TY - JOUR
T1 - A Hybrid CNN-LSTM Architecture for High Accurate Edge-Assisted Bandwidth Prediction
AU - Wen, Hanfei
AU - Yu, Jun
AU - Pan, Guangjin
AU - Chen, Xiaojing
AU - Zhang, Shunqing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - bandwidth prediction
KW - CNN
KW - LSTM
KW - Mobile edge computing
KW - RAN monitoring information
UR - http://www.scopus.com/inward/record.url?scp=85139840969&partnerID=8YFLogxK
U2 - 10.1109/LWC.2022.3213017
DO - 10.1109/LWC.2022.3213017
M3 - Article
AN - SCOPUS:85139840969
SN - 2162-2337
VL - 11
SP - 2640
EP - 2644
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 12
ER -