# Augmenting Channel Simulator and Semi- Supervised Learning for Efficient Indoor Positioning

arXiv: https://arxiv.org/abs/2408.00429 · HTML: https://mlsanity.com/paper/2408.00429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions](https://mlsanity.com/paper/2407.00896/) (2024)

## Built on by (most cited first, 1 of 1)

- [Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recovery](https://mlsanity.com/paper/2510.02744/) (2025)

## Abstract (arXiv)

This work aims to tackle the labor-intensive and resource-consuming task of indoor positioning by proposing an efficient approach. The proposed approach involves the introduction of a semi-supervised learning (SSL) with a biased teacher (SSLB) algorithm, which effectively utilizes both labeled and unlabeled channel data. To reduce measurement expenses, unlabeled data is generated using an updated channel simulator (UCHS), and then weighted by adaptive confidence values to simplify the tuning of hyperparameters. Simulation results demonstrate that the proposed strategy achieves superior performance while minimizing measurement overhead and training expense compared to existing benchmarks, offering a valuable and practical solution for indoor positioning.