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

TL;DR

This paper introduces a semi-supervised learning approach combined with an improved channel simulator to enhance indoor positioning accuracy while reducing measurement costs and training expenses.

Contribution

It proposes a novel SSLB algorithm and an updated channel simulator that together improve indoor positioning efficiency and performance.

Findings

Superior positioning accuracy with less measurement overhead

Effective utilization of unlabeled data through adaptive weighting

Reduced training expenses compared to benchmarks

Abstract

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.