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