基于神经网络的适应性资源分配,用于5G异质超密度网络
Alanoud Salah Alhazmi1,2, Mohammed Amer Arafah1
1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了5G异质超密度网络 (HUDNs) 的神经网络适应性资源分配 (NN-ARA) 框架. 通过动态管理资源,NN-ARA提高了频谱效率和数据速率,在高流量负载下改善了服务连续性.
科学领域:
- 电信工程 电信工程 电信工程
- 无线网络优化无线网络优化
- 网络中的人工智能
背景情况:
- 5G网络面临着挑战,尽管频谱带宽更广,但流量需求不断增加.
- 异质超密度网络 (HUDNs) 对于流量卸载至关重要,但由于不同的基站和服务质量 (QoS) 需求,它们存在复杂的资源分配挑战.
- 静态资源分配方法不灵活,导致在复杂的5G环境中使用频谱效率低下.
研究的目的:
- 为5G HUDNs开发一个联合的用户协会-资源分配 (UA-RA) 框架.
- 动态调整资源分配以适应实时网络条件,以提高频谱效率和在高流量负载下服务比率.
- 通过由软件定义网络 (SDN) 控制器管理的细胞间资源再分配来缓解拥堵.
主要方法:
- 为5G HUDNs开发了一个联合的用户协会-资源分配 (UA-RA) 框架.
- 软件定义网络 (SDN) 控制器被用于中央集中的UA-RA和细胞间资源再分配.
- 在模拟数据上训练了一个神经网络适应性资源分配 (NN-ARA) 模型,以低计算成本近似有效的分配决策.
主要成果:
- 与基线方法相比,NN-ARA方法显示了显著的改善.
- 在宏观和小细胞层分别实现了高达20.8%和11%的下链数据速率.
- 提高了分别约20.7%和11.1%的光谱效率,并将平均阻塞比率降低了多达55%.
结论:
- 该NN-ARA框架提供了一个适应性,可扩展性和SDN协调的解决方案,用于5G和未来6G HUDNs的高效频谱利用.
- 拟议的模型有效地解决了复杂网络环境中动态资源配置的挑战.
- 在高流量负载下,NN-ARA确保了更好的服务连续性和网络性能.
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