Dynamic spectrum access with learning for cognitive radio
We study the problem of cooperative dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). Assuming Markovian state-evolutions for the primary channels, we propose a greedy channel selection and access policy that satisfies an interfer...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 107 |
|---|---|
| container_issue | |
| container_start_page | 103 |
| container_title | |
| container_volume | |
| creator | Unnikrishnan, J. Veeravalli, V.V. |
| description | We study the problem of cooperative dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). Assuming Markovian state-evolutions for the primary channels, we propose a greedy channel selection and access policy that satisfies an interference constraint and also outperforms some existing schemes in average throughput. When the distribution of the signal from the primary is unknown and belongs to a parameterized family, we develop an algorithm that can learn the parameter of the distribution still guaranteeing the interference constraint. This algorithm also outperforms the popular approach that assumes a worst-case value for the parameter thus illustrating the sub-optimality of the popular worst-case approach. |
| doi_str_mv | 10.1109/ACSSC.2008.5074371 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5074371</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5074371</ieee_id><sourcerecordid>5074371</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-a9ca3a64f69b690989572ae8b7f9e51b796d454206f7f62fecc113d0d52e95fe3</originalsourceid><addsrcrecordid>eNo1kMlOwzAURc0kUUp_ADb-gYTn2W9ZhTJIlVgU1pXjPBejNqmSAOrfg0S5m7M40llcxm4ElEIA3s2r1aoqJYAvDTitnDhhV0JLrSVqIU_ZRBpnC6lAnbEZOv_vAM7ZRIDxhVWoLtlsGD7gd9ooj37C8P7Qhl2OfNhTHPvPHQ8x0jDw7zy-8y2Fvs3thqeu57HbtHnMX8T70OTuml2ksB1oduSUvT0sXqunYvny-FzNl0UWzoxFwBhUsDpZrC0CejROBvK1S0hG1A5to42WYJNLViaKUQjVQGMkoUmkpuz2r5uJaL3v8y70h_XxBfUD7xpMBg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Dynamic spectrum access with learning for cognitive radio</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Unnikrishnan, J. ; Veeravalli, V.V.</creator><creatorcontrib>Unnikrishnan, J. ; Veeravalli, V.V.</creatorcontrib><description>We study the problem of cooperative dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). Assuming Markovian state-evolutions for the primary channels, we propose a greedy channel selection and access policy that satisfies an interference constraint and also outperforms some existing schemes in average throughput. When the distribution of the signal from the primary is unknown and belongs to a parameterized family, we develop an algorithm that can learn the parameter of the distribution still guaranteeing the interference constraint. This algorithm also outperforms the popular approach that assumes a worst-case value for the parameter thus illustrating the sub-optimality of the popular worst-case approach.</description><identifier>ISSN: 1058-6393</identifier><identifier>ISBN: 9781424429400</identifier><identifier>ISBN: 1424429404</identifier><identifier>EISSN: 2576-2303</identifier><identifier>EISBN: 1424429412</identifier><identifier>EISBN: 9781424429417</identifier><identifier>DOI: 10.1109/ACSSC.2008.5074371</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bandwidth ; Cognitive radio ; Condition monitoring ; Interference constraints ; Probability ; Radio transmitters ; Statistics ; Throughput ; Uncertainty</subject><ispartof>2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008, p.103-107</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5074371$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5074371$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Unnikrishnan, J.</creatorcontrib><creatorcontrib>Veeravalli, V.V.</creatorcontrib><title>Dynamic spectrum access with learning for cognitive radio</title><title>2008 42nd Asilomar Conference on Signals, Systems and Computers</title><addtitle>ACSSC</addtitle><description>We study the problem of cooperative dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). Assuming Markovian state-evolutions for the primary channels, we propose a greedy channel selection and access policy that satisfies an interference constraint and also outperforms some existing schemes in average throughput. When the distribution of the signal from the primary is unknown and belongs to a parameterized family, we develop an algorithm that can learn the parameter of the distribution still guaranteeing the interference constraint. This algorithm also outperforms the popular approach that assumes a worst-case value for the parameter thus illustrating the sub-optimality of the popular worst-case approach.</description><subject>Bandwidth</subject><subject>Cognitive radio</subject><subject>Condition monitoring</subject><subject>Interference constraints</subject><subject>Probability</subject><subject>Radio transmitters</subject><subject>Statistics</subject><subject>Throughput</subject><subject>Uncertainty</subject><issn>1058-6393</issn><issn>2576-2303</issn><isbn>9781424429400</isbn><isbn>1424429404</isbn><isbn>1424429412</isbn><isbn>9781424429417</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kMlOwzAURc0kUUp_ADb-gYTn2W9ZhTJIlVgU1pXjPBejNqmSAOrfg0S5m7M40llcxm4ElEIA3s2r1aoqJYAvDTitnDhhV0JLrSVqIU_ZRBpnC6lAnbEZOv_vAM7ZRIDxhVWoLtlsGD7gd9ooj37C8P7Qhl2OfNhTHPvPHQ8x0jDw7zy-8y2Fvs3thqeu57HbtHnMX8T70OTuml2ksB1oduSUvT0sXqunYvny-FzNl0UWzoxFwBhUsDpZrC0CejROBvK1S0hG1A5to42WYJNLViaKUQjVQGMkoUmkpuz2r5uJaL3v8y70h_XxBfUD7xpMBg</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Unnikrishnan, J.</creator><creator>Veeravalli, V.V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200810</creationdate><title>Dynamic spectrum access with learning for cognitive radio</title><author>Unnikrishnan, J. ; Veeravalli, V.V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a9ca3a64f69b690989572ae8b7f9e51b796d454206f7f62fecc113d0d52e95fe3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Bandwidth</topic><topic>Cognitive radio</topic><topic>Condition monitoring</topic><topic>Interference constraints</topic><topic>Probability</topic><topic>Radio transmitters</topic><topic>Statistics</topic><topic>Throughput</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Unnikrishnan, J.</creatorcontrib><creatorcontrib>Veeravalli, V.V.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Unnikrishnan, J.</au><au>Veeravalli, V.V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Dynamic spectrum access with learning for cognitive radio</atitle><btitle>2008 42nd Asilomar Conference on Signals, Systems and Computers</btitle><stitle>ACSSC</stitle><date>2008-10</date><risdate>2008</risdate><spage>103</spage><epage>107</epage><pages>103-107</pages><issn>1058-6393</issn><eissn>2576-2303</eissn><isbn>9781424429400</isbn><isbn>1424429404</isbn><eisbn>1424429412</eisbn><eisbn>9781424429417</eisbn><abstract>We study the problem of cooperative dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). Assuming Markovian state-evolutions for the primary channels, we propose a greedy channel selection and access policy that satisfies an interference constraint and also outperforms some existing schemes in average throughput. When the distribution of the signal from the primary is unknown and belongs to a parameterized family, we develop an algorithm that can learn the parameter of the distribution still guaranteeing the interference constraint. This algorithm also outperforms the popular approach that assumes a worst-case value for the parameter thus illustrating the sub-optimality of the popular worst-case approach.</abstract><pub>IEEE</pub><doi>10.1109/ACSSC.2008.5074371</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1058-6393 |
| ispartof | 2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008, p.103-107 |
| issn | 1058-6393 2576-2303 |
| language | eng |
| recordid | cdi_ieee_primary_5074371 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bandwidth Cognitive radio Condition monitoring Interference constraints Probability Radio transmitters Statistics Throughput Uncertainty |
| title | Dynamic spectrum access with learning for cognitive radio |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T11%3A27%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Dynamic%20spectrum%20access%20with%20learning%20for%20cognitive%20radio&rft.btitle=2008%2042nd%20Asilomar%20Conference%20on%20Signals,%20Systems%20and%20Computers&rft.au=Unnikrishnan,%20J.&rft.date=2008-10&rft.spage=103&rft.epage=107&rft.pages=103-107&rft.issn=1058-6393&rft.eissn=2576-2303&rft.isbn=9781424429400&rft.isbn_list=1424429404&rft_id=info:doi/10.1109/ACSSC.2008.5074371&rft_dat=%3Cieee_6IE%3E5074371%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424429412&rft.eisbn_list=9781424429417&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5074371&rfr_iscdi=true |