Intelligent Handover Management in 5G Mobile Networks based on Recurrent Neural Networks
Intelligent Handover Management in 5G Mobile Networks based on Recurrent Neural Networks
复制标题
基于递归神经网络的 5G 移动网络智能切换管理
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Maksymyuk
中科院分区：
文献类型：
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作者：
B. Shubyn;T. Maksymyuk
在现代移动网络中，我们看到用户对流量的使用出现了巨大的飞跃，因此确保网络的正常运行变得越来越困难。在本文中，我们建议使用一种智能的方法来进行网络管理，即切换管理。其主要思想是使用神经网络，它基于用户移动性的知识，可以预测在小区之间移动一组用户的方式，从而提供实现切换的最大效率。从结果可以看出，神经网络预测流量的准确率可以达到90%以上，如果使用合适的设备，探索更持久的流量统计，那么这个值可以显著提高。
In modern mobile networks, we are seeing a huge leap in the uses of traffic by subscribers, so it is becoming increasingly difficult to ensure the proper operation of the network. In this paper, we propose to use an intelligent approach to network management, namely handover management. The main idea is to use neural networks, which, based on knowledge of user mobility, can predict ways of moving a group of subscribers between cells, which provide the maximum effectiveness of the implementation of the handover. From the results, we see that the neural network can predict traffic with an accuracy of more than 90%, and if you use the proper equipment and explore more durable traffic statistics, then this value can be significantly increased.