摘要
通过人工智能算法深度挖掘网管数据、用户级数据、经分数据等各数据之间的关系,找到与流量数据相关的向量或参数作为模型训练的特征构建基站预测模型,提前预知基站即将突发的高负荷,提前扩容,提高用户感知,实现基站流量预测,能较好地解决基站当前面临的容量问题。
Through the artificial intelligence algorithm, the relationship between network management data, user-level data,economic analysis data and other data is deeply mined, and the vector or parameter related to the traffic data is found as the characteristics of the model training to build the base station prediction model, which can predict the sudden high load of the base station in advance, expand the capacity in advance, improve the user perception, realize the base station traffic prediction and better solve the current capacity problem of the base station.
作者
徐运武
XU Yun-wu(Guangdong Songshan Polytechnic College,Shaoguan,Guangdong,China 512126)
出处
《湖南邮电职业技术学院学报》
2022年第4期4-8,29,共6页
Journal of Hunan Post and Telecommunication College
基金
2020年广东省教育厅重点领域专项“基于人工智能的网络优化研究与应用”(项目编号:2020ZDZX3113)
2021年广东省教育厅普通高校特色创新项目“新型微雷达在智能停车场中的研究与应用”(项目编号:2021KTSCX225)
2018年韶关市科学技术局项目“无线信号覆盖最优设计研究”(项目编号:2018sn062)。
关键词
人工智能
基站
流量预测
artificial intelligence
base station
traffic prediction
