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When a channel model is not available, the end-to-end training of encoder and decoder on a fading noisy channel generally requires the repeated use of the channel and of a feedback link.
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S. Park, H. Jang, O. Simeone, and J. Kang, “Learning how to demodulate from few pilots via meta-learning,” in
2019
Cited alongside, same era.
——, “Learning to demodulate from few pilots via offline and online meta-learning,”
2019
Cited alongside, same era.
H. Mao, H. Lu, Y. Lu, and D. Zhu, “Roemnet: Robust meta learning based channel estimation in ofdm systems,” in
2019
Cited alongside, same era.
2019
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L. M. Zintgraf, K. Shiarlis, V. Kurin, K. Hofmann, and S. Whiteson, “Fast context adaptation via meta-learning,” in
2019
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Y. Jiang, H. Kim, H. Asnani, and S. Kannan, “Mind: Model independent neural decoder,” in
Cited in the paper.
M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn, “Meta-learning without memorization,”
2019
Later among the works it cites.
O. Simeone, S. Park, and J. Kang, “From learning to meta-learning: Reduced training overhead and complexity for communication systems,” in
2020
Closest in time.
S. Park, O. Simeone, and J. Kang, “Meta-learning to communicate: Fast end-to-end training for fading channels,” in
2020
Closest in time.
J. Rothfuss, V. Fortuin, and A. Krause, “
2020
Closest in time.
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