用AI增强的路由和切片策略,用于QoS意识的移动特设网络
Venkatesan C1, Shaha Al-Otaibi2, Balaji Vijayan V3
1Department of Electronics and Communication Engineering, HKBK College of Engineering, Opposite Manyata Tech Park, Nagawara, Bengaluru, 560045. Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India. venkatesanc.ec@hkbk.edu.in.
Scientific reports
|January 28, 2026
概括
本研究介绍了使用深度强化学习和网络切片用于移动特设网络 (MANET) 的AI框架. 它显著减少了延迟,并提高了吞吐量和数据包交付,在动态环境中提高了服务质量 (QoS).
科学领域:
- 计算机科学 计算机科学
- 电信工程 电信工程 电信工程
- 人工智能的人工智能
背景情况:
- 移动特设网络 (MANET) 面临着由于节点移动性,干扰和流量变化而维持服务质量 (QoS) 的挑战.
- 现有的路由协议难以适应MANET的动态性质,导致性能下降.
研究的目的:
- 为MANETs提出一个人工智能增强的路由和网络切片框架.
- 为了改善数据包交付,减少延迟,并在动态MANET环境中提高吞吐量.
主要方法:
- 结合深度增强学习 (DRL) 进行智能路由与自适应网络切片 (NS).
- 使用经过近接政策优化训练的DRL代理来根据网络条件选择最佳的下一个跳跃.
- 实现基于模糊逻辑的网络切片器,在网络切片之间实时重新分配带宽.
主要成果:
- 与传统协议 (AODV,DSR) 和独立的DRL路由器相比,拟议的框架将平均延迟减少37%,并将吞吐量提高1.8倍.
- 在高达20m/s的节点速度下,数据包交付比率提高了22%.
- 该计划保持了能源效率和低控制开支.
结论:
- 整合智能路由 (DRL) 与敏捷网络切片 (NS) 是在高度动态的MANET中维持应用级QoS的有效策略.
- 人工智能增强的框架为克服当前MANET的性能限制提供了可行的解决方案.
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