AUTOENCODER-BASED ERROR CORRECTION CODING FOR LOW-RESOLUTION COMMUNICATION

Various embodiments of the present technology provide a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of moderate to low bit quantization (e.g., one-bit quantization) in the receiver. Some embodiments of the error correction code minimize the probability of bit error can be obtained by perfectly training a special autoencoder, in which “perfectly” refers to finding the global minima of its cost function. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder with a suboptimum training, some embodiments utilize turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder.