Improving 5G Positioning Through Signal-to-Noise Ratio Recognition Training

Fifth-generation communication technology enables advanced indoor positioning with its high bandwidth and frequency capabilities. However, indoor environment variability causes signal propagation fluctuations, making existing models inadequate for accurate location estimation. In this paper, we demonstrate that integrating SNR recognition training with the positioning network can enhance localization performance. Specifically, by leveraging a general neural network to extract common features from 5G Channel State Information (CSI), we employ two carefully designed downstream networks for signal-to-noise ratio (SNR) estimation and location prediction. In order to improve localization performance, we utilize mutual information-based metric to direct each network head towards its specific task, thereby refining and enhancing the features pertinent to each task. Moreover, for enhanced adaptive positioning accuracy, the coefficients linked to the features used in SNR and position estimation are determined based on the hidden features of the corresponding identification head. These coefficients are then utilized to appropriately weigh the hidden features of the location regression module. Experimental results using datasets from real-world scenarios demonstrate that our proposed method significantly improves positioning accuracy over state-of-the-art (SOTA) methods. Code can be available at: https://github.com/mxx123321/indoor_snr.