A Hybrid CNN-LSTM Architecture for High Accurate Edge-Assisted Bandwidth Prediction

Hanfei Wen, Jun Yu, Guangjin Pan, Xiaojing Chen, Shunqing Zhang*, Shugong Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In this letter, we propose an edge-assisted bandwidth prediction scheme based on a hybird convolutional neural network (CNN) and long short-term memory (LSTM) architecture. To accurately predict available downlink bandwidth with limited and regulated monitoring information, we decouple feature extraction and sequence learning to capture the feature vector by CNN and resolve the historical feature collection issue by LSTM, respectively. A two-stage training strategy is further adopted to improve the prediction accuracy and generalization ability. In addition, we deploy and evaluate the proposed bandwidth prediction scheme in our prototype system. The test results show that our proposed scheme improves the mean absolute error (MAE) and root mean square error (RMSE) by 81.4% and 82.0%, respectively, compared with the LSTM only prediction scheme, and consumes less than 3 ms running time, which is sufficient for many real-time video transmission applications.

Original languageEnglish
Pages (from-to)2640-2644
Number of pages5
JournalIEEE Wireless Communications Letters
Volume11
Issue number12
DOIs
Publication statusPublished - 1 Dec 2022
Externally publishedYes

Keywords

  • bandwidth prediction
  • CNN
  • LSTM
  • Mobile edge computing
  • RAN monitoring information

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