Soft-Output Deep Neural Network-Based Decoding

TL;DR

The conclusion is made that the new method is prospective for the challenging problem of DNN-based decoding of long codes consisting of short component codes, and significantly outperforms the solution based on the Chase decoder.

Abstract

Deep neural network (DNN)-based channel decoding is widely considered in the literature. The existing solutions are investigated for the case of hard output, i.e. when the decoder returns the estimated information word. At the same time, soft-output decoding is of critical importance for iterative receivers and decoders. In this paper, we focus on the soft-output DNN-based decoding problem. We start with the syndrome-based approach proposed by Bennatan et al. (2018) and modify it to provide soft output in the AWGN channel. The new decoder can be considered as an approximation of the MAP decoder with smaller computation complexity. We discuss various regularization functions for joint DNN-MAP training and compare the resulting distributions for [64, 45] BCH code. Finally, to demonstrate the soft-output quality we consider the turbo-product code with [64, 45] BCH codes as row and column codes. We show that the resulting DNN-based scheme is very close to the MAP-based performance and significantly outperforms the solution based on the Chase decoder. We come to the conclusion that the new method is prospective for the challenging problem of DNN-based decoding of long codes consisting of short component codes.

Figures (7)

• Figure 1: Gated Recurrent Unit cell
• Figure 2: Stacked-GRU model architecture
• Figure 3: Output LLR distributions histogram for $E_s/N_0 = 1 dB$$[64,45]$ BCH code. The moments-based approach is used for regularization.
• Figure 4: Bit error rate results for $[64, 45]$ BCH code
• Figure 5: TPC structure