{
  "abstract": "Dependable data transmission is vital for modern Smart Grid operations, facilitating essential services like demand-side management and Advanced Metering Infrastructure (AMI). Within these networks, Automatic Modulation Recognition (AMR) acts as a crucial technique for blind signal processing and system security. However, the highly volatile nature of Power Line Communication (PLC) channels severely restricts AMR performance. Traditional statistical algorithms generally fail under severe noise conditions (low SNR), whereas standard single-stage deep learning architectures lack transparency and struggle to distinguish highly similar modulation schemes. To overcome these limitations, we introduce an innovative two-tier recognition architecture that integrates statistical feature engineering with cutting-edge neural networks. The preliminary stage executes coarse classification by feeding Higher-Order Cumulants (HOCs) into a Transformer module, effectively categorizing signals into general modulation families (e.g., PSK or QAM). Here, the Transformer’s self-attention mechanism excels at identifying complex correlations among statistical parameters. Subsequently, based on the preliminary result, a specialized Liquid Neural Network (LNN) is deployed for fine-grained analysis. By processing 3D constellation matrices, these specialized LNN models pinpoint the exact modulation order. Their inherent continuous-time properties provide improved adaptability to the temporal fluctuations and heavy noise typical of PLC environments, as evidenced by our low-SNR evaluations. Experimental evaluations confirm that the introduced hierarchical strategy yields markedly higher accuracy rates than traditional algorithms and unified deep learning baseline models, especially in high-noise scenarios. Ultimately, this structured design delivers a highly transparent and robust solution for AMR in challenging communication setups.",
  "abstract_shown": true,
  "affiliations": [
    "Power Dispatching and Control Center, China Southern Power Grid (CSG), Guangzhou, Guangdong, China",
    "College of Electronics and Information Technology, South China University of Technology, Guangzhou, Guangdong, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "FeiFei Hu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Huang"
    },
    {
      "name": "Xubin Lin"
    },
    {
      "name": "Guanhuan Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ft0x71t0k7re.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359526",
  "fulltext_url": null,
  "id": "1ft0x71t0k7re",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359526",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Wireless Signal Modulation Classification"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Background signal noise"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Signaling networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Power distribution/Power grids"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Background signal noise"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal to noise ratio"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical noise/Gaussian noise"
    ]
  ],
  "openalex": "W7223661057",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359526&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1ft0x71t0k7re.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0359526",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359526"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistics",
      "subfield": "statistics"
    }
  ],
  "title": "A coarse-to-fine modulation classification scheme utilizing Transformers and Liquid Neural Networks for Smart Grids",
  "title_text": "A coarse-to-fine modulation classification scheme utilizing Transformers and Liquid Neural Networks for Smart Grids",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/1ft0x71t0k7re",
  "venue": "PLOS One",
  "versions": []
}