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Apply Deep Reinforcement Learning aided by Federated Learning to Wireless Comunication

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Mauriyin/FLDRL-in-Wireless-Communication

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FLDRL-in-Wireless-Communication

  • Simulation code for Paper:
    Lyutianyang Zhang1 , Hao Yin1, Zhanke Zhou, Sumit Roy, Yaping Sun, Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning, VTC2020-Fall.
    1 Both authors contribute equally to this work.
  • Cite our work:
@INPROCEEDINGS{FrmaVTC2020,
  author={L. {Zhang} and H. {Yin} and Z. {Zhou} and S. {Roy} and Y. {Sun}},
  booktitle={IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)}, 
  title={Enhancing {WiFi} Multiple Access Performance with Federated Deep Reinforcement Learning}
  }

Contributors: Hao Yin, Zhanke Zhou

The paper can be found https://ieeexplore.ieee.org/document/9348485

Simulations

Author Notes:

  • Please check config.py for model loading and saving setups.

    • self.saveModel = False
      self.loadModel = True
      
  • Run python3 test_CSMA_DQN_withModelAllocation.py to proceed training.

  • Throughput is about 5.2-5.4

Training log

Number of Station Max Avg Throughput Total training epoch
5 5.45 10w
10 5.46 13w
20 5.28 22w

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Apply Deep Reinforcement Learning aided by Federated Learning to Wireless Comunication

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