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Indoor localization is getting increasing demands for various cutting-edgedtechnologies, like Virtual/Augmented reality and smart home. Traditionalmodel-based localization suffers from significant computational overhead, sofingerprint localization is getting increasing attention, which needs lowercomputation cost after the fingerprint database is built. However, the accuracyof indoor localization is limited by the complicated indoor environment whichbrings the multipath signal refraction. In this paper, we provided a scheme toimprove the accuracy of indoor fingerprint localization from the frequencydomain by predicting the channel state information (CSI) values from anothertransmitting channel and spliced the multi-band information together to getmore precise localization results. We tested our proposed scheme on COST 2100simulation data and real time orthogonal frequency division multiplexing (OFDM)WiFi data collected from an office scenario.

A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization