2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2018
DOI: 10.1109/spawc.2018.8445920
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OFDM-Autoencoder for End-to-End Learning of Communications Systems

Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and m… Show more

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Cited by 235 publications
(155 citation statements)
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“…In particular, the similarities between the autoencoder architecture and the digital communications systems have motivated significant research efforts in the direction of modelling end-to-end communication systems using the autoencoder architectures [11]. Some examples include decoder design for existing channel codes [12], blind channel equalization [13], learning physical layer signal representation for SISO [11] and, OFDM systems [14].…”
Section: Introductionmentioning
confidence: 99%
“…In particular, the similarities between the autoencoder architecture and the digital communications systems have motivated significant research efforts in the direction of modelling end-to-end communication systems using the autoencoder architectures [11]. Some examples include decoder design for existing channel codes [12], blind channel equalization [13], learning physical layer signal representation for SISO [11] and, OFDM systems [14].…”
Section: Introductionmentioning
confidence: 99%
“…Signal parameters are estimated with a separate DNNs and the signal is then processed by manually programmed operations. The authors in [8] and [9] propose to learn merely the receiver side of the system and afterwards apply manual signal processing to support synchronization.…”
Section: Introductionmentioning
confidence: 99%
“…New ways of thinking about communications as end-to-end reconstruction optimization tasks are introduced in [30], which utilize autoencoders to jointly learn transmitter and receiver implementations as well as signal encodings without any prior knowledge. Similar thoughts are applied in OFDM [31], massive MIMO systems [32], millimeter-wave communications [33], optical fiber communications [34] and multi-colored visible light communications [35]. DL for channel coding is also attracting attentions [36,37].…”
Section: Related Workmentioning
confidence: 95%