Augmenting Channel Simulator and Semi- Supervised Learning for Efficient Indoor Positioning
<|reference_start|>Augmenting Channel Simulator and Semi- Supervised Learning for Efficient Indoor Positioning: 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.<|reference_end|>
@article{li2024augmenting,
    title={Augmenting Channel Simulator and Semi- Supervised Learning for Efficient
  Indoor Positioning},
    author={Yupeng Li, Xinyu Ning, Shijian Gao, Yitong Liu, Zhi Sun, Qixing Wang,
  Jiangzhou Wang},
    journal={arXiv preprint arXiv:2408.00429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00429},
    primaryClass={eess.SP cs.AI}
}