Approximations for the product, ratio, and sum of α‐μ random variables with application in the analysis of cognitive radio networks
Summary Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters...
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| Veröffentlicht in: | International journal of communication systems 2021-05, Vol.34 (7), p.n/a |
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| container_title | International journal of communication systems |
| container_volume | 34 |
| creator | Leonardo, Elvio J. Mafra, Samuel B. Montejo‐Sánchez, Samuel Fernández, Evelio M. G. |
| description | Summary
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters of the involved α‐μ distributions. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network. |
| doi_str_mv | 10.1002/dac.4756 |
| format | Article |
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Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters of the involved α‐μ distributions. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.</description><identifier>ISSN: 1074-5351</identifier><identifier>EISSN: 1099-1131</identifier><identifier>DOI: 10.1002/dac.4756</identifier><language>eng</language><publisher>Chichester: Wiley Subscription Services, Inc</publisher><subject>Approximation ; Cognitive radio ; Energy harvesting ; Probability density functions ; product of random variables ; Random variables ; ratio of random variables ; sum of random variables ; α‐μ distribution</subject><ispartof>International journal of communication systems, 2021-05, Vol.34 (7), p.n/a</ispartof><rights>2021 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2936-ee54da3c82b08e0151ee29f7966e8bfb75e46b73f6890d99d97bb126526280af3</citedby><cites>FETCH-LOGICAL-c2936-ee54da3c82b08e0151ee29f7966e8bfb75e46b73f6890d99d97bb126526280af3</cites><orcidid>0000-0001-7958-7569 ; 0000-0002-5238-1795</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fdac.4756$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fdac.4756$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27926,27927,45576,45577</link.rule.ids></links><search><creatorcontrib>Leonardo, Elvio J.</creatorcontrib><creatorcontrib>Mafra, Samuel B.</creatorcontrib><creatorcontrib>Montejo‐Sánchez, Samuel</creatorcontrib><creatorcontrib>Fernández, Evelio M. G.</creatorcontrib><title>Approximations for the product, ratio, and sum of α‐μ random variables with application in the analysis of cognitive radio networks</title><title>International journal of communication systems</title><description>Summary
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters of the involved α‐μ distributions. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.</description><subject>Approximation</subject><subject>Cognitive radio</subject><subject>Energy harvesting</subject><subject>Probability density functions</subject><subject>product of random variables</subject><subject>Random variables</subject><subject>ratio of random variables</subject><subject>sum of random variables</subject><subject>α‐μ distribution</subject><issn>1074-5351</issn><issn>1099-1131</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kL9OwzAQxi0EEqUg8QiWWBhIsZ3Yiceq_JUqscBsOYlNXdI42ElLNzZWXoWdZ-hD8CQ4LSvTne773ae7D4BTjEYYIXJZymKUpJTtgQFGnEcYx3i_79MkojHFh-DI-zlCKCOMDsDHuGmcfTML2Rpbe6itg-1MwTAsu6K9gK4XLqCsS-i7BbQabr5-3j8330GpS7uAS-mMzCvl4cq0MyibpjLF1g2aeusla1mtvfH9cmGfa9OapQrrpbGwVu3Kuhd_DA60rLw6-atD8HRz_Ti5i6YPt_eT8TQqCI9ZpBRNShkXGclRphCmWCnCdcoZU1mu85SqhOVprFnGUcl5ydM8x-FTwkiGpI6H4GznGx587ZRvxdx2LhzoBaGIExwnNAnU-Y4qnPXeKS0aFyJya4GR6GMWIWbRxxzQaIeuTKXW_3LiajzZ8r8u24Ic</recordid><startdate>20210510</startdate><enddate>20210510</enddate><creator>Leonardo, Elvio J.</creator><creator>Mafra, Samuel B.</creator><creator>Montejo‐Sánchez, Samuel</creator><creator>Fernández, Evelio M. G.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-7958-7569</orcidid><orcidid>https://orcid.org/0000-0002-5238-1795</orcidid></search><sort><creationdate>20210510</creationdate><title>Approximations for the product, ratio, and sum of α‐μ random variables with application in the analysis of cognitive radio networks</title><author>Leonardo, Elvio J. ; Mafra, Samuel B. ; Montejo‐Sánchez, Samuel ; Fernández, Evelio M. G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2936-ee54da3c82b08e0151ee29f7966e8bfb75e46b73f6890d99d97bb126526280af3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Approximation</topic><topic>Cognitive radio</topic><topic>Energy harvesting</topic><topic>Probability density functions</topic><topic>product of random variables</topic><topic>Random variables</topic><topic>ratio of random variables</topic><topic>sum of random variables</topic><topic>α‐μ distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leonardo, Elvio J.</creatorcontrib><creatorcontrib>Mafra, Samuel B.</creatorcontrib><creatorcontrib>Montejo‐Sánchez, Samuel</creatorcontrib><creatorcontrib>Fernández, Evelio M. G.</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of communication systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leonardo, Elvio J.</au><au>Mafra, Samuel B.</au><au>Montejo‐Sánchez, Samuel</au><au>Fernández, Evelio M. G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Approximations for the product, ratio, and sum of α‐μ random variables with application in the analysis of cognitive radio networks</atitle><jtitle>International journal of communication systems</jtitle><date>2021-05-10</date><risdate>2021</risdate><volume>34</volume><issue>7</issue><epage>n/a</epage><issn>1074-5351</issn><eissn>1099-1131</eissn><abstract>Summary
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters of the involved α‐μ distributions. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.
Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.</abstract><cop>Chichester</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/dac.4756</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-7958-7569</orcidid><orcidid>https://orcid.org/0000-0002-5238-1795</orcidid></addata></record> |
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| language | eng |
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| source | Access via Wiley Online Library |
| subjects | Approximation Cognitive radio Energy harvesting Probability density functions product of random variables Random variables ratio of random variables sum of random variables α‐μ distribution |
| title | Approximations for the product, ratio, and sum of α‐μ random variables with application in the analysis of cognitive radio networks |
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