  Because of the advantages of computation complexity compared with traditional
localization algorithms, fingerprint based localization is getting increasing
demand. Expanding the fingerprint database from the frequency domain by channel
reconstruction can improve localization accuracy. However, in a mobility
environment, the channel reconstruction accuracy is limited by the time-varying
parameters. In this paper, we proposed a system to extract the time-varying
parameters based on space-alternating generalized expectation maximization
(SAGE) algorithm, then used variational auto-encoder (VAE) to reconstruct the
channel state information on another channel. The proposed scheme is tested on
the data generated by the deep-MIMO channel model. Mathematical analysis for
the viability of our system is also shown in this paper.
