Because of the advantages of computation complex- ity 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.

基于指纹定位的通道重建，利用空间交替广义期望极大化算法提取时变参数，并利用变分自动编码器在另一个通道上重建通道状态信息。在深度多输入多输出通道模型产生的数据上测试了该方案，并在本文中展示了我们系统的可行性的数学分析。