# Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks

arXiv: https://arxiv.org/abs/2108.08241 · HTML: https://mlsanity.com/paper/2108.08241/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)

## Built on by (most cited first, 5 of 5)

- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [Velocity-Based Channel Charting with Spatial Distribution Map Matching](https://mlsanity.com/paper/2311.08016/) (2023)
- [Channel Charting in Smart Radio Environments](https://mlsanity.com/paper/2508.07305/) (2025)
- [Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2511.00919/) (2025)
- [Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory](https://mlsanity.com/paper/2511.17007/) (2025)

## Abstract (arXiv)

In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks. In particular, a convolutional autoencoder model is proposed to estimate the three-dimensional location of wireless user equipment (UE), based on multipath channel state information (CSI), received by different base stations. In order to learn the radio-geometry map and capture the relative position of each UE, an autoencoder-based channel chart is constructed in an unsupervised manner, such that neighboring UEs in the physical space will remain close in the channel chart. Next, the channel charting model is extended to a semi-supervised framework, where the autoencoder is divided into two components: an encoder and a decoder, and each component is optimized individually, using the labeled CSI dataset with associated location information, to further improve positioning accuracy. Simulation results show that the proposed CC-aided semi-supervised localization yields a higher accuracy, compared with existing supervised positioning and conventional unsupervised CC approaches.