LSTM-based throughput prediction for LTE networks
Hyeonjun Na, Yongjoo Shin, Dongwon Lee, Joohyun Lee
Abstract
Throughput prediction is crucial for reducing latency in time-critical services. We study the attention-based LSTM model for predicting future throughput. First, we collected the TCP logs and throughputs in LTE networks and transformed them using CUBIC and BBR trace log data. Then, we use the sliding window method to create input data for the prediction model. Finally, we trained the LSTM model with an attention mechanism. In the experiment, the proposed method shows lower normalized RMSEs than the other method.
Topics & Concepts
ThroughputComputer scienceLatency (audio)Sliding window protocolTRACE (psycholinguistics)Data miningReal-time computingArtificial intelligenceAlgorithmWindow (computing)WirelessTelecommunicationsLinguisticsOperating systemPhilosophyNetwork Traffic and Congestion ControlImage and Video Quality AssessmentWireless Networks and Protocols