Efficient Deep Learning Adaptation for Cross-Environment RSS-Based Indoor Localization

Abstract

Received signal strength (RSS)-based indoor localization has attracted increasing attention due to its low cost and compatibility with existing wireless infrastructures. However, RSS measurements are highly sensitive to environmental variations, making it challenging for deep learning-based localization models to generalize across different physical configurations. Driven by these challenges, this paper proposes a scenario-adaptive RSS localization framework based on backbone reuse and efficient adaptation. The proposed architecture consists of a lightweight extractor and a shared backbone, where the extractor projects heterogeneous RSS inputs into a unified feature space and the backbone captures transferable localization knowledge. During adaptation, the backbone trained from the source dataset is reused, while the extractor is optimized to adapt to the new dataset configuration. A short optimization stage is further introduced to slightly refine the backbone with a lower learning rate. Experimental results on four datasets demonstrate that the proposed training strategy enables faster convergence and improved adaptation compared with training from scratch. In addition, the proposed framework reduces training time under different dataset configurations, verifying its effectiveness and efficiency for adaptive RSS-based indoor localization.