A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization
This work addresses indoor localization for applications like VR/AR and smart homes, offering an incremental improvement over existing fingerprint-based methods.
The paper tackled the problem of indoor localization accuracy limited by multipath signal refraction by predicting channel state information across multiple frequency bands and splicing them together, achieving more precise localization results on both simulated COST 2100 data and real-time OFDM WiFi data from an office scenario.
Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario.