作者
Mohsen Khani, Shahram Jamali, Mohammad Karim Sohrabi, Mohammad Mohsen Sadr, Ali Ghaffari
来源
International Journal of Communication Systems
页码范围
e5857
简介
The emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing …
学术搜索中的文章
Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey
M Khani, S Jamali, MK Sohrabi, MM Sadr, A Ghaffari - International Journal of Communication Systems