Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of signal processing systems 2011-04, Vol.63 (1), p.1-12
Hauptverfasser: Nassar, Marcel, Gulati, Kapil, DeYoung, Marcus R., Evans, Brian L., Tinsley, Keith R.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 12
container_issue 1
container_start_page 1
container_title Journal of signal processing systems
container_volume 63
creator Nassar, Marcel
Gulati, Kapil
DeYoung, Marcus R.
Evans, Brian L.
Tinsley, Keith R.
description In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10  − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable ( S αS ) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as S αS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.
doi_str_mv 10.1007/s11265-009-0350-7
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671340131</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1671340131</sourcerecordid><originalsourceid>FETCH-LOGICAL-c364t-edc81e46186998531ce4ea34cf8f16a2347ce5231f55753027151015d3dd4f933</originalsourceid><addsrcrecordid>eNp9kD1PwzAQhi0EEuXjB7BlZDH4YjtORlQVqCiwFDFaxj5XrlIn2CkS_55UgZXpbnifV3cPIVfAboAxdZsBykpSxhrKuGRUHZEZNLyhNYA8_tsZ1KfkLOctYxVTEmbk6TkMYWOGEDfFC5pEfcDWFcs4YPKYMFosQixWph-6vljsPtA5dMV7SNhizsU6mZgthi9M-YKceNNmvPyd5-TtfrGeP9LV68NyfreilldioOhsDSgqqKumqSUHiwINF9bXHipTcqEsypKDl1JJzkoFEhhIx50TvuH8nFxPvX3qPveYB70L4w1tayJ2-6yhUsAFAw5jFKaoTV3OCb3uU9iZ9K2B6YM4PYnTozh9EKfVyJQTk8ds3GDS226f4vjRP9APEAtveg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1671340131</pqid></control><display><type>article</type><title>Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers</title><source>SpringerLink Journals - AutoHoldings</source><creator>Nassar, Marcel ; Gulati, Kapil ; DeYoung, Marcus R. ; Evans, Brian L. ; Tinsley, Keith R.</creator><creatorcontrib>Nassar, Marcel ; Gulati, Kapil ; DeYoung, Marcus R. ; Evans, Brian L. ; Tinsley, Keith R.</creatorcontrib><description>In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10  − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable ( S αS ) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as S αS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.</description><identifier>ISSN: 1939-8018</identifier><identifier>EISSN: 1939-8115</identifier><identifier>DOI: 10.1007/s11265-009-0350-7</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Circuits and Systems ; Complexity ; Computer Imaging ; Computer simulation ; Electrical Engineering ; Engineering ; Filtering ; Filtration ; Image Processing and Computer Vision ; Interference ; Laptop ; Mathematical models ; Pattern Recognition ; Pattern Recognition and Graphics ; Signal,Image and Speech Processing ; Transceivers ; Vision</subject><ispartof>Journal of signal processing systems, 2011-04, Vol.63 (1), p.1-12</ispartof><rights>Springer Science+Business Media, LLC 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-edc81e46186998531ce4ea34cf8f16a2347ce5231f55753027151015d3dd4f933</citedby><cites>FETCH-LOGICAL-c364t-edc81e46186998531ce4ea34cf8f16a2347ce5231f55753027151015d3dd4f933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11265-009-0350-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11265-009-0350-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Nassar, Marcel</creatorcontrib><creatorcontrib>Gulati, Kapil</creatorcontrib><creatorcontrib>DeYoung, Marcus R.</creatorcontrib><creatorcontrib>Evans, Brian L.</creatorcontrib><creatorcontrib>Tinsley, Keith R.</creatorcontrib><title>Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers</title><title>Journal of signal processing systems</title><addtitle>J Sign Process Syst</addtitle><description>In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10  − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable ( S αS ) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as S αS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.</description><subject>Circuits and Systems</subject><subject>Complexity</subject><subject>Computer Imaging</subject><subject>Computer simulation</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Filtering</subject><subject>Filtration</subject><subject>Image Processing and Computer Vision</subject><subject>Interference</subject><subject>Laptop</subject><subject>Mathematical models</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Signal,Image and Speech Processing</subject><subject>Transceivers</subject><subject>Vision</subject><issn>1939-8018</issn><issn>1939-8115</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEuXjB7BlZDH4YjtORlQVqCiwFDFaxj5XrlIn2CkS_55UgZXpbnifV3cPIVfAboAxdZsBykpSxhrKuGRUHZEZNLyhNYA8_tsZ1KfkLOctYxVTEmbk6TkMYWOGEDfFC5pEfcDWFcs4YPKYMFosQixWph-6vljsPtA5dMV7SNhizsU6mZgthi9M-YKceNNmvPyd5-TtfrGeP9LV68NyfreilldioOhsDSgqqKumqSUHiwINF9bXHipTcqEsypKDl1JJzkoFEhhIx50TvuH8nFxPvX3qPveYB70L4w1tayJ2-6yhUsAFAw5jFKaoTV3OCb3uU9iZ9K2B6YM4PYnTozh9EKfVyJQTk8ds3GDS226f4vjRP9APEAtveg</recordid><startdate>20110401</startdate><enddate>20110401</enddate><creator>Nassar, Marcel</creator><creator>Gulati, Kapil</creator><creator>DeYoung, Marcus R.</creator><creator>Evans, Brian L.</creator><creator>Tinsley, Keith R.</creator><general>Springer US</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110401</creationdate><title>Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers</title><author>Nassar, Marcel ; Gulati, Kapil ; DeYoung, Marcus R. ; Evans, Brian L. ; Tinsley, Keith R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-edc81e46186998531ce4ea34cf8f16a2347ce5231f55753027151015d3dd4f933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Circuits and Systems</topic><topic>Complexity</topic><topic>Computer Imaging</topic><topic>Computer simulation</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Filtering</topic><topic>Filtration</topic><topic>Image Processing and Computer Vision</topic><topic>Interference</topic><topic>Laptop</topic><topic>Mathematical models</topic><topic>Pattern Recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Signal,Image and Speech Processing</topic><topic>Transceivers</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nassar, Marcel</creatorcontrib><creatorcontrib>Gulati, Kapil</creatorcontrib><creatorcontrib>DeYoung, Marcus R.</creatorcontrib><creatorcontrib>Evans, Brian L.</creatorcontrib><creatorcontrib>Tinsley, Keith R.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of signal processing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nassar, Marcel</au><au>Gulati, Kapil</au><au>DeYoung, Marcus R.</au><au>Evans, Brian L.</au><au>Tinsley, Keith R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers</atitle><jtitle>Journal of signal processing systems</jtitle><stitle>J Sign Process Syst</stitle><date>2011-04-01</date><risdate>2011</risdate><volume>63</volume><issue>1</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1939-8018</issn><eissn>1939-8115</eissn><abstract>In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10  − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable ( S αS ) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as S αS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11265-009-0350-7</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1939-8018
ispartof Journal of signal processing systems, 2011-04, Vol.63 (1), p.1-12
issn 1939-8018
1939-8115
language eng
recordid cdi_proquest_miscellaneous_1671340131
source SpringerLink Journals - AutoHoldings
subjects Circuits and Systems
Complexity
Computer Imaging
Computer simulation
Electrical Engineering
Engineering
Filtering
Filtration
Image Processing and Computer Vision
Interference
Laptop
Mathematical models
Pattern Recognition
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Transceivers
Vision
title Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T19%3A38%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mitigating%20Near-field%20Interference%20in%20Laptop%20Embedded%20Wireless%20Transceivers&rft.jtitle=Journal%20of%20signal%20processing%20systems&rft.au=Nassar,%20Marcel&rft.date=2011-04-01&rft.volume=63&rft.issue=1&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1939-8018&rft.eissn=1939-8115&rft_id=info:doi/10.1007/s11265-009-0350-7&rft_dat=%3Cproquest_cross%3E1671340131%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1671340131&rft_id=info:pmid/&rfr_iscdi=true