Linearization of RF Power Amplifiers in Wideband Communication Systems by Adaptive Indirect Learning Using RPEM Algorithm
This paper proposes a new approach of digital predistortion (DPD) technique based on the adaptive indirect learning architecture (ILA) by using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs) in emerging wideband communication sy...
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| Veröffentlicht in: | Mobile networks and applications 2020-10, Vol.25 (5), p.1988-1997 |
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| container_end_page | 1997 |
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| container_issue | 5 |
| container_start_page | 1988 |
| container_title | Mobile networks and applications |
| container_volume | 25 |
| creator | Le, Duc Han Hoang, Van-Phuc Nguyen, Minh Hong Nguyen, Hien M. Nguyen, Duc Minh |
| description | This paper proposes a new approach of digital predistortion (DPD) technique based on the adaptive indirect learning architecture (ILA) by using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs) in emerging wideband communication systems. In the proposed RPEM-based linearization approach, the forgetting factor varies with time and is less sensitive to noise. Therefore, the predistorter (PD) parameter estimates become more consistent and accurate in steady state so that the mean square errors can be reduced. Both the error vector magnitude (EVM) and the adjacent channel power ratio (ACPR) are used to evaluate the DPD technique in RF PAs employing the proposed linearization. The efficiency validation of the proposed method is based on a simulated PA Wiener model. The simulation results have clarified the improvement of the proposed adaptive ILA-based DPD with RPEM algorithm in terms of both EVM and ACPR. |
| doi_str_mv | 10.1007/s11036-020-01545-z |
| format | Article |
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In the proposed RPEM-based linearization approach, the forgetting factor varies with time and is less sensitive to noise. Therefore, the predistorter (PD) parameter estimates become more consistent and accurate in steady state so that the mean square errors can be reduced. Both the error vector magnitude (EVM) and the adjacent channel power ratio (ACPR) are used to evaluate the DPD technique in RF PAs employing the proposed linearization. The efficiency validation of the proposed method is based on a simulated PA Wiener model. 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Hoang, Van-Phuc ; Nguyen, Minh Hong ; Nguyen, Hien M. ; Nguyen, Duc Minh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-6f0a5c54e3e141f0fcb08b1529d07fd4b344574aafd21712bf552dfbdf3fa033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Broadband</topic><topic>Communications Engineering</topic><topic>Communications systems</topic><topic>Computer Communication Networks</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>IT in Business</topic><topic>Linearization</topic><topic>Machine learning</topic><topic>Networks</topic><topic>Noise sensitivity</topic><topic>Parameter estimation</topic><topic>Parameter sensitivity</topic><topic>Power amplifiers</topic><topic>Radio frequency</topic><topic>Wideband communications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Le, Duc Han</creatorcontrib><creatorcontrib>Hoang, Van-Phuc</creatorcontrib><creatorcontrib>Nguyen, Minh Hong</creatorcontrib><creatorcontrib>Nguyen, Hien M.</creatorcontrib><creatorcontrib>Nguyen, Duc Minh</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Mobile networks and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Le, Duc Han</au><au>Hoang, Van-Phuc</au><au>Nguyen, Minh Hong</au><au>Nguyen, Hien M.</au><au>Nguyen, Duc Minh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Linearization of RF Power Amplifiers in Wideband Communication Systems by Adaptive Indirect Learning Using RPEM Algorithm</atitle><jtitle>Mobile networks and applications</jtitle><stitle>Mobile Netw Appl</stitle><date>2020-10-01</date><risdate>2020</risdate><volume>25</volume><issue>5</issue><spage>1988</spage><epage>1997</epage><pages>1988-1997</pages><issn>1383-469X</issn><eissn>1572-8153</eissn><abstract>This paper proposes a new approach of digital predistortion (DPD) technique based on the adaptive indirect learning architecture (ILA) by using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs) in emerging wideband communication systems. 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| subjects | Adaptive systems Algorithms Broadband Communications Engineering Communications systems Computer Communication Networks Electrical Engineering Engineering IT in Business Linearization Machine learning Networks Noise sensitivity Parameter estimation Parameter sensitivity Power amplifiers Radio frequency Wideband communications |
| title | Linearization of RF Power Amplifiers in Wideband Communication Systems by Adaptive Indirect Learning Using RPEM Algorithm |
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