.png){kind=icon}
返回
Constrained Reinforcement Learning for Resource Allocation in Network Slicing
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2021.3053612.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In network slicing, dynamic resource allocation is the key to network performance optimization. Deep reinforcement learning (DRL) is a promising method to exploit the dynamic features of network slicing by interacting with the environment. However, the existing DRL-based resource allocation solutions can only handle a discrete action space. In this letter, we tackle a general DRL-based resource allocation problem which considers a mixed action space including both discrete channel allocation and continuous energy harvesting time division, with the constraints of energy consumption and queue package length. We propose a novel DRL algorithm referred to as constrained discrete-continuous soft actor-critic (CDC-SAC) by redesigning the network architecture and policy learning process. Simulation results show that the proposed algorithm can achieve a significant performance improvement in terms of the total throughput with the strict constraints guarantee.
Keyword:
Resource management
Network slicing
Batteries
Throughput
Quality of service
Energy harvesting
Australia
Network slicing
dynamic resource allocation
deep reinforcement learning
soft actor-critic
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
The Components of Young Children's Emotion Knowledge: Which Are Enhanced by Adult Emotion Talk?
PREAI
分享收藏
Dynamic Network Slicing and Resource Allocation in Mobile Edge Computing Systems移动边缘计算系统中的动态网络切片和资源分配
PREAI
分享收藏Autonomous Cache Resource Slicing and Content Placement at Virtualized Mobile Edge Network.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏
查看更多内容