Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning

• URL: http://arxiv.org/abs/2411.09849v1
• Date: Thu, 14 Nov 2024 23:56:57 GMT
• Title: Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning
• Authors: Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Dimitrios Karslidis, Hatem Abou-Zeid,
• Abstract summary: Foundational deep learning (DL) models are general models, trained on diverse, diverse, and unlabelled datasets. We introduce Masked Spectrogram Modeling, a novel self-supervised learning approach for pretraining foundational DL models on radio signals.
• Score: 6.1339395157466425
• License:
• Abstract: Foundational deep learning (DL) models are general models, trained on large, diverse, and unlabelled datasets, typically using self-supervised learning techniques have led to significant advancements especially in natural language processing. These pretrained models can be fine-tuned for related downstream tasks, offering faster development and reduced training costs, while often achieving improved performance. In this work, we introduce Masked Spectrogram Modeling, a novel self-supervised learning approach for pretraining foundational DL models on radio signals. Adopting a Convolutional LSTM architecture for efficient spatio-temporal processing, we pretrain the model with an unlabelled radio dataset collected from over-the-air measurements. Subsequently, the pretrained model is fine-tuned for two downstream tasks: spectrum forecasting and segmentation. Experimental results demonstrate that our methodology achieves competitive performance in both forecasting accuracy and segmentation, validating its effectiveness for developing foundational radio models.

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