• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Physical Communication >Machine learning methods for SIR prediction in cellular networks
【24h】

Machine learning methods for SIR prediction in cellular networks

机译：蜂窝网络中SIR预测的机器学习方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurate assessment of the wireless coverage of a station is considered a key feature in 5G networks. Determining the reception coverage of transmitters becomes a complicated problem when there are interfering transmitters, and it becomes increasingly more complicated when the transmission powers of those transmitters are not uniform. In this paper, we compare different Machine Learning techniques that can be used to predict the wireless coverage maps. We consider the following Machine Learning methods: (1) Radial Basis Network; a type of Artificial Neural Network which typically uses Gaussian kernels, (2) an Artificial Neural Network which uses a sigmoid function as an activator,(3) A Multi-Layer Perceptron with two hidden layers, and (4) the K-Nearest-Neighbors technique.We show how it is possible to train the Neural Networks to generate coverage maps based on samples and we check the accuracy level of the learning process on a test set, using these four different learning techniques. The conclusion of our experiments is that if the sample points are randomly located, the Radial Basis Network and the Multi-Layer Perceptron perform better than the other methods.Thus, these models can be considered promising candidates for learning coverage maps, and can be used for efficient spectrum management within the framework of 5G cellular networks. (C) 2018 Elsevier B.V. All rights reserved.
机译：准确评估站点的无线覆盖范围被认为是5G网络的关键功能。当存在干扰的发射机时，确定发射机的接收覆盖范围成为一个复杂的问题，而当那些发射机的发射功率不均匀时，确定发射机的接收范围变得越来越复杂。在本文中，我们比较了可用于预测无线覆盖图的不同机器学习技术。我们考虑以下机器学习方法：（1）径向基网络；一种通常使用高斯核的人工神经网络；（2）使用S形函数作为激活器的人工神经网络；（3）具有两个隐藏层的多层感知器；以及（4）K-Nearest-邻居技术：我们展示了如何训练神经网络以基于样本生成覆盖图，并使用这四种不同的学习技术在测试集上检查学习过程的准确性水平。我们的实验结论是，如果将样本点随机放置，则径向基网络和多层感知器的性能要优于其他方法，因此，这些模型可以被认为是学习覆盖图的有希望的候选者，并且可以被使用。在5G蜂窝网络框架内进行有效的频谱管理。 （C）2018 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号