A Novel Cross-band CSI Prediction Scheme for Multi-band Fingerprint based Localization
This work addresses localization accuracy for mobile users in wireless networks, but it appears incremental as it builds on existing fingerprint and reconstruction methods.
The paper tackles the problem of improving fingerprint-based localization accuracy in mobility environments by addressing the limitation of channel reconstruction due to time-varying parameters, proposing a system that extracts these parameters using the SAGE algorithm and reconstructs channel state information with a VAE, tested on deep-MIMO channel model data with mathematical analysis for viability.
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.