Research on throughput prediction of 5G network based on LSTM

Abstract

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.

Keywords

• wireless network
• flow forecast
• long short-term memory (lstm)
• schedule
• throughput