Research on throughput prediction of 5G network based on LSTM
Lanlan Li, Ye Tao
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.
Topics & Concepts
Computer scienceWireless networkThroughputArtificial neural networkLong short term memoryTerm (time)Scheduling (production processes)WirelessArtificial intelligenceMachine learningComputer networkRecurrent neural networkReal-time computingEngineeringTelecommunicationsOperations managementPhysicsQuantum mechanicsAdvanced Data and IoT TechnologiesTraffic Prediction and Management TechniquesAdvanced Computing and Algorithms