On the Optimum Number of Coefficients of Sparse Digital Predistorters: A Bayesian Approach
This work presents insights on the application of the Bayesian information criterion (BIC) to fix the optimum number of coefficients in the Volterra series applied to the modeling and linearization of power amplifiers (PAs). The BIC is transformed from a rule to be applied after selection techniques...
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| Veröffentlicht in: | IEEE microwave and wireless components letters 2020-12, Vol.30 (12), p.1117-1120 |
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| container_title | IEEE microwave and wireless components letters |
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| creator | Becerra, Juan A. Madero-Ayora, Maria Jose Noguer, Rafael G. Crespo-Cadenas, Carlos |
| description | This work presents insights on the application of the Bayesian information criterion (BIC) to fix the optimum number of coefficients in the Volterra series applied to the modeling and linearization of power amplifiers (PAs). The BIC is transformed from a rule to be applied after selection techniques to a stopping criterion, which enables the halting of the algorithm when a condition is reached. This study reveals that the BIC is equivalent to allow a certain identification normalized mean square error (NMSE) decrease after the inclusion of a model component. Experimental results of the digital predistortion of a class J PA are provided, demonstrating the proposal applicability in the attaining of the optimum number of coefficients. A comparison is made between the results obtained when the stopping rule is applied to the hill climbing (HC) and the doubly orthogonal matching pursuit (DOMP) algorithms. |
| doi_str_mv | 10.1109/LMWC.2020.3027878 |
| format | Article |
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The BIC is transformed from a rule to be applied after selection techniques to a stopping criterion, which enables the halting of the algorithm when a condition is reached. This study reveals that the BIC is equivalent to allow a certain identification normalized mean square error (NMSE) decrease after the inclusion of a model component. Experimental results of the digital predistortion of a class J PA are provided, demonstrating the proposal applicability in the attaining of the optimum number of coefficients. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-7c15b8989cfd16419f2fc2125b1f9ca3f4d1c740bd03bf4bf2a8141e8d6aebe73</citedby><cites>FETCH-LOGICAL-c293t-7c15b8989cfd16419f2fc2125b1f9ca3f4d1c740bd03bf4bf2a8141e8d6aebe73</cites><orcidid>0000-0002-4351-7830 ; 0000-0001-6614-2771 ; 0000-0003-0879-5891</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9222205$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9222205$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Becerra, Juan A.</creatorcontrib><creatorcontrib>Madero-Ayora, Maria Jose</creatorcontrib><creatorcontrib>Noguer, Rafael G.</creatorcontrib><creatorcontrib>Crespo-Cadenas, Carlos</creatorcontrib><title>On the Optimum Number of Coefficients of Sparse Digital Predistorters: A Bayesian Approach</title><title>IEEE microwave and wireless components letters</title><addtitle>LMWC</addtitle><description>This work presents insights on the application of the Bayesian information criterion (BIC) to fix the optimum number of coefficients in the Volterra series applied to the modeling and linearization of power amplifiers (PAs). 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A comparison is made between the results obtained when the stopping rule is applied to the hill climbing (HC) and the doubly orthogonal matching pursuit (DOMP) algorithms.</description><subject>Algorithms</subject><subject>Bayes methods</subject><subject>Bayesian analysis</subject><subject>Bayesian information criterion (BIC)</subject><subject>Coefficients</subject><subject>Cost function</subject><subject>Criteria</subject><subject>digital predistortion (DPD)</subject><subject>doubly orthogonal matching pursuit (DOMP)</subject><subject>hill climbing (HC) algorithm</subject><subject>Matched pursuit</subject><subject>Matching pursuit algorithms</subject><subject>Mathematical models</subject><subject>order reduction</subject><subject>Power amplfiers</subject><subject>power amplifier (PA)</subject><subject>Power amplifiers</subject><subject>Predistortion</subject><issn>1531-1309</issn><issn>2771-957X</issn><issn>1558-1764</issn><issn>2771-9588</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LxDAQxYsouK7-AeIl4LlrJk3bxNu6fsLqCiqCl5CmEzfL9sOkPex_b8uKc5l58N48-EXROdAZAJVXy-fPxYxRRmcJZbnIxUE0gTQVMeQZPxzvBGJIqDyOTkLYUApccJhEX6uadGskq7ZzVV-Rl74q0JPGkkWD1jrjsO7CqN9a7QOSW_ftOr0lrx5LF7rGd-jDNZmTG73D4HRN5m3rG23Wp9GR1duAZ397Gn3c370vHuPl6uFpMV_Ghsmki3MDaSGkkMaWkHGQllnDgKUFWGl0YnkJJue0KGlSWF5YpgVwQFFmGgvMk2l0uf871P70GDq1aXpfD5WK8UzkKacCBhfsXcY3IXi0qvWu0n6ngKoRoRoRqhGh-kM4ZC72GYeI_37JhqFp8gupmG0Z</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Becerra, Juan A.</creator><creator>Madero-Ayora, Maria Jose</creator><creator>Noguer, Rafael G.</creator><creator>Crespo-Cadenas, Carlos</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-4351-7830</orcidid><orcidid>https://orcid.org/0000-0001-6614-2771</orcidid><orcidid>https://orcid.org/0000-0003-0879-5891</orcidid></search><sort><creationdate>20201201</creationdate><title>On the Optimum Number of Coefficients of Sparse Digital Predistorters: A Bayesian Approach</title><author>Becerra, Juan A. ; Madero-Ayora, Maria Jose ; Noguer, Rafael G. ; Crespo-Cadenas, Carlos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-7c15b8989cfd16419f2fc2125b1f9ca3f4d1c740bd03bf4bf2a8141e8d6aebe73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Bayes methods</topic><topic>Bayesian analysis</topic><topic>Bayesian information criterion (BIC)</topic><topic>Coefficients</topic><topic>Cost function</topic><topic>Criteria</topic><topic>digital predistortion (DPD)</topic><topic>doubly orthogonal matching pursuit (DOMP)</topic><topic>hill climbing (HC) algorithm</topic><topic>Matched pursuit</topic><topic>Matching pursuit algorithms</topic><topic>Mathematical models</topic><topic>order reduction</topic><topic>Power amplfiers</topic><topic>power amplifier (PA)</topic><topic>Power amplifiers</topic><topic>Predistortion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Becerra, Juan A.</creatorcontrib><creatorcontrib>Madero-Ayora, Maria Jose</creatorcontrib><creatorcontrib>Noguer, Rafael G.</creatorcontrib><creatorcontrib>Crespo-Cadenas, Carlos</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE microwave and wireless components letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Becerra, Juan A.</au><au>Madero-Ayora, Maria Jose</au><au>Noguer, Rafael G.</au><au>Crespo-Cadenas, Carlos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the Optimum Number of Coefficients of Sparse Digital Predistorters: A Bayesian Approach</atitle><jtitle>IEEE microwave and wireless components letters</jtitle><stitle>LMWC</stitle><date>2020-12-01</date><risdate>2020</risdate><volume>30</volume><issue>12</issue><spage>1117</spage><epage>1120</epage><pages>1117-1120</pages><issn>1531-1309</issn><issn>2771-957X</issn><eissn>1558-1764</eissn><eissn>2771-9588</eissn><coden>IMWCBJ</coden><abstract>This work presents insights on the application of the Bayesian information criterion (BIC) to fix the optimum number of coefficients in the Volterra series applied to the modeling and linearization of power amplifiers (PAs). The BIC is transformed from a rule to be applied after selection techniques to a stopping criterion, which enables the halting of the algorithm when a condition is reached. This study reveals that the BIC is equivalent to allow a certain identification normalized mean square error (NMSE) decrease after the inclusion of a model component. Experimental results of the digital predistortion of a class J PA are provided, demonstrating the proposal applicability in the attaining of the optimum number of coefficients. A comparison is made between the results obtained when the stopping rule is applied to the hill climbing (HC) and the doubly orthogonal matching pursuit (DOMP) algorithms.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LMWC.2020.3027878</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-4351-7830</orcidid><orcidid>https://orcid.org/0000-0001-6614-2771</orcidid><orcidid>https://orcid.org/0000-0003-0879-5891</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Bayes methods Bayesian analysis Bayesian information criterion (BIC) Coefficients Cost function Criteria digital predistortion (DPD) doubly orthogonal matching pursuit (DOMP) hill climbing (HC) algorithm Matched pursuit Matching pursuit algorithms Mathematical models order reduction Power amplfiers power amplifier (PA) Power amplifiers Predistortion |
| title | On the Optimum Number of Coefficients of Sparse Digital Predistorters: A Bayesian Approach |
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