LWM-Spectro: A Foundation Model for Wireless Baseband Signal Spectrograms

TL;DR

LWM-Spectro is a transformer-based foundation model trained on large-scale I/Q spectrogram data that learns transferable wireless signal representations, improving performance on various downstream tasks with limited supervision.

Contribution

It introduces a novel self-supervised learning framework with MoE architecture for wireless signals, enabling robust transfer to multiple tasks.

Findings

Outperforms existing methods in modulation classification.

Effective in few-shot and data-rich scenarios.

Provides a unified foundation for wireless signal understanding.

Abstract

The received in-phase and quadrature (I/Q) baseband signals inherently encode physical-layer and channel characteristics of wireless links. Learning robust and transferable representations directly from such raw signals, however, remains challenging due to heterogeneous communication systems, diverse propagation environments, and limited labeled data. To address this, we present LWM-Spectro, a transformer-based foundation model pretrained on large-scale I/Q data represented as time-frequency spectrograms. The model leverages self-supervised masked modeling, contrastive learning, and a mixture-of-experts (MoE) architecture to learn general-purpose wireless representations. These representations transfer effectively to downstream tasks such as modulation classification and joint SNR/mobility recognition, even with minimal supervision. Across tasks, LWM-Spectro consistently outperforms state-of-the-art deep learning baselines in both few-shot and data-rich regimes, providing a unified foundation for wireless learning.