover.
[… table, the authors used the multiagent Q-learning approach. In [17], the authors investigate a …

… computing resource allocation in multi-access edge network slicing (NS) in the context of …
of slice resource utilization levels across slice tenants (ie, Mobile Virtual Network Operators (…

… learning problem. Leveraging reinforcement learning techniques, we train a network slicing
policy function that we parametrize via a neural network, and that is a function of both, the …

… approach to network slicing that advances a fundamental rethinking of the mobile network
to shift its architectural vision from a network of … , machine learning, and autonomic network …

… with an actual characterization of the customers perspective, and the exploitation of a
powerful learning tool such as FL to improve accuracy in the placement procedure. …

… We consider a network slicing system which consists of a single base station (BS) and N
mobile users with a single antenna and rechargeable battery for energy harvesting. The N …

… for multi-domain experimentation network infrastructures. The … for network slicing and a
practical realization of the Slice-as-a-Service (SlaaS) paradigm, with intelligent end-to-end slice …

… wireless communication systems. To fully utilize the advantages of GNN, DT, and RL in
network slicing, we propose a DT network … learning approach for beyond 5g network slicing,” in …

… learning (DRL) method. We present a novel Actor-Critic-based network slicing approach
called, … a zero-touch network slicing scheme with a multi-objective approach where the central …