Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

Chun-Ming Huang

doi:10.1109/access.2022.3146336

Abstract

In the past few years, deep learning has been widely used in various fields due to its outstanding progress. One of the latest applications of deep learning is to use a neural network (NN) with trainable multiplicative weights to design decoders for error-correcting codes. High quality data are essential for deep learning to train robust NN models. In this study, two novel semi-/auto-adaptive SNR algorithms are proposed to efficiently train the neural decoders based on the Sum-Product Algorithm (SPA). For illustration, several neural SPA decoders for the Bose-Chaudhuri-Hocquenghem (BCH) code and low-density parity-check (LDPC) code have been constructed as examples. Simulation results show that, compared with the original neural decoders, the performance of these neural decoders trained by the proposed algorithms can be improved in the range of 0.2 to 0.6 dB. Moreover, the training time required for these decoders to achieve convergence can be reduced by up to 28.8% for the BCH code, and up to 35.6% for the LDPC code, without increasing decoding complexity.

Highlights

Owing to the presence of noise interference in digital message transmission, the received message may not be exactly the same as what was sent
We present the results of training and applying the proposed algorithms to two different codes, BCH(127, 106) [24] code and (155, 64) low-density parity-check (LDPC) code [25]
The training is performed by using the stochastic gradient descent (SGD) with mini-batch, and the optimizer for training the neural network is set to RMSPROP [26]

Summary

Introduction

Owing to the presence of noise interference in digital message transmission, the received message may not be exactly the same as what was sent. Detailed descriptions of the capabilities of these three codes are given for meeting different requirements associated with the enhanced Mobile Broad Band (eMBB), UltraReliable Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC) applications of 5G, as well as the application specific integrated circuit (ASIC) implementation of the decoders. Among these codes, LDPC codes have attracted widespread attention through the excellent performance of the iterative belief propagation (BP) decoding algorithm [2]. Because of its widespread popularity, adaptability, and parallelism in cost-effective hardware implementations, LDPC codes have been used to improve data reliability in various communication applications [5]

Methods

Results

Conclusion