SNWPM: A Siamese network based wireless positioning model resilient to partial base stations unavailable

Abstract

Artificial intelligence (AI) models are promising to improve the accuracy of wireless positioning systems, particularly in indoor environments where unpredictable radio propagation channel is a great challenge. Although great efforts have been made to explore the effectiveness of different AI models, it is still an open problem whether these models, trained with the data collected from all base stations (BSs), could work when some BSs are unavailable. In this paper, we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work. Particularly, a Siamese Network based Wireless Positioning Model (SNWPM) is proposed to predict the location of mobile user equipment from channel state information (CSI) collected from 5G BSs. Furthermore, a Feature Aware Attention Module (FAAM) is introduced to reinforce the capability of feature extraction from CSI data. Experiments are conducted on the 2022 Wireless Communication AI Competition (WAIC) dataset. The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable. Compared with other AI models, the proposed SNWPM can reduce the positioning error by nearly 50% to more than 60% while using less parameters and lower computation resources.