5GLoc: Graph-Based Learning for Robust 5G Indoor Localization under Dynamic Anchor Visibility

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Abstract
With the evolution of Fifth-Generation (5G) wireless systems, high-resolution channel observations enabled by large bandwidths and massive Multiple-Input Multiple-Output (MIMO) have opened new opportunities for indoor positioning. However, while existing fingerprinting-based learning methods excel in complex multipath environments, they often rely on fixed anchor configurations. This makes them vulnerable to dynamic anchor visibility caused by signal blockages, user mobility, or hardware failures in practical deployments. To address this, we propose 5G Graph-based Localization (5GLoc), a learning-driven framework designed for robust indoor positioning under dynamic anchor availability. By modeling localization instances as graphs, 5GLoc utilizes a Dynamic Graph Attention Network (DGAT) to flexibly accommodate varying numbers and combinations of anchors, ensuring robust inference under heterogeneous observations. We further introduce a Combinatorial Dynamic Node Masking (CDNM) strategy for data augmentation, as well as a composite loss function with learnable weights for balancing accuracy and stability. Finally, a map-constrained Particle Filter (PF) is integrated to refine trajectories for spatial feasibility and trajectory consistency. Experimental results based on the IPIN 2025 dataset demonstrate that 5GLoc achieves superior performance in complex warehouse environments, with a 75th percentile error of 0.857 m. Compared to several representative positioning methods, 5GLoc reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 42.2%–80.2% and 50.6%–80.8%, respectively, maintaining high reliability even under extreme conditions with reduced anchor availability. This framework provides an effective solution for 5G localization in dynamic environments, meeting both precision and real-time requirements.
Funding Information
  • Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project (2024ZD1004101)
  • National Natural Science Foundation of China (42274048)

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