CNN-Based Approach for Enhancing 5G LDPC Code Decoding Performance

Abstract

This paper presents the novel Stochastic decoding-convolutional neural network (SD-CNN) structure, with the goal of enhancing 5G LDPC code’s decoding efficiency in the presence of correlated noise of the channel. Applying the stochastic approach acts as an alternate technique to fixed-point LDPC decoding to enhance the decoder’s hardware efficiency. In order to improve the efficacy of the decoder, we adopted deep learning method such as Convolutional neural network (CNN). CNNs can be employed for denoising purposes in communication systems, where signals may be compromised by diverse forms of noise while being transmitted, with the aim of enhancing signal quality and dependability. The SD-CNN architecture combines a trained CNN with a stochastic decoder of 5G LDPC codes, thereby utilizing the CNN’s capability to accurately estimate channel noise and, consequently, enhance the error correction capabilities of the decoder. The generated output of the trained CNN is then fed back into the stochastic decoder, creating an iterative process between the SD and CNN that ultimately leads to superior decoding performance. For 5G LDPC code word length $N = 3808$ , with a base code rate $R = 1/3$ , the suggested SD-CNN architecture achieves a BER of $10^{-6}$ at 0.6 dB of SNR per bit in the strong correlation of channel noise condition, in comparison to SD, which achieves a BER of $10^{-6}$ at 3.5 dB of SNR per bit. The results demonstrate that there is a 2.9 dB improvement.

Keywords

• Channel coding
• convolutional neural network (CNN)
• fifth generation (5G) wireless technology
• stochastic decoding (SD)
• low density parity check codes