{
  "id": "2011.12288",
  "version": "v1",
  "published": "2020-11-24T18:57:40.000Z",
  "updated": "2020-11-24T18:57:40.000Z",
  "title": "Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)",
  "authors": [
    "Srinivasan Sridharan"
  ],
  "comment": "5G, Machine learning (ML), Self-organizing Networks (SONs), 5G Standalone, Artificial Intelligence (AI)",
  "doi": "10.14445/22312803/IJCTT-V68I11P105",
  "categories": [
    "cs.NI",
    "cs.LG"
  ],
  "abstract": "Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Standalone (SA) system is one of the 5G communication tracks that transforms 4G networking to next-generation technology that is based on mobile applications. The research's main aim is to an overview of machine learning (ML) in 5G standalone core networks. 5G Standalone is considered a key enabler by the service providers as it improves the efficacy of the throughput that edges the network. It also assists in advancing new cellular use cases like ultra-reliable low latency communications (URLLC) that supports combinations of frequencies.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-24T18:57:40.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "self organizing network",
      "machine learning",
      "5g standalone core networks",
      "5g communication tracks",
      "ultra-reliable low latency communications"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}