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IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)

Graph Neural Network Approaches to LDPC Decoding in 5G/6G Communications

Juliy Boiko, Ilya Pyatin, Volodymir Druzhynin

Published Feb 17, 2026• 0 citations
Publisher
DOI
10.1109/tcset65181.2026.11460731

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Abstract In the evolving landscape of wireless communication, particularly in Beyond 5G (B5G) systems, channel coding must meet increasingly demanding requirements for reliability, adaptability, and low latency. Traditional decoding algorithms such as Belief Propagation (BP), while effective in structured environments, lack the flexibility to adapt to dynamic and noisy channel conditions. This paper presents a novel Graph Neural Network (GNN)-based approach for decoding Low-Density Parity-Check (LDPC) codes, leveraging the inherent bipartite graph structure of Tanner graphs. The proposed decoder learns data-driven message-passing strategies that outperform BP across a wide range of signal-to-noise ratios (SNRs) and block lengths.Our implementation integrates Python-based GNN modeling with MATLAB-based LDPC encoding and benchmarks the decoding performance under additive white Gaussian noise (AWGN) channels. Experimental results demonstrate that the GNN-based decoder achieves significantly lower bit error rates (BER), faster convergence, and superior robustness to noise variability compared to BP, particularly at medium-to-high SNR levels. Furthermore, an ablation study validates the architectural efficiency of shallow GNNs with attention mechanisms and skip connections. This work highlights the potential of machine learning-enhanced decoders in redefining the physical layer of next-generation wireless networks and sets the foundation for scalable, adaptive, and intelligent decoding systems in B5G and 6G architectures.
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.