4789106d9d4dbe83ce41 · Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band

{
  "title_en": "Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2022-05-19",
  "date_original": "May 19, 2022",
  "url": "https://www.nist.gov/publications/deep-learning-path-loss-prediction-35-ghz-cbrs-spectrum-band",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Thao T. Nguyen , Raied Caromi , Kassem Kallas",
  "abstract_excerpt_en": "In the 3.5 GHz citizen broadband radio service (CBRS) band, accurate path loss prediction is very important to protect the incumbent from harmful interference caused by the lower tier users. Within the current CBRS standards developed by the Wireless",
  "id": "4789106d9d4dbe83ce41",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2022-05-19",
      "date_original": "May 19, 2022",
      "url": "https://www.nist.gov/publications/deep-learning-path-loss-prediction-35-ghz-cbrs-spectrum-band",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Thao T. Nguyen , Raied Caromi , Kassem Kallas",
      "abstract_excerpt_en": "In the 3.5 GHz citizen broadband radio service (CBRS) band, accurate path loss prediction is very important to protect the incumbent from harmful interference caused by the lower tier users. Within the current CBRS standards developed by the Wireless"
    }
  ],
  "translation_state": "none"
}