  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.
