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When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder.
2014
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T. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Trans. Cognitive Commun. and Netw. , vol. 3, no. 4, pp. 563–575, 2017
2017
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S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Deep learning based communication over the air,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, no. 1, pp. 132–143, 2017
2017
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. 34th International Conference on Machine Learning (ICML) , 2017, pp. 1126–1135
2017
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O. Simeone, “A very brief introduction to machine learning with applications to communication systems,” IEEE Trans. Cognitive Commun. and Netw. , vol. 4, no. 4, pp. 648–664, 2018
2018
Cited alongside, same era.
——, “A brief introduction to machine learning for engineers,” Foundations and Trends® in Signal Processing , vol. 12, no. 3-4, pp. 200–431, 2018
2018
Cited alongside, same era.
E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Transactions on Cognitive Communications and Networking , 2019
2019
Cited alongside, same era.
F. A. Aoudia and J. Hoydis, “Model-free training of end-to-end communication systems,” IEEE Journal on Selected Areas in Commun. , vol. 37, no. 11, pp. 2503–2516, 2019
2019
Cited alongside, same era.
2019
Closest in time.
Y. Jiang, H. Kim, H. Asnani, and S. Kannan, “Mind: Model independent neural decoder,” in Proc. IEEE Signal Processing Advances in Wireless Commun. (SPAWC) , Cannes, France, July 2019
2019
Closest in time.
H. Mao, H. Lu, Y. Lu, and D. Zhu, “Roemnet: Robust meta learning based channel estimation in ofdm systems,” in Proc. IEEE Int. Conf. Commun. (ICC) , Shanghai, China, May 2019
2019
Closest in time.
2019
Closest in time.
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