The challenging neural decoding with general-purpose networks and its improvement via probabilistic embeddings
Résumé
As deep learning is becoming more and more popular in a variety of fields, it is natural to ask whether it can be used for decoding error-correcting codes. In this work, we show that the answer is yes, but with a caveat: Naive application of general-purpose neural networks is not well suited for bitwise decoding. We thoroughly identify the challenges that prevent general-purpose neural networks from decoding successfully, including the curse of dimensionality and the requirement of extremely high accuracy. We then propose a proba- bilistic embedding method in the preprocessing stage that facilitate the learning. We also show that this new method allows general-purpose neural networks to decode with a perfor- mance that is close to the theoretical optimality while saving time compared to traditional decoding methods such as maximum-likelihood decoding or the BCJR algorithm.