b075a7853eca63a410f3 · A Machine Learning Based Scheme for Dynamic Spectrum Access
A Machine Learning Based Scheme for Dynamic Spectrum Access
In this paper, we present a machine learning (ML) based dynamic spectrum access (DSA) scheme which can be used in a system in which primary user (PU) spectrum occupancy can be represented as a sequence of busy (on) and idle (off) periods. We use real world
{
"title_en": "A Machine Learning Based Scheme for Dynamic Spectrum Access",
"designation": "",
"series": "",
"status": "",
"date": "2021-03-30",
"date_original": "March 30, 2021",
"url": "https://www.nist.gov/publications/machine-learning-based-scheme-dynamic-spectrum-access",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Anirudha Sahoo",
"abstract_excerpt_en": "In this paper, we present a machine learning (ML) based dynamic spectrum access (DSA) scheme which can be used in a system in which primary user (PU) spectrum occupancy can be represented as a sequence of busy (on) and idle (off) periods. We use real world",
"id": "b075a7853eca63a410f3",
"sources": [
"NIST Publications"
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{
"title_en": "A Machine Learning Based Scheme for Dynamic Spectrum Access",
"designation": "",
"series": "",
"status": "",
"date": "2021-03-30",
"date_original": "March 30, 2021",
"url": "https://www.nist.gov/publications/machine-learning-based-scheme-dynamic-spectrum-access",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Anirudha Sahoo",
"abstract_excerpt_en": "In this paper, we present a machine learning (ML) based dynamic spectrum access (DSA) scheme which can be used in a system in which primary user (PU) spectrum occupancy can be represented as a sequence of busy (on) and idle (off) periods. We use real world"
}
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