IEEE Space, Aerospace and Defence Conference (SPACE)

Intelligent Channel Coding Using Graph Neural Networks: A Machine Learning Perspective

K S Koushik, M. Thilakeshwari, T. Likith Vignesh, Nagarjuna Telagam, Ambar Bajpai

Published Jul 21, 2025• 0 citations

Abstract

Wireless communication is rapidly evolving, and this evolution has led to increasing demands for better error correction, lower latency, and higher adaptability, particularly in Beyond 5G (B5G) networks. Traditional channel coding methods like Low-density Parity-check (LDPC) and Turbo codes often struggle to meet these advanced requirements. To address this challenge, we explore the use of Graph Neural Networks (GNNs) to enhance LDPC decoding. By imposing the structured nature of the Tanner graph, GNNs improve decoding accuracy, optimize bit error rate (BER), and adapt to changing signal conditions in real-time. We implement and evaluate our approach using MATLAB and Python, training our ML-based decoder on varying SNR levels to ensure robustness. The results show that integrating machine learning into LDPC decoding significantly enhances error correction and communication reliability. This work highlights the potential of ML-driven techniques in shaping the future of wireless networks.

DOI: 10.1109/space65882.2025.11171308

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