Denoising and Restoring Channel State Information for 5G Indoor Positioning in Low-SNR Scenarios

Abstract

Indoor positioning with 5G technologies depends on accurate Channel State Information (CSI) for high-precision location services, but limited data, especially in low signal-to-noise ratio (SNR) environments, presents a significant challenge. Traditional deep learning methods, relying on "Learning + Finetuning", require large datasets at specific SNR levels, limiting their effectiveness. This paper introduces a novel CSI image generation and restoration method designed for small-sample conditions, allowing data from various SNR levels to be utilized to improve downstream task performance. We propose a new CSI generation model that leverages graph neural networks and a multi-level loss function to accurately generate CSI images corresponding to specific SNRs. Our approach addresses the unique characteristics of CSI signals that traditional image generation methods fail to capture. Experiments show that our approach significantly improves prediction accuracy, outperforming existing methods, particularly in low-SNR scenarios. Our core code is publically available at https://github.com/kaqiz/DARC.