A dual-mode framework for indoor localization via temporal learning and knowledge distillation

This paper proposes a dual-mode learning framework that combines sequence modeling with LSTM and lightweight inference using a distilled MLP. The framework supports both temporal RSS sequences and single-snapshot inputs for efficient real-time indoor localization.