Learning to Detect

@article{Samuel2019LearningTD,
  title={Learning to Detect},
  author={Neev Samuel and Tzvi Diskin and A. Wiesel},
  journal={IEEE Transactions on Signal Processing},
  year={2019},
  volume={67},
  pages={2554-2564}
}
  • Neev Samuel, Tzvi Diskin, A. Wiesel
  • Published 2019
  • Computer Science, Mathematics
  • IEEE Transactions on Signal Processing
  • In this paper, we consider multiple-input-multiple-output detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the proposed approaches and achieve state-of-the-art… CONTINUE READING
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    References

    SHOWING 1-10 OF 42 REFERENCES
    Going deeper with convolutions
    • 22,341
    • PDF
    An Introduction to Deep Learning for the Physical Layer
    • T. O'Shea, J. Hoydis
    • Computer Science, Mathematics
    • IEEE Transactions on Cognitive Communications and Networking
    • 2017
    • 873
    • PDF
    Neural Network Detection of Data Sequences in Communication Systems
    • 130
    • PDF
    Deep robust regression
    • 2
    Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
    • 220
    • PDF
    Deep MIMO detection
    • Neev Samuel, Tzvi Diskin, A. Wiesel
    • Computer Science, Mathematics
    • 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
    • 2017
    • 224
    • PDF
    An Introduction to Machine Learning Communications Systems
    • 72
    • PDF
    Deep Residual Learning for Image Recognition
    • 60,095
    • PDF
    Learning to invert: Signal recovery via Deep Convolutional Networks
    • A. Mousavi, Richard Baraniuk
    • Computer Science, Mathematics
    • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    • 2017
    • 164
    • PDF
    Learning representations by back-propagating errors
    • 17,007
    • PDF