使用神经网络合集方法准确预测路径损失
1Division of Interdisciplinary Studies in Cultural Intelligence, Dongduk Women's University, Seoul 02784, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究引入了一种机器学习方法,用于预测蜂网络路径损失,减少耗时的实地测试. 拟议的神经网络组合准确预测路径损失,优于现有技术.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 路径损失显著影响了蜂基站定位.
- 对于路径丢失的传统现场测量是耗时的.
- 准确的路径丢失预测对于高效的网络部署至关重要.
研究的目的:
- 开发一种基于机器学习的方法,用于准确预测路径丢失.
- 为了减少对基础站安置的广泛现场测试的依赖.
- 提高路径丢失预测模型的性能和准确性.
主要方法:
- 应用神经网络合体学习技术用于路径损失预测.
- 通过选择高性能神经网络进行超参数后优化,构建了一个合奏.
- 通过使用公共数据集对各种机器学习方法进行方法性能评估.
主要成果:
- 拟议的机器学习方法在路径丢失预测方面表现出卓越的性能.
- 与基线方法相比,神经网络组合显著提高了预测准确性.
- 该模型准确地预测了路径损失,验证了其有效性.
结论:
- 拟议的机器学习方法为传统路径损失测量方法提供了高效和准确的替代方案.
- 神经网络合体学习是一种可行的技术,用于增强细胞网络中的路径损失预测.
- 这种方法可以优化基站定位并降低部署成本.
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