COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. 1 Telecommunications Industry...

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

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Document Cover Sheet

Project Number PN-3-0062-RV2

Document Title TIA-921B Preliminary HMM Cluster Analysis

Source Alcatel-Lucent

Contact Les J. Wu

Alcatel-Lucent

600 Mountain Avenue

Room 2b-219

Murray Hill, NJ 07974

Phone: 908-582-4964

Fax: 908-582-4964 Email: [email protected]

Distribution TR-30.3

For Incorporation Into TIA Publication X For Information

Intended Purpose of Document (Select one) Other (describe) -

The document to which this cover statement is attached is submitted to a Formulating Group or sub-element thereof of the Telecommunications Industry Association (TIA) in accordance with the provisions of Sections 6.4.1–6.4.6 inclusive of the TIA Engineering Manual dated October 2009, all of which provisions are hereby incorporated by reference.

Abstract

Update of recent G.1050/921 Markov Model Extraction and Cluster Analysis and quick look wireless networks

Telecommunications Industry Association TR-30.3/11-07-008Telecon July 12, 2011

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. ALCATEL-LUCENT — INTERNAL PROPRIETARY — USE PURSUANT TO COMPANY INSTRUCTION

TIA-921B Preliminary HMM Cluster Analysis

Les J. WuJuly 2011


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G1050 Examples(Counter results from Telchemy vqcapture alpha)


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What does it all mean?

• Probabilities computed from counts. .xls previously submitted available as contribution 7

ftp://[email protected]/TR-30/TR-30.3/Public/1104-Austin/31106007-markov%20model.xls

• Is there clustering of variables or computed parameters? Very Few Errors in VoIP cases with errors

Many Px probabilities could not be extracted due to P32=0

• Dataset has had an initial look using statistical package “R” Principle Component Analysis is similar to Eigenvalue Decomposition or SVD

.csv and commands used in analysis in backup section of this presentation

• By definition, some of the model parameters are correlated P1+P2+P3 = 1

Sum of Pxy = 3

P22:P23

P11:P13


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Pairwise P-Plot

• P1+P2+P3 = 1

• Color coding:

Red=HD

Blue=SD

Green=VoIP


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Comp.1 Comp.2 Comp.3Standard deviation 0.04214965 0.01097286 3.577409e-06Proportion of Variance 0.93652931 0.06347068 6.746376e-09Cumulative Proportion 0.93652931 0.99999999 1.000000e+00Loadings: Comp.1 Comp.2 Comp.3p1 0.780 0.242 0.577p2 -0.180 -0.796 0.577p3 -0.600 0.554 0.577

Hmm[,18:20]


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Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 0.8137195 0.15383318 0.028077692 3.643808e-03Proportion of Variance 0.9643663 0.03446613 0.001148194 1.933764e-05Cumulative Proportion 0.9643663 0.99883247 0.999980662 1.000000e+00

Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7p11 -0.703 0.707p13 0.703 0.707p31 -0.807 0.577 p32 0.476 -0.536 0.389 0.577 p33 -0.404 0.571 0.418 0.577 p22 -0.550 -0.439 0.707 p23 0.550 0.439 0.707

Hmm[,10:16]


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Comp.1 Comp.2 Comp.3 Comp.4Standard deviation 7.462114e+05 3.534456e+03Proportion of Variance 9.999772e-01 2.243425e-05 Cumulative Proportion 9.999772e-01 9.999996e-01

Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6c11 1.000 c13 0.147 0.954 -0.258c14 0.101 0.925 0.366c22 -0.931 -0.365 c23 -0.242 0.379 -0.893c33 -0.362 0.919 -0.147


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Wireless


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Wireless Uplink TCP GoodputFairfax, Va – 2 July 2011


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Loaded Uplink Delay Histogram


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LTE/WiFi Delay Distribution


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Hotel Wifi vs. 3G Goodput


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LTE TCP Transaction


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Backup


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R Commands

> hmm<-read.csv("hmm.csv")

> vec<-c("red", "red", "blue", "blue", "green", "green")

> class<-unclass(hmm$file)

> class2<-unclass(hmm$codec)

> vec2=c("blue", "red", "green")

> class

> class2

> pairs(na.omit(hmm[10:16]), pch=21, bg=vec[class])

> pairs(na.omit(hmm[17:20]), pch=21, bg=vec[class])

> hmm.prc<-princomp(na.omit(hmm[,10:16]))

> summary(hmm.prc, loadings=TRUE)

> pairs(hmm.prc$scores, pch=21, bg=vec[class])

> hmm.prc<-princomp(na.omit(hmm[,18:20]))

> summary(hmm.prc, loadings=TRUE)

> pairs(hmm.prc$scores, pch=21, bg=vec[class])

> plot(hmm.prc$scores[,1:2], pch=21, bg=vec2[class2],main="r-video b-audio")

> plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class],main="r-HD b-SD")

> plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class])

> text(hmm.prc$scores[,1:2], labels=class, pos=4)

> vec<-c("red", "red", "blue", "blue", "green", "green")

> pairs(hmm.prc$scores, pch=21, bg=vec[unclass(hmm$file)])

case file codec c11 c13 c14 c22 c23 c33 p11 p13 p31 p32 p33 p22Dw1 HDTV1_Downaudio 86939 105 22 27 2 661 0.99879 0.00121 0.13672 0.0026 0.86068 0.93103Dw2 HDTV1_Downaudio 86965 117 18 5 1 648 0.99866 0.00134 0.15274 0.00131 0.84595 0.83333Dw3 HDTV1_Downaudio 86960 90 22 0 0 678 0.99897 0.00103 0.11719 0 0.88281 NADw4 HDTV1_Downaudio 86943 100 32 15 2 655 0.99885 0.00115 0.1321 0.00264 0.86526 0.88235Dw5 HDTV1_Downaudio 86829 121 92 50 7 646 0.99861 0.00139 0.15633 0.00904 0.83463 0.87719Dw6 HDTV1_Downaudio 86848 117 89 33 7 649 0.99865 0.00135 0.15136 0.00906 0.83959 0.825Dw7 HDTV1_Downaudio 86842 117 90 32 5 653 0.99865 0.00135 0.15097 0.00645 0.84258 0.86486Dw8 HDTV1_Downaudio 86790 126 103 63 7 646 0.99855 0.00145 0.16175 0.00899 0.82927 0.9Gw1 HDTV1_Downaudio 86979 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw2 HDTV1_Downaudio 86977 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw3 HDTV1_Downaudio 86973 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw4 HDTV1_Downaudio 86970 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw5 HDTV1_Downaudio 86968 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw6 HDTV1_Downaudio 86964 167 3 0 0 609 0.99808 0.00192 0.21521 0 0.78479 NAGw7 HDTV1_Downaudio 86962 168 0 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NAGw8 HDTV1_Downaudio 86957 168 1 0 0 609 0.99807 0.00193 0.21622 0 0.78378 NADw3 HDTV2_Downaudio 100179 2 7 0 0 2 0.99998 0.00002 0.5 0 0.5 NAGw3 HDTV2_Downaudio 100186 2 0 0 0 2 0.99998 0.00002 0.5 0 0.5 NADp1 SDTV1_Downaudio 12807 144 83 0 0 899 0.98888 0.01112 0.13806 0 0.86194 NADp2 SDTV1_Downaudio 12769 155 18 25 5 961 0.98801 0.01199 0.13827 0.00446 0.85727 0.83333Dp3 SDTV1_Downaudio 12754 191 27 45 4 911 0.98525 0.01475 0.17269 0.00362 0.82369 0.91837Dp4 SDTV1_Downaudio 12021 172 72 641 83 930 0.98589 0.01411 0.14515 0.07004 0.78481 0.88536Dp6 SDTV1_Downaudio 12047 163 69 607 82 942 0.98665 0.01335 0.13732 0.06908 0.7936 0.88099Dw2 SDTV1_Downaudio 12804 169 20 0 0 940 0.98697 0.01303 0.15239 0 0.84761 NADw4 SDTV1_Downaudio 12793 156 73 12 2 896 0.98795 0.01205 0.14801 0.0019 0.85009 0.85714Dw5 SDTV1_Downaudio 12734 178 38 63 7 912 0.98621 0.01379 0.16226 0.00638 0.83136 0.9Dw6 SDTV1_Downaudio 12761 175 26 31 4 934 0.98647 0.01353 0.15723 0.00359 0.83917 0.88571Dw7 SDTV1_Downaudio 12737 174 7 55 7 951 0.98652 0.01348 0.15371 0.00618 0.84011 0.8871Gp1 SDTV1_Downaudio 12808 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGp2 SDTV1_Downaudio 12808 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGp3 SDTV1_Downaudio 12807 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGp4 SDTV1_Downaudio 12807 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGp6 SDTV1_Downaudio 12804 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGw2 SDTV1_Downaudio 12808 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGw4 SDTV1_Downaudio 12807 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGw5 SDTV1_Downaudio 12807 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGw6 SDTV1_Downaudio 12806 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NAGw7 SDTV1_Downaudio 12806 124 24 0 0 977 0.99041 0.00959 0.11262 0 0.88738 NADp1 SDTV2_Downaudio 17898 156 15 0 0 775 0.99136 0.00864 0.16756 0 0.83244 NADp2 SDTV2_Downaudio 17875 141 31 8 2 786 0.99217 0.00783 0.15178 0.00215 0.84607 0.8Dp3 SDTV2_Downaudio 17806 141 30 68 7 790 0.99214 0.00786 0.15032 0.00746 0.84222 0.90667Dp4 SDTV2_Downaudio 16954 163 109 717 86 811 0.99048 0.00952 0.15377 0.08113 0.76509 0.8929Dp5 SDTV2_Downaudio 17351 153 116 362 59 800 0.99126 0.00874 0.15119 0.0583 0.79051 0.85986Dp6 SDTV2_Downaudio 17062 162 107 616 81 797 0.99059 0.00941 0.15577 0.07788 0.76635 0.88379Dw4 SDTV2_Downaudio 17895 150 19 0 0 778 0.99169 0.00831 0.16164 0 0.83836 NADw6 SDTV2_Downaudio 17839 147 24 29 5 797 0.99183 0.00817 0.1549 0.00527 0.83983 0.85294Dw7 SDTV2_Downaudio 17803 145 28 59 9 796 0.99192 0.00808 0.15263 0.00947 0.83789 0.86765Gp1 SDTV2_Downaudio 17899 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NAGp2 SDTV2_Downaudio 17898 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NAGp3 SDTV2_Downaudio 17897 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NAGp4 SDTV2_Downaudio 17897 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NAGp5 SDTV2_Downaudio 17890 140 21 4 2 784 0.99224 0.00776 0.15119 0.00216 0.84665 0.66667Gp6 SDTV2_Downaudio 17893 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NAGw4 SDTV2_Downaudio 17897 156 15 0 0 774 0.99136 0.00864 0.16774 0 0.83226 NA

Save Me as hmm.csv:


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