# A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization

arXiv: https://arxiv.org/abs/2309.12200 · HTML: https://mlsanity.com/paper/2309.12200/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 19 Sep 2023.

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

- [Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://mlsanity.com/paper/1905.03761/) (2019)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems](https://mlsanity.com/paper/1903.02875/) (2019)
- [Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point-Regression Versus Classification](https://mlsanity.com/paper/1911.08563/) (2019)
- [High Precision Indoor Localization with Dummy Antennas -- An Experimental Study](https://mlsanity.com/paper/2108.10646/) (2021)

## Built on by (most cited first, 2 of 2)

- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [A Novel Cross-band CSI Prediction Scheme for Multi-band Fingerprint based Localization](https://mlsanity.com/paper/2405.03842/) (2024)

## Abstract (arXiv)

Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario.