Stronger Wireless Signals Appear More Poisson
Keeler et al. recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modeled by an inhomogeneous Poisson point process on the positi...
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| Veröffentlicht in: | IEEE wireless communications letters 2016-12, Vol.5 (6), p.572-575 |
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| container_title | IEEE wireless communications letters |
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| creator | Keeler, Paul Ross, Nathan Aihua Xia Blaszczyszyn, Bartlomiejd |
| description | Keeler et al. recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modeled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects. The aim of this letter is to apply some of the main results in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show, it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work. |
| doi_str_mv | 10.1109/LWC.2016.2601913 |
| format | Article |
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New results are derived that show, it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2016.2601913</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Computer Science ; Convergence ; Density measurement ; error bounds ; Mathematical model ; Mathematics ; Networking and Internet Architecture ; Observers ; Poisson approximation ; Poisson density functions ; Power measurement ; Probability ; Propagation ; propagation model ; Signal processing ; Stochastic geometry ; Transmitters ; Wireless networks</subject><ispartof>IEEE wireless communications letters, 2016-12, Vol.5 (6), p.572-575</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-74f57df88be223e94b948eeb7b59fabf8c1d49d043b8f71826925a6d920ef54a3</citedby><cites>FETCH-LOGICAL-c325t-74f57df88be223e94b948eeb7b59fabf8c1d49d043b8f71826925a6d920ef54a3</cites><orcidid>0000-0002-2063-1075</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7551189$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,777,781,793,882,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7551189$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://inria.hal.science/hal-01331897$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Keeler, Paul</creatorcontrib><creatorcontrib>Ross, Nathan</creatorcontrib><creatorcontrib>Aihua Xia</creatorcontrib><creatorcontrib>Blaszczyszyn, Bartlomiejd</creatorcontrib><title>Stronger Wireless Signals Appear More Poisson</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>Keeler et al. recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modeled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects. The aim of this letter is to apply some of the main results in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show, it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.</description><subject>Computer Science</subject><subject>Convergence</subject><subject>Density measurement</subject><subject>error bounds</subject><subject>Mathematical model</subject><subject>Mathematics</subject><subject>Networking and Internet Architecture</subject><subject>Observers</subject><subject>Poisson approximation</subject><subject>Poisson density functions</subject><subject>Power measurement</subject><subject>Probability</subject><subject>Propagation</subject><subject>propagation model</subject><subject>Signal processing</subject><subject>Stochastic geometry</subject><subject>Transmitters</subject><subject>Wireless networks</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEFLAzEQhYMoWGrvgpcFTx62ZpLNJjmWolZYUajSY8h2J3VLbdakCv57s2zpXGaY-eYN8wi5BjoFoPq-Ws2njEI5ZSUFDfyMjBiULGe8EOenmstLMolxS1MkjoEakXx5CH6_wZCt2oA7jDFbtpu93cVs1nVoQ_biA2Zvvo3R76_IhUsjnBzzmHw8PrzPF3n1-vQ8n1X5mjNxyGXhhGycUjUyxlEXtS4UYi1roZ2tnVpDU-iGFrxWToJipWbClo1mFJ0oLB-Tu0H30-5MF9ovG_6Mt61ZzCrT9yhwDkrLX0js7cB2wX__YDyYrf8J_QcGlKCMS81UouhArYOPMaA7yQI1vYcmeWh6D83Rw7RyM6y0iHjCpRCQLvN_SZBqLA</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Keeler, Paul</creator><creator>Ross, Nathan</creator><creator>Aihua Xia</creator><creator>Blaszczyszyn, Bartlomiejd</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Computer Science Convergence Density measurement error bounds Mathematical model Mathematics Networking and Internet Architecture Observers Poisson approximation Poisson density functions Power measurement Probability Propagation propagation model Signal processing Stochastic geometry Transmitters Wireless networks |
| title | Stronger Wireless Signals Appear More Poisson |
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