LSTM-based throughput prediction for LTE networksopen access
- Authors
- Na, Hyeonjun; Shin, Yongjoo; Lee, Dongwon; Lee, Joohyun
- Issue Date
- Apr-2023
- Publisher
- 한국통신학회
- Keywords
- Machine learning; Deep learning; Throughput prediction; LSTM; Attention method
- Citation
- ICT Express, v.9, no.2, pp.247 - 252
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ICT Express
- Volume
- 9
- Number
- 2
- Start Page
- 247
- End Page
- 252
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188307
- DOI
- 10.1016/j.icte.2021.12.001
- 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.(c) 2021 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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