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.

翻译：由于相比传统定位算法在计算复杂度上的优势，基于指纹的定位需求日益增长。通过信道重构从频域扩展指纹数据库可提升定位精度。然而，在移动环境中，信道重构精度受限于时变参数。本文提出一种基于空间交替广义期望最大化（SAGE）算法提取时变参数的系统，进而利用变分自编码器（VAE）重构另一信道上的信道状态信息。所提方案在深度MIMO信道模型生成的数据上进行了测试，并给出了系统可行性的数学分析。