Research on throughput prediction of 5G network based on LSTM  

作　　者：Lanlan Li Tao Ye 

机构地区：[1]Purple Mountain Labs,Nanjing 210000,China [2]China Communications Construction Second Harbor Engineering Company Ltd.,Wuhan 430040,China

出　　处：《Intelligent and Converged Networks》2022年第2期217-227,共11页智能与融合网络（英文）

摘　　要：This paper proposes a wireless network traffic prediction model based on long-term and short-term memory cyclic neural networks.Through simulation experiments,the throughput prediction of 5G wireless networks using different scheduling algorithms for many different types of services is studied.The results verify that the long short-term memory prediction model has acceptable prediction accuracy and algorithm training speed,meets the needs of wireless network traffic prediction,and has a good application prospect.

关 键 词：wireless network flow forecast long short-term memory(LSTM) SCHEDULE THROUGHPUT 

分 类 号：TN929.5[电子电信—通信与信息系统]