Decoding 5G-NR Communications via Deep Learning
This addresses the computational complexity and performance trade-offs in 5G communications for industries relying on efficient signal processing, though it appears incremental as it builds on existing deep learning methods applied to a new domain.
The paper tackled the problem of decoding 5G-NR communications by proposing Autoencoding Deep Neural Networks (ADNN) for demapping and decoding, achieving a 3 dB reduction in Signal-to-Noise Ratio (SNR) for a given Bit Error Rate (BER) target in AWGN channels.
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.