{
  "annotation_id": "80286324-6c02-47ac-9772-14af2745a66f",
  "date_created": "2026-02-17T05:53:23.425000Z",
  "date_modified": "2026-02-17T05:53:23.425000Z",
  "file_hash": "6134e40d3ad3eaf29adae2489fd3922414092db361791c51b405476c83b74990",
  "private": false,
  "record": {
    "abstract": "Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovative approaches such as the use of Transformer models. This work investigates the application of Transformer models, specifically the GPT-2 architecture, to generate novel modulation schemes for wireless communications. By training a GPT-2 model on a dataset of existing modulation formulas, new modulation schemes has been created. These generated schemes are then compared to traditional methods using key performance metrics such as Signal-to-Noise Ratio (SNR) and Power Spectrum Density (PSD). The results show that Transformer-generated modulation schemes can achieve performance comparable to, and in some cases outperforming, traditional methods. This demonstrates that advanced CR systems could greatly benefit from the implementation of Transformer models, leading to more efficient, robust, and secure communication systems.",
    "arxiv_id": "2601.10519",
    "authors": [
      "Andrea Melis",
      "Andrea Piroddi",
      "Roberto Girau"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models",
    "url": "https://arxiv.org/abs/2601.10519",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d7aea700-e355-498c-bb07-3d1c0c709efb",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}