NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:1585
Title:Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

This paper considers a combination of encoder and decoder architecture, which is a serially-concatenated code with interleavers, as in the turbo codes, combined with turbo-like iterative decoding, and proposes implementing encoders and decoders of the constituent codes with 1D-CNN, which allow us to train the encoders and the decoders in an end-to-end and data-driven fashion. Two reviewers raised concern about the scalability issue of the proposal, and the authors admit in their rebuttal that it is a central question. Although the review scores exhibited a large split in the initial round of review, mainly due to the scalability issue as well as comparison in performance with other existing coding schemes, after the authors' rebuttal all the reviewers rated this paper above the acceptance threshold. I would therefore like to recommend acceptance of this paper.