Autoencoder-Based Error Correction Coding for One-Bit Quantization
This work addresses bandwidth-efficient error correction for communication systems with one-bit quantization, offering a novel hybrid method that is incremental in combining existing techniques.
The paper tackles the problem of error correction coding for AWGN channels with one-bit quantization by proposing a novel scheme that combines autoencoders with turbo codes, achieving performance close to a hypothetically perfect autoencoder and outperforming conventional turbo codes for QPSK modulation while enabling operation with 16-QAM.
This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in the receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which "perfectly" refers to converging the global minima. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder with a suboptimum training, we propose utilizing turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder. It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so. The proposed coding method is as bandwidth efficient as the integrated (outer) turbo code, since the autoencoder exploits the excess bandwidth from pulse shaping and packs signals more intelligently thanks to sparsity in neural networks. Our results show that the proposed coding scheme at finite block lengths outperforms conventional turbo codes even for QPSK modulation. Furthermore, the proposed coding method can make one-bit quantization operational even for 16-QAM.