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In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization.
2014
Earlier work this paper cites.
I. Goodfellow et al. , “Generative adversarial nets”, in Proc. NIPS , pp. 2672-2680, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
E. Nachmani, Y. Be’ery, D. Burshtein, “Learning to decode linear codes using deep learning”, in Proc. IEEE Annu. Allerton Conf. Commun. Control Comput. (Allerton) , pp. 341-346, 2016
2016
Earlier work this paper cites.
N. Samuel, T. Diskin, A. Wiesel, “Deep MIMO detection”, in Proc. IEEE 18th Int. Workshop Signal Process. Adv. Wireless Commun., pp. 690-694, 2017
2017
Earlier work this paper cites.
T. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Trans. on Cogn. Comm un. Netw., vol. 3, no. 4, pp. 563-575, Dec. 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
H. Ye, G. Y. Li, and B.-H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems” IEEE Wireless Commun. Lett. , no. 7, pp.114-117, Feb. 2018
2018
Cited alongside, same era.
N. Farsad, M. Rao, A. Goldsmith, “Deep learning for joint source-channel coding of text,” In Proc. ICASSP 2018
2018
Cited alongside, same era.
S. Dorner, S. Cammerer, J. Hoydis, S. ten Brink, “Deep learning-based communication over the air”, IEEE J. Select. Topics Signal Process., vol.12, no. 1, pp. 132-143, Feb. 2018
2018
Closest in time.
A. Felix, S. Cammerer, S. Dorner, J. Hoydis, and S. ten Brink, “OFDM autoencoder for end-to-end learning of communications systems,” in Proc. IEEE Int. Workshop Signal Proc. Adv. Wireless Commun.(SPAWC) , Jun. 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
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