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ALSTM: An Attention-based LSTM Model for Multi-Scenario Bandwidth Prediction

  • Published:2021-12 Issue: Volume: Page:
  • ISSN:
  • Container-title:2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)
  • language:
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Author:

Li Penghui1,Jiang Xianliang1,Jin Guang1,Yu Yan1,Xie Zhijun1

Affiliation:

1. Ningbo University,Faculty of Electrical Engineering and Computer Science,Ningbo,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Ningbo Natural Science Foundation

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning;2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON);2023-09-11

2. CapRadar: Real-time adaptive bandwidth prediction for dynamic wireless networks;Computer Networks;2023-09

3. Bi‐LSTM model with time distribution for bandwidth prediction in mobile networks;ETRI Journal;2023-08

4. Learning-Based Congestion Control Assisted by Recurrent Neural Networks for Real-Time Communication;2023 IEEE Symposium on Computers and Communications (ISCC);2023-07-09

5. Combining Random Forest and Linear Regression to Improve Network Traffic Prediction;2023 23rd International Conference on Transparent Optical Networks (ICTON);2023-07-02
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