Reviews: Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

In recent years, several papers have employed deep learning methods to decode various classes of codes (turbo codes, linear codes, polar codes). This work focuses on turbo codes, and has the more ambitious goal of providing joint training of the decoder and the encoder (which means that the resulting code will not be a turbo code in the traditional sense). The authors borrow some ideas from the turbo coding literature (e.g., interleaving) and use CNNs to design decoder and encoder (as opposed to RNNs used in several other papers). The proposed TurboAE algorithm achieves performance which is comparable to state-of-the-art codes (see Figure 1). This is quite impressive, even though the code length is quite short (i.e. 100 bits).