# A Novel Cross-band CSI Prediction Scheme for Multi-band Fingerprint based Localization

arXiv: https://arxiv.org/abs/2405.03842 · HTML: https://mlsanity.com/paper/2405.03842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 5 of 5)

- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization](https://mlsanity.com/paper/2309.12200/) (2023)
- [Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment](https://mlsanity.com/paper/1609.00661/) (2016)

## Abstract (arXiv)

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.