Indoor Localization Based on CSI Fingerprint by Siamese Convolution Neural Network

In this paper, a novel indoor localization system with channel state information (CSI) fingerprints is proposed, which learns the spatial and frequency features of CSI in the fifth-generation (5G) cellular network by a Siamese convolution neural network. In particular, considering that the CSI continuously collected by a moving target possesses the implicit spatial association, we locate the target by the successive CSI data gathered within a time interval which can be regarded as an information subspace of the fingerprint database. Therefore, the fingerprint localization can be modeled as a subspace matching problem and solved by the Siamese network-based similarity learning. In the proposed system, we design a structure of CSI fingerprint which includes the information from multiple base stations in spatial and frequency domains. Then, the proposed Siamese architecture extracts the CSI feature and estimates the location of the target by feature similarity comparison. Compared with the existing algorithms, it can increase the positioning accuracy significantly by the feature relevance among the CSI data collected at different positions. The field tests indicate that compared to other CSI fingerprint-based positioning methods, our proposed algorithm can effectively reduce the localization error.

Automated summary

This paper proposes an indoor localization system using CSI fingerprints in the 5G network with a Siamese convolution neural network. The system locates targets by utilizing continuous CSI data and modeling it as a subspace matching problem, solving it through similarity learning. By comparing feature similarities, the system significantly improves positioning accuracy and effectively reduces localization errors compared to other methods.