A Foundation Model for Wireless Technology Recognition and Localization Tasks

Abstract

Wireless Technology Recognition (WTR) and localization are essential in modern communication systems, enabling efficient spectrum usage, coexistence across diverse technologies, and accurate positioning in dynamic environments. Real-world deployments must handle signals from 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 fail 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 aciq and Channel Impulse Response (CIR) timeseries data. The model aims for reusability and generalizability compared to single-task architectures. It 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.