Wireless Technology Recognition (WTR) and localization are essential in modern communication systems, enabling efficient spectrum management, seamless coexistence of diverse technologies, and accurate positioning in dynamic environments. In real-world conditions, solutions must handle signals from various resources with different sampling rates, capturing devices, frequency bands, and propagation conditions. Traditional methods, such as energy detection and conventional Deep Learning (DL) models like Convolutional Neural Networks (CNNs), often lack the robustness to generalize across unseen technologies, environments, or tasks. In this work, we introduce a Transformer-based foundation model for both WTR and localization, pre-trained in a self-supervised manner on large-scale, unlabeled datasets of In-phase and Quadrature (IQ) and Channel Impulse Response (CIR) timeseries. The model leverages input patching for computational efficiency and employs a two-stage pipeline: self-supervised pre-training to learn general-purpose representations, followed by lightweight fine-tuning for task-specific adaptation. This enables the model to generalize to new wireless technologies and unseen environments using minimal labeled samples. Evaluations across short-range and long-range datasets show superior accuracy in WTR (up to 99.99%), Line-Of-Sight (LOS) detection (up to 100%), and ranging error correction (reducing Mean Absolute Error (MAE) by up to 50%), all while maintaining low computational complexity. These results underscore the potential of a reusable wireless foundation model for multi-task applications with minimal retraining.
Cheraghinia et al. (2025) studied this question.