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We developed a fast and efficient deep reinforcement learning model to do dynamic network slicing that optimizes the service quality in real-time. Our solution ...
In this paper, we propose a fast-learning DRL model that can dynamically optimize the slice configuration of unplanned Wi-Fi networks without expert knowledge.
Jul 29, 2022 · In this article, we discuss how to design and deploy deep reinforcement learning (DRL), a model-free approach, to address the network slicing problem.
This paper proposes Deep reinforcement learning model for Network Slice Reconfiguration with Dummy and Partial greedy exploration (DNSR-DP), an online learning ...
Abstract—Network slicing, a key enabler for future wire- less networks, divides a physical network into multiple logical networks that can be dynamically ...
Jul 29, 2022 · To serve these use cases cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according to ...
In this paper, we combine the reinforcement learning with communication learning. The agents select its communication objectives based on priority and mask off ...
Feb 13, 2022 · In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services.
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Apr 15, 2022 · This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL ...
We model the strengthening issue of industrial network slicing as a combinatorial optimization problem on graphs and propose an intelligent method based on Deep ...