Deep learning-based encoder for one-bit quantization

Date

2019-12-01

Author

Balevi, Eren
Andrews, Jeffrey G.

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© 2019 IEEE.This paper proposes a deep learning-based error correction coding for AWGN channels under the constraint of one-bit quantization in receivers. An autoencoder is designed and integrated with a turbo code that acts as an implicit regularization. This implicit regularizer facilitates approaching the Shannon bound for the one-bit quantized AWGN channels even if the autoencoder is trained suboptimally, since one-bit quantization stymies ideal training. Our empirical results show that the proposed coding scheme gives better results at finite block lengths than conventional turbo codes even for QPSK modulation, which can achieve the Shannon bound at infinite block length despite one-bit quantization. Furthermore, the proposed coding method makes one- bit quantization operational even for 16-QAM, which is unprecedented.

Subject Keywords

Code design, Deep learning, One-bit quantization

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081964523&origin=inward
https://hdl.handle.net/11511/100177

DOI

https://doi.org/10.1109/globecom38437.2019.9013923

Conference Name

2019 IEEE Global Communications Conference, GLOBECOM 2019

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Balevi and J. G. Andrews, “Deep learning-based encoder for one-bit quantization,” presented at the 2019 IEEE Global Communications Conference, GLOBECOM 2019, Hawaii, Amerika Birleşik Devletleri, 2019, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081964523&origin=inward.