… (5G) communication system, network slicing can share the underlying … network slicing
resource allocation algorithm based on Deep Q-Networks (DQN), which is suitable for multi-slice …

… Deep Q-Learning (DQL) based network slicing framework to dynamically reconfigure the scale
of radio and computing resources of a slice … a lowcomplexity algorithm to optimize the real-…

… to apply DRL in network slicing from a general perspective. … -training for the priority-based
network slicing policy takes two days in an Intel Core i7-4712MQ processor to converge the Q…

… This paper presents an online Deep Q-learning based network slicing technique that …
allocation and slice selection to serve the network users. The Next Generation Mobile Network (…

… Slicing approach, to provide slice’s member with the requested channel resources, by … a
network slicing architecture based on SDN and NFV for IIoT 4.0 to meet multitude slice services …

… network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms
… We analyze the approaches according to the optimization objective, the network focus (…

… To evaluate the performance of the proposed deep dueling network, we will compare its
performance with other deep reinforcement learning algorithms, ie, deep Q-learning [15] and …

… , we first develop a Q-learning algorithm (Q-EFNS). Next, to … Deep QEFNS (DQ-EFNS) and
Deep Dueling Q-EFNS (Dueling DQEFNS) algorithms for dynamic edge/fog network slicing …

… We present a fast-learning DRL model that can dynamically optimize the network slice
configuration in Wi-Fi networks. Our network slices require each a different network configuration. …

… a novel network slicing approach with an advanced deep learning architecture, called deep
… As aforementioned, in this work, we propose reinforcement learning approaches which can …