Modeling RACH Arrivals and Collisions for Human-Type Communication
This letter proposes an analytical model to evaluate the collision probability on the Random-Access CHannel (RACH) in Long-Term Evolution systems as a function of the number of user equipment, the number of available preambles, and the Inter-arrival times of the RACH Requests (IRRs) of the average u...
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| Veröffentlicht in: | IEEE communications letters 2016-07, Vol.20 (7), p.1417-1420 |
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| creator | Foddis, Gianluca Garroppo, Rosario Giordano, Stefano Procissi, Gregorio Roma, Simone Topazzi, Simone |
| description | This letter proposes an analytical model to evaluate the collision probability on the Random-Access CHannel (RACH) in Long-Term Evolution systems as a function of the number of user equipment, the number of available preambles, and the Inter-arrival times of the RACH Requests (IRRs) of the average user. The model for the IRR of the average user is obtained from real traffic data captured at the eNodeB of a mobile operator, and is derived by emulating the radio resource control (RRC) state machine for different RRCs Inactivity timer (RRCIT) settings. The results of this letter suggest that when RRCIT is set to a few seconds, a mixture model is more accurate than the Poisson hypothesis both in modeling the IRR and in estimating the RACH performance. |
| doi_str_mv | 10.1109/LCOMM.2016.2560819 |
| format | Article |
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The model for the IRR of the average user is obtained from real traffic data captured at the eNodeB of a mobile operator, and is derived by emulating the radio resource control (RRC) state machine for different RRCs Inactivity timer (RRCIT) settings. The results of this letter suggest that when RRCIT is set to a few seconds, a mixture model is more accurate than the Poisson hypothesis both in modeling the IRR and in estimating the RACH performance.</description><identifier>ISSN: 1089-7798</identifier><identifier>EISSN: 1558-2558</identifier><identifier>DOI: 10.1109/LCOMM.2016.2560819</identifier><identifier>CODEN: ICLEF6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Analytical models ; Channels ; Collisions ; Data models ; Evolution ; Exponential distribution ; Fitting ; Indexes ; Information technology ; Mathematical analysis ; mixture model ; Mixture models ; Mobile communication ; Modelling ; Operators ; RACH collision probability ; radio resource control (RRC) inactivity timer ; random access opportunity (RAO) ; RRC state machine ; Timing devices</subject><ispartof>IEEE communications letters, 2016-07, Vol.20 (7), p.1417-1420</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The model for the IRR of the average user is obtained from real traffic data captured at the eNodeB of a mobile operator, and is derived by emulating the radio resource control (RRC) state machine for different RRCs Inactivity timer (RRCIT) settings. The results of this letter suggest that when RRCIT is set to a few seconds, a mixture model is more accurate than the Poisson hypothesis both in modeling the IRR and in estimating the RACH performance.</description><subject>Analytical models</subject><subject>Channels</subject><subject>Collisions</subject><subject>Data models</subject><subject>Evolution</subject><subject>Exponential distribution</subject><subject>Fitting</subject><subject>Indexes</subject><subject>Information technology</subject><subject>Mathematical analysis</subject><subject>mixture model</subject><subject>Mixture models</subject><subject>Mobile communication</subject><subject>Modelling</subject><subject>Operators</subject><subject>RACH collision probability</subject><subject>radio resource control (RRC) inactivity timer</subject><subject>random access opportunity (RAO)</subject><subject>RRC state machine</subject><subject>Timing devices</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE9LwzAYh4MoOKdfQC8FL1468yZNmhxnUSdsDGSeQ9YmktE2M1mFfXszNzx4yb_3-YX3fRC6BTwBwPJxXi0XiwnBwCeEcSxAnqERMCZykpbzdMZC5mUpxSW6inGDMRaEwQg9LXxjWtd_Zu_TapZNQ3Dfuo2Z7pus8m3rovN9zKwP2WzodJ-v9luTKl039K7Wu1S9Rhc2RczNaR-jj5fnVTXL58vXt2o6z2tK6C7n1Kw1LXiD2bo0tWSEmtpCetJrQYxodNEALRhPVxACuJWCM8aksY20JdAxejj-uw3-azBxpzoXa9O2ujd-iArSRIyXACyh9__QjR9Cn7pLFKayKJKVRJEjVQcfYzBWbYPrdNgrwOqgVf1qVQet6qQ1he6OIWeM-QuUBacYU_oDQfdyGg</recordid><startdate>20160701</startdate><enddate>20160701</enddate><creator>Foddis, Gianluca</creator><creator>Garroppo, Rosario</creator><creator>Giordano, Stefano</creator><creator>Procissi, Gregorio</creator><creator>Roma, Simone</creator><creator>Topazzi, Simone</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Analytical models Channels Collisions Data models Evolution Exponential distribution Fitting Indexes Information technology Mathematical analysis mixture model Mixture models Mobile communication Modelling Operators RACH collision probability radio resource control (RRC) inactivity timer random access opportunity (RAO) RRC state machine Timing devices |
| title | Modeling RACH Arrivals and Collisions for Human-Type Communication |
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