Authors
Yousif Al-Dakoki, Jasraj Gill, Sunila Akbar, Lorenzo Maria Monteforte, Luca Chiaraviglio, Hina Tabassum
Published in
IEEE transactions on neural networks and learning systems. Volume PP. Sep 28, 2026. Epub Sep 28, 2026.
Abstract
This brief proposes a multi-modal self-supervised learning (SSL) framework for classifying wireless signals from raw in-phase and quadrature (IQ) traces, enabling joint classification of transmission bandwidths, power levels, and modulation types. Unlike conventional modulation classification, jointly inferring these attributes yields a more discriminative signature for reliable signal identification. The proposed framework employs a contrastive masked autoencoder (CMAE) to exploit the intrinsic structure of the unlabeled data, while reducing reliance on manual annotation. IQ-CMAE integrates generative reconstruction and contrastive alignment objectives, reconstructing masked inputs across multiple IQ modalities, namely spectrograms, Gramian angular fields (GAFs), and constellations while enforcing instance-level discrimination via contrastive learning. To balance the dual objectives, we employ contrastive gradient stopping, restricting contrastive gradients to the top K encoder layers, which enhances stability and robustness under channel variations and low-data regimes. We further conduct a systematic analysis of multi-modal fusion depth-evaluating early, mid, and late fusion strategies to characterize their interaction with the hybrid learning objective. Extensive experiments on both controlled-lab and real-world datasets demonstrate that IQ-CMAE with mid-level fusion significantly outperforms standard MAE and contrastive SSL baselines in classification accuracy and representation robustness.
PMID:
42804405
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.
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