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Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Yihan Jiang · Hyeji Kim · Himanshu Asnani · Sreeram Kannan · Sewoong Oh · Pramod Viswanath

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #243

Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically optimal channel codes have been developed by mathematicians for communicating under canonical models after over 60 years of research. On the other hand, in many non-canonical channel settings, optimal codes do not exist and the codes designed for canonical models are adapted via heuristics to these channels and are thus not guaranteed to be optimal. In this work, we make significant progress on this problem by designing a fully end-to-end jointly trained neural encoder and decoder, namely, Turbo Autoencoder (TurboAE), with the following contributions: (a) under moderate block lengths, TurboAE approaches state-of-the-art performance under canonical channels; (b) moreover, TurboAE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. TurboAE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance.

Author Information

Yihan Jiang (University of Washington Seattle)
Hyeji Kim (Samsung AI Center Cambridge)
Himanshu Asnani (University of Washington, Seattle)
Sreeram Kannan (University of Washington)
Sewoong Oh (University of Washington)
Pramod Viswanath (UIUC)

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