Published as a conference paper at ICLR 2018 COMMUNICATION ALGORITHMS VIA DEEP LEARNING
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parametrized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.
In this paper the authors propose to use RNNs and LSTMs for channel coding. But I have the impression the authors completely miss the state of the art in channel coding and the results are completely useless for any current communication system. I believe that machine learning, in general, and deep learning, in particular, might be of useful for physical layer communications. I just do not see why it would be useful for channel coding over the AWGN channel. Let me explain.
If the decoder knows that the encoder is using a convolutional code, why does it need to learn the decoder instead of using the Viterbi or BCJR algorithms that are known to be optimal for sequences and symbols, respectively. I cannot imagine an scenario in which the decoder does not know the convolutional code that it is being used and the encoder sends 120,000 bits of training sequence (useless bits from information standpoint) for the decoder to learn it. More important question, do the authors envision that this learning is done every time there is a new connection or it is learnt once and for all. If it is learnt every time that would be ideal if we were discovering new channel codes everyday, clearly not the case. If we learnt it one and for all and then we incorporated in the standard that would only make sense if the GRU structure was computationally better than the BCJR or Viterbi. I would be surprise if it is. If instead of using 2 or 3 memories, we used 6-8 does 120,000 bits be good enough or we need to exponentially increase the training sequence? So the first result in the paper shows that a tailored structure for convolutional encoding can learn to decode it. Basically, the authors are solving a problem that does not need solving. 
For the Turbocodes the same principle as before applies. In this case the comments of the authors really show that they do not know anything about coding. In Page 6, we can read: “Unlike the convolutional codes, the state of the art (message-passing) decoders for turbo codes are not the corresponding MAP decoders, so there is no contradiction in that our neural decoder would beat the message-passing ones”. This is so true, so I expected the DNN structure to be significantly better than turbodecoding. But actually, they do not. These results are in Figure 15 page 6 and the solution for the turbo decoders and the DNN architecture are equivalent. I am sure that the differences in the plots can be explained by the variability in the received sequence and not because the DNN is superior to the turbodecoder. Also in this case the training sequence is measured in the megabits for extremely simple components. If the convolutional encoders were larger 6-8 bits, we would be talking about significantly longer training sequences and more complicated NNs.
In the third set the NNs seems to be superior to the standard methods when burst-y noise is used, but the authors seems to indicate that that NN is trained with more information about these bursts that the other methods do not have. My impression is that the authors would be better of focusing on this example and explain it in a way that it is reproducible. This experiment is clearly not well explained and it is hard to know if there is any merit for the proposed NN structure. 
Finally, the last result would be the more interesting one, because it would show that we can learn a better channel coding and decoding mechanism that the ones humans have been able to come up with. In this sense, if NNs can solve this problem that would be impressive and would turn around how channel coding is done nowadays. If this result were good enough, the authors should only focus in it and forget about the other 3 cases. The issue with this result is that it actually does not make sense. The main problem with the procedure is that the feedback proposal is unrealistic, this is easy to see in Figure 16 in which the neural encoder is proposed. It basically assumes that the received real-valued y_k can be sent (almost) noiselessly to the encoder with minimal delay and almost instantaneously. So the encoder knows the received error and is able to cancel it out. Even if this procedure could be implemented, which it cannot be. The code only uses 50 bits and it needed 10^7 iterations (500Mbs) to converge. The authors do not show how far they are from the Shannon limit, but I can imagine that with 50 bit code, it should be pretty far.  
We know that with long enough LDPC codes we can (almost) reach the Shannon limit, so new structure are not needed. If we are focusing on shorter codes (e.g. latency?) then it will be good to understand why do we need to learn the channel codes. A comparison to the state of the art would be needed. Because clearly the used codes are not close to state of the art. For me the authors either do not know about coding or are assuming that we do not, which explains part of the tone of this review.