关于DNN在场景变化下对电源控制在无细胞大规模MIMO中的稳定性的研究
Guillermo García-Barrios1, Manuel Fuentes1, David Martín-Sacristán2
15G Communications for Future Industry Verticals S.L. (Fivecomm), Camí de Vera s/n (6D Building), 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
对于6G无电池大规模MIMO功率控制的机器学习模型显示出强大的稳定性. 一个低复杂度的深度神经网络 (DNN) 在各种网络条件下保持了性能,支持实际部署.
科学领域:
- 无线通信网络是无线通信网络.
- 机器学习应用程序 机器学习应用程序
- 信号处理 信号处理
背景情况:
- 6G无线网络需要先进的解决方案,如无细胞大规模MIMO和机器学习 (ML).
- 机器学习模型的通用性对于实际部署至关重要,但通常需要复杂的模型和大型数据集.
- 这项研究解决了在6G系统中需要强大,低复杂度的ML模型的需求.
研究的目的:
- 分析低复杂度深度神经网络 (DNN) 的稳定性,用于无细胞大规模MIMO系统的功率控制.
- 评估DNN在各种网络配置和传播环境中的性能.
- 为基于机器学习的功率控制研究提供可重复的框架.
主要方法:
- 训练了一个低复杂度的DNN来模拟三个功率控制方案:最大-最小光谱效率 (SE) 公平性,SE总量最大化和分数功率控制.
- 通过测试不同数量的接入点,用户设备和传播条件的未见的场景来评估模型的稳定性.
- 使用Kolmogorov-Smirnov测试对累积分布函数进行量化比较的性能指标.
主要成果:
- 在不同的未见网络场景中,DNN表现出强大的稳定性.
- 总量SE最大化功率控制方案表现特别强的性能.
- 统计测试 (科尔莫戈罗夫-斯米尔诺夫) 证实了稳定性,D统计<0.05和p值>0.001.
结论:
- 低复杂度的DNN可以在6G的无细胞大规模MIMO系统中实现强大的功率控制.
- 这些发现支持基于机器学习的电源控制在未来的无线网络中的实际可行性.
- 为进一步研究提供了可重复的数据集和框架.
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