CLPREM:用于5G移动网络的实时流量预测方法.
1National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
PloS one
|April 1, 2024
概括
本研究介绍了一种基于集群的轻量级预测模型 (CLPREM),用于准确的5G网络流量预测. CLPREM提高了实时预测的准确性和效率,解决了传统方法的局限性.
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:
- 传统的网络流量预测方法对于大型网络和5G是不够的.
- 准确的实时流量预测对于网络资源优化和异常检测至关重要.
研究的目的:
- 提出一种用于5G移动网络实时流量预测的新方法.
- 提高网络流量预测模型的稳定性和准确性.
主要方法:
- 基于集群的轻量级预测模型 (CLPREM) 的开发.
- 长短期内存 (LSTM) 网络的集成,数据增强,集群和模型压缩.
- 实施独特的数据处理和分类技术,以提高可靠性.
主要成果:
- 与传统的预测方案相比,CLPREM的准确性更高.
- 拟议的模型实现了实时交通预测的较低时间成本.
- 新增的预处理方法进一步提高了CLPREM的准确性和异常预测能力.
结论:
- CLPREM有效地解决了5G网络中实时流量预测的挑战.
- 该模型为网络监控和管理提供了强大,准确和高效的解决方案.
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