{
  "abstract": "Automatic modulation classification (AMC) is a crucial task in modern communication systems, enabling the identification of modulation schemes in received signals. This paper presents a proposed model to achieve AMC more accurately by using hybrid techniques, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) with spectrograms as input features. The system is evaluated under the influence of Additive White Gaussian Noise (AWGN) with Signal-to-Noise Ratios (SNR) set at -5, 5, and 10 dB. The model was trained using the Adam optimizer with a fixed learning rate of 0.00001, mini-batch size of 32, and four epochs. The reported overall accuracies are 75.00%, 87.73%, and 99.68% at − 5, 5, and 10 dB, respectively. CPFSK accuracy is 0% at − 5 and 5 dB and 100% at 10 dB. These results demonstrate strong performance on the evaluated MATLAB/AWGN test configuration, but they do not establish robustness under multipath fading, Doppler, timing offsets, non-Gaussian noise, alternative train/test splits, alternative optimizers or learning-rate schedules, or alternative time–frequency representations. In addition, convergence beyond four epochs, CNN-only/LSTM-only ablations, STFT sensitivity, model parameter count, and inference latency were not experimentally evaluated. Therefore, the reported results should be interpreted as dataset- and channel-specific evidence rather than as a controlled claim of superiority over other AMC approaches.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Ola Fadhil Obead"
    },
    {
      "name": "A. M. El-Assy"
    },
    {
      "name": "Hossam El-Din Moustafa"
    },
    {
      "name": "Hala B. Nafea"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fes8ggk6rzrwy.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74229-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-74229-5",
  "id": "fes8ggk6rzrwy",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74229-5",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.nature.com/articles/s41598-026-74229-5.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/fes8ggk6rzrwy.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:13Z",
      "native_id": "10.1038/s41598-026-74229-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74229-5"
    },
    {
      "first_seen": "2026-11-08T14:20:57Z",
      "native_id": "089a2119f2d94f8a94d25cc6204a7415",
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      "url": "https://doi.org/10.1038/s41598-026-74229-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Robust spectrogram-based automatic modulation classification via deep CNN-LSTM architecture",
  "title_text": "Robust spectrogram-based automatic modulation classification via deep CNN-LSTM architecture",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T14:21:17Z",
  "url": "https://research.draconitic.com/paper/fes8ggk6rzrwy",
  "venue": "Scientific Reports",
  "versions": []
}