On a Unified Deep Neural Network Decoding Architecture

In modern communication systems, multiple types of error-correcting codes can be utilized for different transmission scenarios. Therefore, the receiver should include the decoder compatible with multiple codes used in the transmission scheme, which results in the increase of the resources required for its implementation. In this paper, we investigate the possibility of training a single syndrome-based DNN decoder to solve the problem of unified decoding. We observe, that the syndrome-based approach allows to extend the unified decoding capabilities to the codes with considerably larger lengths in comparison to the initially described by Wang et al. (2018) method. Through numerical experiments, we show that the model trained for decoding a pair of moderate lengths codes (BCH and CRC-Aided Polar) achieves performance results comparable with classical decoding solutions, while sharing the same architecture and the set of trainable weights. We note, that the unification of a syndrome-based DNN decoder does not lead to large performance degradation, in comparison to the decoder trained on a single code. The approach described in the paper is promising in terms of reducing the hardware resources required to implement the decoder.