Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

Abstract

SummaryThe 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 on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.