Feedback Turbo Autoencoder

Designing channel codes is one of the core research areas for modern communication systems. Canonical channel codes asymptotically achieve near-capacity performance under a large block length regime for additive white gaussian noise channels. However, this achieved success does not generalize to many channels. Channels with output feedback, proposed by Shannon, is one of such channels where practical codes have been unknown for several decades. Recently it has been demonstrated that deep learning based code outperforms the state-of-the-art codes for channels with output feedback. While the success is promising and inspiring, there are a few major challenges that need to be addressed. Firstly, the channel assumes a feedback with a unit step delay, which is not very practical. Second is the lack of generalization to larger block lengths. In this work, we propose Feedback Auto Turbo Encoder (FTAE) which harmoniously combines interleaver and iterative decoding with CNN architectures and demonstrate the blocklength gain and improved performance in the block feedback setting.