基于神经网络的路径损失建模和未来无线网络的集体方法.
Mohamed K Elmezughi1, Omran Salih2, Thomas J Afullo1
1The Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa.
Heliyon
|October 9, 2023
概括
一个整体机器学习模型为高频无线网络提供了卓越的路径损失预测准确性. 这种先进的模型在复杂的环境中确保了更好的服务质量.
科学领域:
- 无线通信无线通信
- 机器学习 机器学习
- 电磁学 电磁学 电磁学 电磁学
背景情况:
- 技术进步需要更高的数据速度,推动对更高频段 (毫米波和亚特拉赫兹) 的需求.
- 现有的5G和以后的路径损失预测模型缺乏适用于具有挑战性的环境所需的灵活性和准确性.
- 准确的路径损失预测对于部署有保证服务质量的无线网络至关重要.
研究的目的:
- 开发和评估基于先进的机器学习的高频频段路径损失预测模型.
- 为了比较人工神经网络 (ANN),循环神经网络-长期短期记忆 (RNN-LSTM),卷积神经网络 (CNN) 和基于集合方法的模型的性能.
- 在复杂的室内环境中确定最有效和最准确的路径损失预测模型.
主要方法:
- 实现ANN,RNN-LSTM和CNN模型用于路径损失预测.
- 开发一种基于集合方法的新型神经网络路径损失模型.
- 基于预测准确度,稳定性,功能贡献和计算时间的性能分析.
- 使用室内走廊测量活动 (视线和非视线) 的数据进行培训和测试.
主要成果:
- 与单个ANN,RNN-LSTM和CNN模型相比,基于整体方法的模型显示出更高的预测准确性和效率.
- 该研究提供了所有四种模型的广泛性能分析.
- 拟议的整体模型在复杂的环境中显示出高的预测准确性和效率.
结论:
- 基于整体方法的路径损失预测模型是高频无线通信的一个有希望的解决方案.
- 该模型提供了更高的准确性和效率,对于优化无线网络部署至关重要.
- 这些发现支持使用先进的机器学习技术在具有挑战性的场景中可靠地预测路径丢失.
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