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End-to-End (E2E) learning-based concept has been recently introduced to jointly optimize both the transmitter and the receiver in wireless communication systems.
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S. Gu, T. Lillicrap, I. Sutskever, and S. Levine, “Continuous deep Q-learning with model-based acceleration,” International conference on machine learning , 2016
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T. O’shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking , vol. 3, no. 4, pp. 563-575, Oct. 2017
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S. Dorner, S. Cammerer, J. Hoydis, and S. T. Brink, “Deep learning based communication over the air,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, no. 1, pp. 132-143, Feb. 2018
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Cited alongside, same era.
F. Aoudia and J. Hoydis, “End-to-end learning of communications systems without a channel model,” 52nd Asilomar Conference on Signals, Systems, and Computers , Pacific Grove, CA, USA, 28-31 Oct. 2018
2018
Cited alongside, same era.
V. Raj and S. Kalyani, “Backpropagating through the air: Deep learning at physical layer without channel models,” IEEE Communications Letters , vol. 22, no. 11, pp. 2278-2281, Nov. 2018
2018
Cited alongside, same era.
Cited in the paper.
Cited in the paper.
Cited in the paper.
M. Goutay, F. A. Aoudia, and J. Hoydis, “Deep reinforcement learning autoencoder with noisy feedback,” International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT) , Avignon, France, 03-07 Jun. 2019
2019
Later among the works it cites.
O. Jovanovic, M. P. Yankov, F. D. Ros, and D. Zibar, “Gradient-free training of autoencoders for non-differentiable communication channels,” Journal of Lightwave Technology , vol. 39, no. 20, pp. 6381-6391, Aug. 2021
2021
Later among the works it cites.
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