{
  "id": "2411.09849",
  "version": "v1",
  "published": "2024-11-14T23:56:57.000Z",
  "updated": "2024-11-14T23:56:57.000Z",
  "title": "Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning",
  "authors": [
    "Ahmed Aboulfotouh",
    "Ashkan Eshaghbeigi",
    "Dimitrios Karslidis",
    "Hatem Abou-Zeid"
  ],
  "categories": [
    "eess.SP",
    "cs.AI",
    "cs.LG",
    "cs.NI"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-11-14T23:56:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "self-supervised radio pre-training",
      "foundational models",
      "spectrogram learning",
      "downstream tasks",
      "experimental results demonstrate"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}