Learning Physical-Layer Communication With Quantized Feedback

@article{Song2019LearningPC,
  title={Learning Physical-Layer Communication With Quantized Feedback},
  author={Jinxiang Song and Bile Peng and Christian H{\"a}ger and Henk Wymeersch and Anant Sahai},
  journal={IEEE Transactions on Communications},
  year={2019},
  volume={68},
  pages={645-653}
}
  • Jinxiang Song, Bile Peng, +2 authors Anant Sahai
  • Published in
    IEEE Transactions on…
    2019
  • Computer Science, Engineering, Mathematics
  • Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 37 REFERENCES

    Deep Reinforcement Learning Autoencoder with Noisy Feedback

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    End-to-End Learning of Communications Systems Without a Channel Model

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

    VIEW 1 EXCERPT

    Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism

    Model-Free Training of End-to-End Communication Systems

    Towards Hardware Implementation of Neural Network-based Communication Algorithms

    • Fayçal Ait Aoudia, Jakob Hoydis
    • Computer Science, Mathematics
    • 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
    • 2019
    VIEW 1 EXCERPT

    Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

    VIEW 2 EXCERPTS