Decoding 5G-NR Communications via Deep Learning

TL;DR

This paper introduces a novel deep learning-based method using Autoencoding Neural Networks combined with Deep Neural Networks to improve decoding in 5G-NR communications, reducing the required SNR for a given BER.

Contribution

It presents a new deep learning architecture for demapping and decoding in 5G, achieving better performance than traditional methods.

Findings

Achieves 3 dB SNR reduction at a specific BER target.

Demonstrates effectiveness of deep learning in 5G physical layer decoding.

Outperforms conventional LDPC decoding methods.

Abstract

Upcoming modern communications are based on 5G specifications and aim at providing solutions for novel vertical industries. One of the major changes of the physical layer is the use of Low-Density Parity-Check (LDPC) code for channel coding. Although LDPC codes introduce additional computational complexity compared with the previous generation, where Turbocodes where used, LDPC codes provide a reasonable trade-off in terms of complexity-Bit Error Rate (BER). In parallel to this, Deep Learning algorithms are experiencing a new revolution, specially to image and video processing. In this context, there are some approaches that can be exploited in radio communications. In this paper we propose to use Autoencoding Neural Networks (ANN) jointly with a Deep Neural Network (DNN) to construct Autoencoding Deep Neural Networks (ADNN) for demapping and decoding. The results will unveil that, for a particular BER target, $3$ dB less of Signal to Noise Ratio (SNR) is required, in Additive White Gaussian Noise (AWGN) channels.