Sparse system identification over adaptive networks

Sparse system identification over adaptive networks

Recently several algorithms have been developed to exploit the distributive nature of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, none of the proposed algorithms addresses the issue of sparse system identification over an adaptive network. Recently, severa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Bin Saeed, Muhammad O., Sheikh, A. U. H.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Diffusion/incremental algorithms
distributed networks
Estimation
sparse estimation
Vectors
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 5
container_issue
container_start_page 1
container_title
container_volume
creator Bin Saeed, Muhammad O.
Sheikh, A. U. H.
description Recently several algorithms have been developed to exploit the distributive nature of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, none of the proposed algorithms addresses the issue of sparse system identification over an adaptive network. Recently, several LMS-based sparse estimation algorithms have been devised. This work proposes a distributed sparse LMS algorithm for parameter estimation over adaptive networks. Two different distributed schemes have been used for incorporating the sparse LMS algorithm into the adaptive network framework. Mean transient analysis has been carried out. Simulation results show that in a sparse environment the proposed algorithms perform better than the LMS algorithm.
doi_str_mv 10.1109/ICCSPA.2013.6487313
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6487313</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6487313</ieee_id><sourcerecordid>6487313</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-b8616b4e355c1ae18205925b0b157ffbb29e8fd0aceb186b97e1ae81e989028f3</originalsourceid><addsrcrecordid>eNpFj91Kw0AUhFdEUGufoDd5gcRzdrN_lyX4UygoVK_LbnIWVm0Sskulb2_AQq-GgfmGGcZWCBUi2MdN0-ze1xUHFJWqjRYortg91koLbjja64sBuGXLlL4AYEYVanvHxG50U6IinVKmQxE76nMMsXU5Dn0xHGkqXOfGHI9U9JR_h-k7PbCb4H4SLc-6YJ_PTx_Na7l9e9k0620ZUctceqNQ-ZqElC06wnmAtFx68Ch1CN5zSyZ04FryaJS3muaYQbLGAjdBLNjqvzcS0X6c4sFNp_35pPgDwbVGVA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Sparse system identification over adaptive networks</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Bin Saeed, Muhammad O. ; Sheikh, A. U. H.</creator><creatorcontrib>Bin Saeed, Muhammad O. ; Sheikh, A. U. H.</creatorcontrib><description>Recently several algorithms have been developed to exploit the distributive nature of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, none of the proposed algorithms addresses the issue of sparse system identification over an adaptive network. Recently, several LMS-based sparse estimation algorithms have been devised. This work proposes a distributed sparse LMS algorithm for parameter estimation over adaptive networks. Two different distributed schemes have been used for incorporating the sparse LMS algorithm into the adaptive network framework. Mean transient analysis has been carried out. Simulation results show that in a sparse environment the proposed algorithms perform better than the LMS algorithm.</description><identifier>ISBN: 1467328200</identifier><identifier>ISBN: 9781467328203</identifier><identifier>EISBN: 1467328219</identifier><identifier>EISBN: 9781467328210</identifier><identifier>EISBN: 1467328197</identifier><identifier>EISBN: 9781467328197</identifier><identifier>DOI: 10.1109/ICCSPA.2013.6487313</identifier><language>eng</language><publisher>IEEE</publisher><subject>Diffusion/incremental algorithms ; distributed networks ; Estimation ; sparse estimation ; Vectors</subject><ispartof>2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2013, p.1-5</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/6487313$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6487313$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bin Saeed, Muhammad O.</creatorcontrib><creatorcontrib>Sheikh, A. U. H.</creatorcontrib><title>Sparse system identification over adaptive networks</title><title>2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA)</title><addtitle>ICCSPA</addtitle><description>Recently several algorithms have been developed to exploit the distributive nature of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, none of the proposed algorithms addresses the issue of sparse system identification over an adaptive network. Recently, several LMS-based sparse estimation algorithms have been devised. This work proposes a distributed sparse LMS algorithm for parameter estimation over adaptive networks. Two different distributed schemes have been used for incorporating the sparse LMS algorithm into the adaptive network framework. Mean transient analysis has been carried out. Simulation results show that in a sparse environment the proposed algorithms perform better than the LMS algorithm.</description><subject>Diffusion/incremental algorithms</subject><subject>distributed networks</subject><subject>Estimation</subject><subject>sparse estimation</subject><subject>Vectors</subject><isbn>1467328200</isbn><isbn>9781467328203</isbn><isbn>1467328219</isbn><isbn>9781467328210</isbn><isbn>1467328197</isbn><isbn>9781467328197</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj91Kw0AUhFdEUGufoDd5gcRzdrN_lyX4UygoVK_LbnIWVm0Sskulb2_AQq-GgfmGGcZWCBUi2MdN0-ze1xUHFJWqjRYortg91koLbjja64sBuGXLlL4AYEYVanvHxG50U6IinVKmQxE76nMMsXU5Dn0xHGkqXOfGHI9U9JR_h-k7PbCb4H4SLc-6YJ_PTx_Na7l9e9k0620ZUctceqNQ-ZqElC06wnmAtFx68Ch1CN5zSyZ04FryaJS3muaYQbLGAjdBLNjqvzcS0X6c4sFNp_35pPgDwbVGVA</recordid><startdate>201302</startdate><enddate>201302</enddate><creator>Bin Saeed, Muhammad O.</creator><creator>Sheikh, A. U. H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201302</creationdate><title>Sparse system identification over adaptive networks</title><author>Bin Saeed, Muhammad O. ; Sheikh, A. U. H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b8616b4e355c1ae18205925b0b157ffbb29e8fd0aceb186b97e1ae81e989028f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Diffusion/incremental algorithms</topic><topic>distributed networks</topic><topic>Estimation</topic><topic>sparse estimation</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Bin Saeed, Muhammad O.</creatorcontrib><creatorcontrib>Sheikh, A. U. H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bin Saeed, Muhammad O.</au><au>Sheikh, A. U. H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sparse system identification over adaptive networks</atitle><btitle>2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA)</btitle><stitle>ICCSPA</stitle><date>2013-02</date><risdate>2013</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><isbn>1467328200</isbn><isbn>9781467328203</isbn><eisbn>1467328219</eisbn><eisbn>9781467328210</eisbn><eisbn>1467328197</eisbn><eisbn>9781467328197</eisbn><abstract>Recently several algorithms have been developed to exploit the distributive nature of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, none of the proposed algorithms addresses the issue of sparse system identification over an adaptive network. Recently, several LMS-based sparse estimation algorithms have been devised. This work proposes a distributed sparse LMS algorithm for parameter estimation over adaptive networks. Two different distributed schemes have been used for incorporating the sparse LMS algorithm into the adaptive network framework. Mean transient analysis has been carried out. Simulation results show that in a sparse environment the proposed algorithms perform better than the LMS algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ICCSPA.2013.6487313</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 1467328200
ispartof 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2013, p.1-5
issn
language eng
recordid cdi_ieee_primary_6487313
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Diffusion/incremental algorithms
distributed networks
Estimation
sparse estimation
Vectors
title Sparse system identification over adaptive networks
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T06%3A48%3A00IST&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=Sparse%20system%20identification%20over%20adaptive%20networks&rft.btitle=2013%201st%20International%20Conference%20on%20Communications,%20Signal%20Processing,%20and%20their%20Applications%20(ICCSPA)&rft.au=Bin%20Saeed,%20Muhammad%20O.&rft.date=2013-02&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.isbn=1467328200&rft.isbn_list=9781467328203&rft_id=info:doi/10.1109/ICCSPA.2013.6487313&rft_dat=%3Cieee_6IE%3E6487313%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467328219&rft.eisbn_list=9781467328210&rft.eisbn_list=1467328197&rft.eisbn_list=9781467328197&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6487313&rfr_iscdi=true