Bandwidth allocation using machine learning
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status o...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Patent |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Roy, Rajarshi Tang, Yeqing Hu, Bin |
description | Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11503615B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11503615B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11503615B23</originalsourceid><addsrcrecordid>eNrjZNB2SsxLKc9MKclQSMzJyU9OLMnMz1MoLc7MS1fITUzOyMxLVchJTSzKAwrwMLCmJeYUp_JCaW4GRTfXEGcP3dSC_PjU4oLE5NS81JL40GBDQ1MDYzNDUycjY2LUAAB7Oim4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Bandwidth allocation using machine learning</title><source>esp@cenet</source><creator>Roy, Rajarshi ; Tang, Yeqing ; Hu, Bin</creator><creatorcontrib>Roy, Rajarshi ; Tang, Yeqing ; Hu, Bin</creatorcontrib><description>Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; MULTIPLEX COMMUNICATION ; PHYSICS ; TRANSMISSION ; WIRELESS COMMUNICATIONS NETWORKS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221115&DB=EPODOC&CC=US&NR=11503615B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221115&DB=EPODOC&CC=US&NR=11503615B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Roy, Rajarshi</creatorcontrib><creatorcontrib>Tang, Yeqing</creatorcontrib><creatorcontrib>Hu, Bin</creatorcontrib><title>Bandwidth allocation using machine learning</title><description>Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>MULTIPLEX COMMUNICATION</subject><subject>PHYSICS</subject><subject>TRANSMISSION</subject><subject>WIRELESS COMMUNICATIONS NETWORKS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNB2SsxLKc9MKclQSMzJyU9OLMnMz1MoLc7MS1fITUzOyMxLVchJTSzKAwrwMLCmJeYUp_JCaW4GRTfXEGcP3dSC_PjU4oLE5NS81JL40GBDQ1MDYzNDUycjY2LUAAB7Oim4</recordid><startdate>20221115</startdate><enddate>20221115</enddate><creator>Roy, Rajarshi</creator><creator>Tang, Yeqing</creator><creator>Hu, Bin</creator><scope>EVB</scope></search><sort><creationdate>20221115</creationdate><title>Bandwidth allocation using machine learning</title><author>Roy, Rajarshi ; Tang, Yeqing ; Hu, Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11503615B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>MULTIPLEX COMMUNICATION</topic><topic>PHYSICS</topic><topic>TRANSMISSION</topic><topic>WIRELESS COMMUNICATIONS NETWORKS</topic><toplevel>online_resources</toplevel><creatorcontrib>Roy, Rajarshi</creatorcontrib><creatorcontrib>Tang, Yeqing</creatorcontrib><creatorcontrib>Hu, Bin</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Roy, Rajarshi</au><au>Tang, Yeqing</au><au>Hu, Bin</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Bandwidth allocation using machine learning</title><date>2022-11-15</date><risdate>2022</risdate><abstract>Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US11503615B2 |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY MULTIPLEX COMMUNICATION PHYSICS TRANSMISSION WIRELESS COMMUNICATIONS NETWORKS |
title | Bandwidth allocation using machine learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T23%3A06%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Roy,%20Rajarshi&rft.date=2022-11-15&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11503615B2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |