基于深度强化学习学习的5G低轨道星座网络切片的可靠性映射研究
Yunjie Xiao1, Nan Li2, Jiangtao Yu2
1Information & Communication Company, SMEPC, Jingan, Shanghai, 200072, China. jiegan11487878257@163.com.
Scientific reports
|July 3, 2024
概括
本研究介绍了5G低轨道星座网络切片可靠性映射的深度强化学习模型,解决状态空间爆炸. 该方法提高了网络性能,并确保了对各种网络切片需求的高可靠性.
科学领域:
- 电信工程 电信工程 电信工程
- 网络可靠性 网络可靠性
- 人工智能在网络中的作用
背景情况:
- 在5G低轨道星座网络中确保可靠的通信对于网络切片性能至关重要.
- 状态空间爆炸问题阻碍了这些复杂网络中的高效可靠性映射.
- 现有的方法难以满足严格的虚拟网络功能 (VNF) 和链接可靠性要求.
研究的目的:
- 为5G低轨道星座网络切片开发可靠性映射模型.
- 通过深度强化学习来解决州空间爆炸问题.
- 为了提高网络切片在低轨道星座的整体可靠性和性能.
主要方法:
- 在软件定义网络 (SDN) 和网络功能虚拟化 (NFV) 集成架构中实施了深度强化学习方法.
- 开发了一个可靠性映射模型,考虑了VNF资源要求和约束.
- 采用基于重要性的节点和链路备份策略,以提高VNF/链路可靠性.
主要成果:
- 拟议的方法显著提高了网络吞吐量,降低了数据包丢失率,并增强了切片内部流量.
- 网络故障在0.3秒内完全修复.
- 对于不同数量的网络切片请求,可靠性保持在98%以上,不同服务功能链 (SFC) 长度的平均网络延迟低于0.15秒.
结论:
- 深度强化学习有效地解决了5G低轨道星座网络切片可靠性映射中的状态空间爆炸问题.
- 综合方法提高了网络性能指标和故障修复能力.
- 该模型展示了强大的可靠性和低延迟,适合要求低轨道星座网络环境.
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