7182d40a24f7081d65c5 · Deep Learning for MPC Extraction and False Detection

{
  "title_en": "Deep Learning for MPC Extraction and False Detection",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2025-05-28",
  "date_original": "May 28, 2025",
  "url": "https://www.nist.gov/publications/deep-learning-mpc-extraction-and-false-detection",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Howard Dai, Jack Chuang , Jian Wang , Samuel Berweger , David Griffith",
  "abstract_excerpt_en": "Multipath component (MPC) extraction is critical for channel modeling and joint communications and sensing (JCAS). The super-resolution algorithm known as CLEAN-Space-Alternating Generalized Expectation (CLEANSAGE) is widely used for MPC extraction",
  "id": "7182d40a24f7081d65c5",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Deep Learning for MPC Extraction and False Detection",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2025-05-28",
      "date_original": "May 28, 2025",
      "url": "https://www.nist.gov/publications/deep-learning-mpc-extraction-and-false-detection",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Howard Dai, Jack Chuang , Jian Wang , Samuel Berweger , David Griffith",
      "abstract_excerpt_en": "Multipath component (MPC) extraction is critical for channel modeling and joint communications and sensing (JCAS). The super-resolution algorithm known as CLEAN-Space-Alternating Generalized Expectation (CLEANSAGE) is widely used for MPC extraction"
    }
  ],
  "translation_state": "none"
}