researches in telecommunications at izhevsk state technical university

Researches in Telecommunications

at Izhevsk State Technical University

Albert Abilov

Seminar at Chair of Telecommunications,TU Dresden

October 21, 2008

What would i like to tell What would i like to tell today abouttoday about Grant for my staying at TU Dresden Where do i live and work Several words about me The main researches made in past Tools for telecom courses

2

Grant for my staying at TU Grant for my staying at TU DresdenDresden Scholarship of «Mikhail Lomonosov»-Programme:

Research Grants and Research Stays for Doctoral Candidates and Young University Teachers from the Natural Sciences and Engineering

Scholarship is jointly granted by DAAD (www.daad.de) and Russian Education Ministry (www.ed.gov.ru)

Host part is Chair of Telecommunication, TU Dresden (www.ifn.et.tu-dresden.de/tk), Prof. Dr.-Ing. Ralf Rehnert

The period of stay for research is 3 months3

Where do i live and workWhere do i live and workMy District and City

Udmurt Republic is one of 85 districts of Russia

Izhevsk is Capitol of Udmurt Republic

Population of Izhevsk is about 650 000 people

Izhevsk is located

about 1 100 km from Moscow

Udmurt Republic: www.udmurt.ruIzhevsk: www.izh.ru

4

Where do i live and workWhere do i live and workMy University

Izhevsk State Technical University is one of 4 State universities in Izhevsk

There are about 10 000 students and 14 faculties in the most of technical areas.

Izhevsk State Technical University:

University has cooperation and student/researcher exchanges with many Russians and abroad universities.

www.inter.istu.ru

It was created in 1952

5

Where do i live and workWhere do i live and workOur ChairOur Chair

Faculty ofInstrumentation

Engineering

Radio EngineeringRadio Engineering

Equipments and methodsEquipments and methodsof quality controlof quality control

Design of radio-equipmentDesign of radio-equipment

Electrical EngineeringElectrical EngineeringLaser systemsLaser systems

PhysicsPhysics

TelecommunicationTelecommunicationnetworks and systemsnetworks and systems

http://www.istu.ru/unit/prib/netChair of Telecommunication Networks and Systems:

Specialities for students– Telecom networks and

switching systems– Transmit telecom systems

Labs– Switching systems– Electronics lab– Communication networks

Department (Chair) was created at 1998

6

Several words about meSeveral words about me

ALBERT ALBERT ABILOVABILOV

Candidate of Science, Docent

in Izhevsk State Technical University

Address:7, Studencheskaya str.Izhevsk, 426069, RUSSIAOffice:Izhevsk State Technical UniversityBuilding 1, Floor 4, Room 403

Phone/fax: +7 3412 580399Mobile: +7 9128 562202E-mail: [email protected]

My contacts

WWW: http://www.istu.ru/unit/prib/net/abilov

7

Candidate of Science (PhD) Candidate of Science (PhD) thesestheses

Creation of mathematical models of mobile communication systems

Research and design algorithms for optimal receiving of digital signals

Creation of realistic algorithms for receiving of digital signals and for control of forward channel state in mobile system

Creation of simulation model for control algorithms

Analysis of efficiency of former and offered algorithms for receiving of digital signals and for control of forward channel state by means of simulation

Design of hard- and software facilities for realization of offered algorithms in subscriber station of “Volemot” mobile system

Trial (field) testing and experimental evaluation of offered algorithms efficiency

Design and research of digital signal estimation and optimal utilization of frequency resource algorithms in mobile telecommunication system

Supervisor: Prof. Vladimir V. Khvorenkov

The main tasks:

8

Candidate of Science thesesCandidate of Science theses

Math model of digital mobile communication channel

Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system

Channel А

Channel В

kkA ,1 D

kk ,1

guk

gWk 1

gZ k 1

gX k 1

gxk 1

gWkkBgXgZ kkk

,111

gukkgxkkgX kkk

,1,11 gX

gZ

gW

– state vector;

– estimation vector;

– errors vector;

guk – control vector;


Source of control

Source of informationcodewords

Source of errors

For estimation

D – delay;

9

Candidate of Science thesesCandidate of Science thesesDesign and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system

B(k+1,k) D

D B(k+1,k)

D B(k+1,k)

Receive

Quality

analysis

gX ik 1

ˆ

gWk0

1

SmQ

Control unit

gu am 1

gW Sk

11

gWk1

1

00 DfPош

11 DfPош

11 SSош DfP

gW sk 1

gZ s

k 1

gW sk 1

ˆ

A(k+1,k) D

gxk 1

gX k 1

0

gu bm 1

Backward channel

1

S - 1

i

i

if (s = i)

Criterion of channel quality is minimum of bit errors ratio (BER)


Sources of errors

Source of informationcodewords

Errorsestimation

Quality of channelsestimation

mt

lt

kt

searcht synt

0 1 1n

1K

1S

0 1

0

Model of control channel searching in mobile system

10


Algorithm of digital information receiving in signaling channels

of “VOLEMOT” mobile system. Results of simulation


Codeword structure

1 1 1 1 1 0 0 0

Synchronization Information

Algorithm which was:compare of two nearbycodewords during fix time

Offered and realized algorithm:voting method

0.993619

0

pps_sgi

pls_sgi

ppm2_sgi

plm2_sgi

ppm3_sgi

0.9981 10

3 Pei

1 103

0.01 0.1 1

0

0.2

0.4

0.6

0.8

1

1

ошP

,ппP

лтP

3ппP

3лтP

1лтP

1ппP

4ппP

0.993507

0

pps_sgi

pls_sgi

ppm1_sgi

plm1_sgi

0.9981 10

3 Pei

1 103

0.01 0.1 1

0

0.2

0.4

0.6

0.8

1

1

ошP

,ппP

лтP

2ппP

2лтP

1лтP

1ппP


Bit error probability Bit error probability

Pro

bab

ility

of

code

wor

d re

ceiv

e

Pro

bab

ility

of

code

wor

d re

ceiv

eCorrect receive forformer algorithm

Correct receive foroffered algorithm

False receive foroffered algorithm

False receive forformer algorithm

Correct receive foroffered algorithm withreduced probabilityof false receive

11


Algorithm of digital information receiving in signaling channels

of “VOLEMOT” mobile system. Results of simulation


Codeword structure

1 1 1 1 1 0 0 0


Offered synchronization byte: 01111110

1

0

pps1_sgi

ppm3_sgi

ppm6_sgi

0.9981 10

3 Pei

1 103

0.01 0.1 1

0

0.2

0.4

0.6

0.8

1

1

ошP

ппP

1ппP

3ппP

6ппP


Pro

bab

ility

of

code

wor

d re

ceiv

e

Bit error probability

Correct receive foroffered algorithm withnew synchro-byte

Codeword structure

0 1 1 1 1 1 1 0


Offered

Former

Correct receive foroffered algorithm withformer synchro-byte

Correct receive forformer algorithm withformer synchro-byte

12

Candidate of Science thesesCandidate of Science thesesDesign and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system

1x

2x

1D

2D

11 DfPош ,

gWPm

1

1

22 DfPош

gWPm

2

1

Codewords generator

Re-ceiver

Estima-tion Q

DB

gX ik 1

ˆ

Compare unit

Select of channel

gWk 1ˆ

imQ

2) порQ

2) SmS

m Qi min1

i

Channel switch

Generator of dis-tances ПСD

Cycle generator

gX k 1

gWk 1

Recorder of channel state

SSош DfP

gWP Sm

1

SD

Sx

1)

0

11

ll

ii

при

при

Si

Si

l

l

1) прQ

1) Former control algorithm; 2) Offered control algorithm

срF

Errors source


x 0

BS1 BS4 BS2

1x 4x

2x

maxx

1D 4D

2D ПСD

ПСx

3x

3D

BS3

Simulation model of control channel searching in mobile system

13


Simulation model of control channel searching in mobile system


0.04

0

F1i

F3

24.8888890 Perri

0 5 10 15 20 250

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.04

0

F2i

F4

Fgr

24.8888890 Perri

0 5 10 15 20 250

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

БС1 БС4 БС2 БС3

i = 1

x , км

x , км

а

б

i = 2

i = 1 i = 4 i = 2

iF

iF

iпсрF .

iпорF

iдсрF .

i = 3

i = 3

iпсрF .

= 0,001587

iдсрF . = 0,002832

Former control algorithm:

Offered control algorithm:

M

BQF

M

m

im

iср

1

0

Criterion of efficiency: average bit errors ratio on the simulation interval

порF = 0,01

Threshold for changing channel:


14


Realization and operational testing (trial) of algorithms– The developed algorithms were realized in Mobile subscriber terminal URAL-RS6 for mobile system VOLEMOT (Russia)– Bit error rate measurement on the real mobile network (VOLEMOT)


БС 11

БС 15

БС 12

БС 14

13

1 4

7

9 11

6

8 14

15

18

20 23

25

27

29

31

37

33 35

41

42

44

46

39

38

49

55

56 57

53

47

12 14

11

11 15

15 12 12 14

11

11 12

Change of channels:

Former argorithm

Offered argorithm


15


Realization and operational testing (trial) of algorithms on real system

0

0,001

0,002

0,003

0,004

0,005

0,006

0,007

0 0,005 0,01 0,015 0,02 0,025 0,03

Treshold for changing channel

Ave

rag

e B

ER

Simulation Trial test


0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Measurements, m

а

BE

R

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Measurements, m

б

BE

R

iдсрF .

iпсрF .

iпорF

i = 11 i = 12 i = 14

i = 11 i = 15 i = 12 i = 14

iпсрF .

= 0,002538

iдсрF . = 0,004809

Offered control algorithm


Former control algorithm

How threshold for changing channel influence on average BER and gain (results of simulation and experiment)

0,5

1

1,5

2

2,5

3

3,5

4

4,5

0 0,005 0,01 0,015 0,02 0,025 0,03

Threshold for changing channel

Gai

n

Simulation Trial test

iпср

iдср

выигр F

Fk

.

.

Gain:

Average BER for former algorithm

Average BER ratio for offered algorithm

16

Applications for network Applications for network planningplanningTool for cellular radio subsystem planning

Realization of model in network planning tool

Features of tool:• approximate coverage of cell calculation;• network configuration planning

Interface

Parameters of network

Factors of Hata model

Switching center parameters

Base station parameters

Co-author: Roman Semieshin

17

Applications for network Applications for network planningplanningTool for urban and rural telephone networks planning

Realization of famous models in network planning tool

Co-author: Alexey Susekov

Features of tool:• traffic calculation;• trunk lines calculation;• for urban and rural applications;• network planning and traffic forecasting.

It is now utilized for:educational process

Interface

Switching station parameters

Types of traffic

18

Telecom infrastructure Telecom infrastructure developmentdevelopmentResearch Project № П-1-02: Conception of telecommunication infrastructure development in Udmurt Republic till 2010 year Grant: Ministry of fuel, energy and communication of Udmurt Republic, Russia

Advisor and Principal Investigator: Albert Abilov

To analyze dynamic and state of the art of info-communication development in World, Russia and Udmurt Republic

To determine the most important trends, basic views and regulations concerning telecommunication networks and services development in the Udmurt Republic up to the year 2010

Basic objectives and tasks of the conception:

Expected resulting effect: Realization of the conception will reduce the lag of the Udmurt Republic in

the world basic telecommunication indices and will facilitate to provide people and organizations with high-quality communication services

Conception (220 pp.) has been approved and accepted for realization by Government of Udmurt Republic (Russia) in June 2004

19

Impact economics & education Impact economics & education on ICTon ICT World trends of info-communications development

– General analysis of info-communications development

0

0,5

1

1,5

2

2,5

3

3,5

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Su

bsc

rib

ers,

bill

ion Main telephone lines

Mobile cellular subscribersInternet usersBroadband subscribers

Research Project № 07-07-07009:Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)


Asia46%

Europe23%

USA and Canada

17%

Latin America

9%

Oceania1%

Africa4%

Percentages of Internet users over the world (2007 year) Key ICT indicators in dynamic

а) Developed economies b) Developing economies c) Poor economies

20


– Wired telephone communication dynamics



1977197919811983198519871989199119931995199719992001200320052007

Nor

way

Icel

and

Swit

zerl

and

Irel

and

Den

mar

kU

nite

d St

ates

Swed

enN

ethe

rlan

dsA

ustr

iaFi

nlan

dU

nite

d K

ingd

omA

ustr

alia

Japa

nB

elgi

umFr

ance

Can

ada

Ger

man

yK

uwai

tIt

aly

Uni

ted

Ara

b E

mir

ates

New

Zea

land

Spai

nH

ong

Kon

g, C

hina

Cyp

rus

Gre

ece

0

10

20

30

40

50

60

70

80

Tel

epho

ne li

nes

dens

ity,

%

1977197919811983198519871989199119931995199719992001200320052007

Cze

ch R

epub

lic

Hun

gary

Est

onia

Slov

ak R

epub

lic

Pola

ndL

ithu

ania

Mex

ico

Chi

leL

atvi

aR

ussi

aV

enez

uela

Mal

aysi

aT

urke

yA

rgen

tina

Rom

ania

Bra

zil

Bul

gari

a

Bel

arus

Tha

ilan

d

Col

ombi

a

0

10

20

30

40

50

60

70

80

Tel

epho

ne li

nes

dens

ity,

%

1977197919811983198519871989199119931995199719992001200320052007

Nic

arag

uaA

fgha

nist

anN

iger

iaM

ongo

lia

Yem

enPa

kist

anSe

nega

lZ

ambi

aK

enya

Ben

inG

hana

Cam

bodi

a

Gui

nea

Moz

ambi

que

Tan

zani

a

Gam

bia

Uga

nda

Nep

al

Rw

anda

Eth

iopi

a

0

2

4

6

8

10

Tel

epho

ne li

nes

dens

ity,

%а) Developed economies

b) Developing economies

c) Poor economies

21


– Mobile cellular communication dynamics



а) Developed economies

19811983198519871989199119931995199719992001200320052007

Nor

way

Icel

and

Swit

zerl

and

Irel

and

Den

mar

kU

nite

d St

ates

Swed

enN

ethe

rlan

dsA

ustr

iaFi

nlan

dU

nite

d K

ingd

omA

ustr

alia

Japa

nB

elgi

umFr

ance

Can

ada

Ger

man

yK

uwai

tIt

aly

Uni

ted

Ara

bE

mir

ates

New

Zea

land

Spai

nH

ong

Kon

g, C

hina

Cyp

rus

Gre

ece

0102030405060708090100110120130140150

Mob

ile

cell

ular

den

sity

, %

1987

1989

1991

1993

1995

1997

1999

20012003

20052007

Cze

ch R

epub

lic

Hun

gary

Est

onia

Slov

ak R

epub

lic

Pola

ndL

ithu

ania

Mex

ico

Chi

leL

atvi

aR

ussi

aV

enez

uela

Mal

aysi

aT

urke

yA

rgen

tina

Rom

ania

Bra

zil

Bul

gari

aB

elar

us

Tha

ilan

d

Col

ombi

a

0102030405060708090100110120130140150

Mob

ile

cell

ular

den

sity

, %


1991

1993

1995

1997

1999

2001

2003

2005

2007N

icar

agua

Afg

hani

stan

Nig

eria

Mon

goli

aY

emen

Paki

stan

Sene

gal

Zam

bia

Ken

yaB

enin

Gha

naC

ambo

dia

Gui

nea

Moz

ambi

que

Tan

zani

aG

ambi

aU

gand

a

Nep

al

Rw

anda

Eth

iopi

a

0

10

20

30

40

50

60

70

80

90

100

Mob

ile

cell

ular

den

sity

, %

c) Poor economies

22


– Internet dynamics



199119931995199719992001200320052007

Nor

way

Icel

and

Sw

itzer

land

Irel

and

Den

mar

kU

nite

d S

tate

sS

wed

enN

ethe

rlan

dsA

ustr

iaF

inla

ndU

nite

d K

ingd

omA

ustr

alia

Japa

nB

elgi

umF

ranc

eC

anad

aG

erm

any

Kuw

ait

Ital

y

Uni

ted

Ara

b E

mir

ates

New

Zea

land

Spa

in

Hon

g K

ong,

Chi

na

Cyp

rus

Gre

ece

0102030405060708090100

Inte

rnet

use

rs d

ensi

ty, %

1991

1993

1995

1997

19992001

2003

20052007

Cze

ch R

epub

lic

Hun

gary

Est

onia

Slov

ak R

epub

lic

Pola

ndC

hile

Lit

huan

iaM

exic

oL

atvi

aR

ussi

aV

enez

uela

Mal

aysi

aT

urke

yA

rgen

tina

Rom

ania

Bra

zil

Bul

gari

aB

elar

usT

hail

and

Col

ombi

a

0

10

20

30

40

50

60

70

80

90

100

Inte

rnet

use

rs d

ensi

ty, %

20002001200220032004200520062007

Nor

way

Icel

and

Swit

zerl

and

Irel

and

Den

mar

kU

nite

d St

ates

Swed

enN

ethe

rlan

dsA

ustr

iaFi

nlan

dU

nite

d K

ingd

omA

ustr

alia

Japa

nB

elgi

umFr

ance

Can

ada

Ger

man

yK

uwai

tIt

aly

Uni

ted

Ara

b E

mir

ates

New

Zea

land

Spai

nH

ong

Kon

g, C

hina

Cyp

rus

Gre

ece

0

5

10

15

20

25

30

35

40

45

50

Bro

adba

nd s

ubsc

ribe

rs d

ensi

ty, %

c) Poor economies


а) Developed economies

23

Impact economics & education Impact economics & education on ICTon ICT What main factors can impact on ICT development?

– Economics (GDP per capita – Gross Domestic Product per capita)



Average info-communication indicators at the year-end of 2007

Development indicators Developedcountries

Developing countri

es

The poorest countries

Telephone lines density, % 48,1 24,4 1,7

Mobile cellular density, % 109,5 99,6 25,9

Internet users density, % 59,5 37,9 3,8

Broadband subscribers density, % 22,4 7,4 0,05

*GDP per capita, thousand $ 49,6 24,5 1,7

* At the year-end of 2006

– Education (EI – Educational Index) its method of calculation is defined in UN Development Programme (UNDP)

Education Index values averaged by country groups

IndicatorDeveloped

countries

Developing countri

es

The poorest countrie

s

Adult literacy, % (among people at the age of 15 and older) 97,9 95,9 55,9

Combined primary, secondary and tertiary school enrollment level, % 91,7 82,4 53,8

Education Index 0,96 0,91 0,55

)1(

)(61

21

2

nn

RRρ

n

kji

were k – sequence number of country; n – number of countries under examination; Ri, Rj – country ranks according to respective indicators.

The Spearmen ranking method enables to estimate, how close the parameters interrelation is.

24

Impact economics & education Impact economics & education on ICTon ICT ICT and Economics



0

1

10

100

100 1000 10000 100000

GDP per capita, $

Tel

eph

one

lines

den

sity

, %

Brazil

China

Czech Rep.

DenmarkGermany

India Namibia

Nigeria

Russia

Rwanda

Saudi Arabia

Zimbabwe

JapanUSA

0

1

10

100

1000

100 1000 10000 100000

GDP per capita, $

Mob

ile c

ellu

lar

den

sity

, % China

Czech Rep.Germany

Denmark

IndiaNamibia

Nigeria

Russia

Rwanda

Saudi Arabia Japan

Zimbabwe

BrazilUSA

0

1

10

100

100 1000 10000 100000

GDP per capita, $

Inte

rnet

use

rs d

ensi

ty, %

Brazil

Russia

China

Czech Rep.

GermanyIndia

Japan USA

Namibia

Nigeria

Rwanda

Saudi ArabiaZimbabwe

Denmark

0

1

10

100

100 1000 10000 100000

GDP per capita, $

Bro

adba

nd s

ubsc

ribe

rs d

ensi

ty, %

Brazil

Chech Rep.

Germany

India

Japan

Saudi Arabia

USA

Russia*

China

Denmark

Venezuela

25

Impact economics & education Impact economics & education on ICTon ICT ICT and Economics



Indicators of mutual influence of info-communication (2007) and economics (2006)

Indices of mutual influence Telephone lines density Mobile cellular density Internet users density Broadband subscr. density

Equation of correlation line y 0,0091x0,8439 0,6109x0,5223 0,0184x0,7856 8E-5x1,3625

Spearmen Index ρ 0,888 0,861 0,850 0,864

0

0,2

0,4

0,6

0,8

1

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Spea

rmen

's in

dex

0

0,2

0,4

0,6

0,8

1

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Sp

earm

en's

ind

ex

0

0,2

0,4

0,6

0,8

1

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Spea

rmen

's in

dex

Interrelation between Telephone lines Density and GDP per capita Interrelation between Mobile Cellular Density and GDP per capita

Interrelation between Internet Users Density and GDP per capita

Dynamics of Spearmen’s Index

26

Impact economics & education Impact economics & education on ICTon ICT ICT and Educational level



0

1

10

100

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Edication Index

Tel

eph

one

lin

es d

ensi

ty,

%

BrazilChina

Czech Rep.

DenmarkGermany

India Namibia

Nigeria

Rwanda

Saudi Arabia

Zimbabwe

USA

Japan

0

1

10

100

1000

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Education Index

Mob

ile

cell

ula

r d

ensi

ty,

%

Brazil

China

Czech Rep. Denmark

India

USA

Namibia

Nigeria

Russia

Rwanda

Saudi Arabia

Japan

Zimbabwe

Germany

0

1

10

100

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Education Index

Inte

rnet

use

r's

den

sity

, %

Brazil

Russia

China

Czech Rep.

Denmark

Germany

India

JapanUSA

Namibia

Nigeria

Rwanda

Saudi Arabia

Zimbabwe

0

1

10

100

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Education Index

Bro

adb

and

su

bsc

rib

ers

den

sity

, %

Brazil

Russia

China

Czech Rep.

DenmarkGermany

India

Japan

USA

Saudi Arabia

27

Impact economics & education Impact economics & education on ICTon ICT ICT and Educational level

Indicators of interrelation Telephone lines density Mobile subscr. density Internet users density Broadband subscr. density

Equation of correlation line y 0,0212e7,6275x 1,7416e4,0555x 0,0565e6,6709x 5E-5e11,924x

Spearmen Index ρ 0,854 0,721 0,794 0,789



Indicators of mutual influence of info-communication (2007) and Educational Index (2006)

Dynamics of Spearmen’s Index

0

0,2

0,4

0,6

0,8

1

2000 2001 2002 2003 2004 2005 2006 2007

Sp

earm

en's

In

dex

0

0,2

0,4

0,6

0,8

1

2000 2001 2002 2003 2004 2005 2006 2007

Sp

earm

en's

In

dex

0

0,2

0,4

0,6

0,8

1

2000 2001 2002 2003 2004 2005 2006 2007

Sp

earm

en's

In

dex

Interrelation between Telephone lines Density and EI

Interrelation between Mobile Cellular Density and EI

Interrelation between Internet Users Density and EI

0,888

0,861

0,850

0,864

0,721

0,794

0,789

0,854

0,5 0,6 0,7 0,8 0,9 1

Telephone lines density

Mobile cellular density

Internet users density

Broadband subscr. Density

Spearmen's Index

Education Index UNDP GDP per capita

28

Educational tool for telecom Educational tool for telecom coursescoursesSignalization in telecommunication networks

The main goal is to give the best understanding of signalization principles by means texts, pictures and animations

Co-author: Vladimir Prozorov

Several examples: Channel associated signalization

29

Educational tool for telecom Educational tool for telecom coursescoursesSignalization in telecommunication networks

The main goal is to give the best understanding of signalization principles by means texts, pictures and animations

Co-author: Vladimir Prozorov

Several examples: Common channel signalization №7

30

Models and algorithms for live

streaming networkswith feedback

Albert Abilov

Seminar at Chair of Telecommunications,TU Dresden

October 21, 2008

What would i like to tell What would i like to tell today abouttoday about Multimedia Streaming Conception Problems and approaches for P2P Streaming Robustness in P2P Streaming Networks Mathematical models for the Streaming System Estimation and Feedback control algorithms Simulation for simplest case Some questions for the research

2

This research has been supported be Swedish Institute and DAAD

Multimedia streaming Multimedia streaming conceptionsconceptions Client/Server Architecture

– Routers can use IP Multicast or IP unicast protocols– Clients (PCs) are directly connected to Server– Difficult realization new protocols on the network– Limited deployment on the Internet, content-

distribution-networks technologies are costly yet– IP multicast requires support at all routers

Peer-to-Peer Overlay Architecture– Last several years multicast services are more and

more considered at the application level– Overlay approach to Multicast is used– Clients act as both customer and intermediate

nodes– Peers convey the live streaming content– IP Unicast on the IP level is used– P2P conception is used for Network Architectures– Low cost for deployment

Main approaches for live streaming

IP level

Application level

Client

Server

Router

IP level

Application level

Peer

Server

Router

3

Problems and approaches for P2P Problems and approaches for P2P streamingstreaming Large population of users requires high transmission capacity at the

streaming server P2P approach aims to alleviate these demands

– Peer uses the upload bandwidth for distributing media stream The number of peers in the overlay may change rapidly Streams are transmitted with end-to-end delays There may be interrupts of connection caused by the frequent joining and

leaving of individual peers The network must be as more as flexible the must be self-adapting and have possibility to change its parameters

(network structure, FEC redundancy, etc) dynamically in depends on changing conditions

Main problems for P2P streaming

Main approaches are considered today by research community Push Method

– Single-Tree-Based Overlays Routing based Overlay Peer-Based Overlay

– Multiple-Tree-Based Overlays Pull Method

– Mesh-Based Overlays

…are not considered as perspective

4

Problems and approaches for P2P Problems and approaches for P2P streamingstreaming

Routing-Based Overlay– Reproduce the native IP Multicast structure– Servers are mounted with programmable routing functions– Servers use upstream capacity for conveying stream data – All servers are stable and do not leave network– High reliability, low flexibility and high cost

Peer-Based Overlay– Peers use upstream capacity for conveying stream data so as to reduce the server load– Each segment (packet) reaches the peer only through one path in the tree– Frequent disconnections of peers can significant degrade the service quality– The most famous projects: SpreadIt, PeerCast, ESM, NICE, D3amcasT and others– The tree structure is fully controlled by Server

Push Method: Single-Tree-Based Overlay

Routing-Based Overlay for single-tree structure

Application level Server

Leaves

Disjoin

Join

Join

Peer-based Overlay for Single-Tree Streaming

Application level Server

Join/Disjoin

Programmable Router

5


Single-Tree Overlay– All segments (packets) go through the same paths– When the peer (parent) leaves the tree:

Server reconstructs the tree structure All its descendants experience loss packets until the tree is

repaired– Buffered data of new parent can preserve segments for children

Push Method: Multiple-Tree vs Single-Tree-Based Overlay

Multiple-Tree Overlay– The segments are allocated in a round robin manner (in block) to as

many as there are trees– Different segments reach the peer through independent overlay paths– If one peer leaves the tree then only one segment is lost in the block – Network or FEC redundancy can recover lost segments– Redundancy requires addition capacity– The most famous projects: SplitStream, CoopNet, P2PCast and other

6


Download Bandwidth (DB) of the Peer– If the peer has DB and UB larger than the required bandwidth (streaming bandwidth – SB) then it can be part of network– The peer can convey at least one stream– If UB/SB ≥ N and DB/SB ≥ N then peer have possibility to relay N different streams

Upload Bandwidth (UB) Allocation Policies– UB = SB

UB of peer is evenly divided among the trees Each peer relays the stream only to one child in each tree Min.breadth-max.depth concept

– UB ≥ N*SB Peer relays data in one tree only, but to several (N) child peers Min.depth-max.breadth concept More difficulty to maintain the trees in a dynamic scenario

Push Method: Download (DB) and Upload (UB) Bandwidth of the Peers

…

DB

UB

UB/SB ≥ NSB

DB

UB

…

SBUB/SB = N

SB – Stream bandwidth

DB – Download Bandwidth

UB – Upload Bandwidth

7

Problems and approaches for P2P Problems and approaches for P2P streamingstreaming Segments pulling concept

– Host interested to content requires server a list of peers which are currently received the same content

– Host established a partner relationship with subset of peers – Each host receives a buffer maps from its partners– Each peer cashes and shares segments of stream by

request– If the peer cannot receive the segment from one peer it

requires (pulls) it from other peer– The most famous projects: CoolStreaming, PPLive

and other

Pull Method: Mesh-Based Overlay

Segment 2

1 2

34

5

Segment 1

Segment 2

1 3

9

4

8

Segment 1

…

Block

6

7

2

5

Advantages– Dynamic overlay which follows the changes

of network conditions– Better Resilience

Deficiencies– Additional delay at each peer due to

requests (pulling) data – Frequent exchange of control messages– Random, hardly predictable performance– Non static network structure

8

Robustness in P2P streaming Robustness in P2P streaming networksnetworks

The main reasons of segment losses in P2P streaming networks– Physical, Data link and Network and Transport Layers

Delays, congestion, etc Physical and Data link and Transport Layers can have mechanisms for data

recovering (FEC, ARQ)– Application layer

Node churns (joins and leaving network) All descendants of leaving peer can not receive segments until the tree is

repaired

Robustness in conditions of node churns

…

…

…

Disjoin

No stream during searching a new peer

Search a new peer

The main methods for recovering the lost data– Physical and Data link and Transport Layers can have mechanisms for data recovering

FEC ARQ

– Application Layer can employ: Multiple Description Coding (MDC) Forward Error Correction (FEC) Multiple-tree Approach Network Redundancy, etc

9


FEC Particularity for P2P Streaming– FEC is not relevant for single-tree-based approach– Packet-level FEC is used– The stream is divided to blocks– Each block has information and redundancy segments

Advantages of FEC for P2P Streaming– The limited lost segments in the block can be reconstructed– There is no delay

Deficiencies of FEC for P2P Streaming– FEC requires additional resource capacity (bandwidth)

Approaches of FEC employment for P2P Streaming– Static FEC (the number of FEC Redundancy Segments is not changed)– Adaptive FEC (the number of FEC Redundancy Segments is regulated in depends on state of the network)– Reed-Solomon code can be used

Forward Error Correction (FEC) for P2P Streaming

RedundantSegments

Data Segments

10

Multiple-Tree Structure– Peer nodes are organized in X trees by centralized managements

protocol– Root (the Server) plays a central role in construction trees– Each node has one child only– S – the number of root’s children– N – the number of peers– I = N/S – the number of layers in the tree– Root sends only one of packets to in a block to its child in given tree

Multiple-Tree-Based Case for UB = SB

FEC Redundancy– X = D + R packets are sent per one block

where D – data; R – redundancy – If at least D packets has been correctly received then the block cam be reconstructed– Required Redundancy Level must be determined by packet loss rate in the network– Peers should report to source about the loss rate they experience– The effective feedback control system must be used

Multiple Tree Structure

11


Measurement of loss packet rate– The packet Loss Rate must be measured in the nodes for each tree

separately– It is necessary to provide a sufficient accuracy of Packet Loss

Estimation 1. Direct Feedback Updates

– Each peer measures Packet Loss Rate and sends updates directly to the Root

– Measurement is made periodically– Root receives N*X updates and can be overloaded

P2P Streaming Structure with feedback (three approaches)

Feedback methods for the P2P streaming

2. Feedback Updates from Leafes (from top to down)– Each children-peer measure stream from its parent-peer, aggregates the results and sent update to its descendant– Only Leaves send the feedback updates directly to he root– The root receive only S*X updates

3. Feedback Updates from Root’s children (from down to top)– Updates are sent from child-peers to parent-peers– Root’s children periodically report the root about measured packet loss rate

12


Measurement of packet loss rate– The root experiences the far less load if it receives updates only from leafs or its children– Accuracy of packet loss tare estimation depends on the sample of measured packets– If the period of updates is one block (X packets) then estimation accuracy is 1/X only– The more blocks is used for measurement, the better accuracy of packet loss estimation– If the period of updates is M block (X packets) then estimation accuracy is 1/MX

Main approaches for the control system (two approaches)1. On-off control system

– Based on step by step increments or decrements of controller output2. Proportional control system

– Number of redundant packets depends on the difference between the calculated and desired loss packet rate

Packet Loss Rate Measurement and Control System

13


Mathematical Models for Mathematical Models for Streaming SystemStreaming System Streaming structure

– Data stream is the sequence blocks (X packets in each block)– The packet is elementary entity in our studies– The packet arrives to the peer through links with different delays or it is lost– tk = X/v – interval between moments k; where v – packet rate

Models of direct data streaming channel

14

Channel description on the base of the states equation approach– – Data Vector which defined on the Galois Field of the second order GF(2) and describes one block of packets

– – Error Vector which describes the loss packet process

– Estimation Vector is result of summation and by rule of module 2

where – transition matrix of data source; – transition matrix of error source; – group operation of summation by module 2; k = 0, 1, … – vector estimation phase

The format of Data Vector is represented as

The Estimation Vector can be presented as

– Example: , where the second packet is lost

Model of direct data streaming channel without FEC and feedback

gXkkAgX kk

,11

gWkkBgXgZ kkk

,111

Mathematical Models for Mathematical Models for Streaming SystemStreaming System

gX

gW

gZ gX

gW

Description of the Data Stream Source

Description of the Direct Channel

kkA ,1 kkB ,1

15

Models of the Direct Channel and Data Streaming Source

Model of direct data streaming channel without FEC and feedback


– The model describes the streaming process in dynamics

Example of the Data Streaming Source Model:

Model of the channel

16

The Streaming Source Model

Model of direct channel with fixed FEC-redundancy and without feedback


– The FEC-Redundancy in the Block does not depend on data streaming content but must depend on the feedback information– The streaming source with redundancy can be presented as two separate source:

Data source without redundancy Redundancy source

– Denote the Vectors:

– the Data Vector; – Redundancy Vector;

– These vectors have the same dimensionality X

– The format of Data Vector is represented as:

– The format Redundancy Vector is represented as:

– In case of fixed redundancy the Vector has one resolved combination only– “1” in the position of denotes a presence of redundant packet in the block

gD

gR

gR

gR

17

The Streaming Source Model– Equation of the streaming source with taking

into account the redundancy:

where – transition matrix of redundancy source

– The format of Streaming Vector is represented as:

Model of direct channel with fixed FEC-redundancy and without feedback


gRkkCgDkkAgX kkk

,1,11

kkC ,1

Model of the streaming source

– The example of the streaming vector presentation:

– “1” denotes a presence of the data packet; “0” denote a presence of redundancy packet– Streaming Vector has only one resolved combination in case of fixed redundancy

This model does not describe the control algorithm generation of the redundancy vector

18

Measurements timing– In general the redundancy can be

controlled with tk period, i.e. interval of one block

– But the number of segments is not enough for required accuracy

– The peer must receive as more as possible packets for the good loss rate measurement (M blocks)

– m – the phase of estimation– tm = tkM – period of measurement

Packet loss rate measurements


Feedback timing (two approaches)1. Feedback packets are sent periodically

– The period of feedbacks sending is tmF , where F is a number of measurements– If F = 1 then feedback is sent on the each measurement– The feedback period tf value is a research question– The more feedback period, the more accuracy of packet loss estimation but the

slower reaction of the control system2. Feedback packets are sent upon request of node

– Threshold criterion– If the estimation of the packet loss rate in the peer is less or more than some

threshold then it sends appropriate feedback

Feedback timing structure

19

Mathematical Models for Mathematical Models for Streaming SystemStreaming System Control timing

– Redundancy is controlled by root– One peer only can not be the

reason for changing redundancy– The peers send the feedback

packets to the root independently and asynchronously

– Feedback packets can experience the different delays

– The control period is not synchronous with feedback period

– The root makes decision every control interval

Decrease redundancy Increase redundancy Do not change redundancy

Control system for redundancy

Control timing structure

Control interval– tc = tfC – period of control, where C – average number of the feedbacks from the peer– If C = 1 then root makes control decision at the average on each feedback interval

20

Mathematical Models for Mathematical Models for Streaming SystemStreaming System Model of the Streaming Source

– Model takes into account the root and leafs only (without aggregation packet loss rate measurements from other peers)

– Error Vector takes into account the character of passing packets through network

– There are S peer-leafs

– Model of the streaming source with redundancy (Streaming Vector):

Model of the streaming with feedback from leafs (simple case)

P2P Streaming with feedback from leafs

gRkkCgDkkAgX ckk

,1,11

21

Mathematical Models for Mathematical Models for Streaming SystemStreaming System Model of the channels

– Model of the channels from root to leafs (Estimation Vectors):

– General model of the channels:

Model of the streaming with feedback from leafs (simple case)

gWkkBgXgZ

gWkkBgXgZ

gWkkBgXgZ

Sk

Sk

Sk

kkk

kkk

,1

,1

,1

11

221

21

111

11

gZ

gZ

gZ

gZSk

k

k

k

1

21

11

1

Structure of P2P streaming network with feedbacks from leafs

22


The network structure– Each peer measures packet loss

rate (PLR)– Summarizes it with the PLR of its

child– Send result and number of

measurement to the parent– Stream source is unified for all

peers (this is simplification)

Model of the channels– General model of the channels

Model of the streaming with feedback and aggregation of loss packet rates

Structure of P2P streaming network with feedbacks and aggregation of loss rates

gZgZgZ

gZgZgZ

gZgZgZ

gZSIk

Sk

Sk

Ikkk

Ikkk

k

121

11

21

221

211

11

121

111

1

23


Model of the channel taking account the FEC– The model of the channel (Estimation Vector) considered above took not into account the FEC procedure– Introducing of a Correction Vector will describe the FEC

– The role of is to compensate the Error Vector

– The compensation ability depends on redundancy (the more redundancy, the mere ability for Error Vector’s compensation)

– Equation for the Estimation Vector:

– The Vector depends on redundancy vector and it is defined as follow:

where r and w are binary elements of redundancy and error vectors, respectively

– Redundancy in the block will recover all lost packets if the weight of the Error Vector is equal or less than the weight of redundancy vector

Model of the streaming with FEC

24

gY

gW

gY

gYgWgXgZ

gY

gR

X

kk

X

kk

X

kk

X

kk

rw

rwgW

11

11

if0

if

gY

=

Estimation and Feedback control Estimation and Feedback control algorithmsalgorithms The PLR as indicator of the network state

– Measurement of the network state is made by counting of loss packets in the measurement period

– Packet Loss Rate indicator is Q– Two type of PLR are considered:

PLR before FEC (Q) PLR after FEC (QFEC)

– The Control Unit of peer receives one of this indicator and uses it for processing

Packet Loss Rate (PLR) estimation

25

Error Vector as the presentation of the packet loss

– The Error Vector:

where

– The weight of the Error Vector is the sum of its “1” elements:

X

jjwW

1

Estimation and Feedback control Estimation and Feedback control algorithmsalgorithms The PLR before FEC

– Sum of the weights of all Error Vectors in a measurement period is Packet Los Rate indicator:

Packet Loss Rate (PLR) estimation

26

M

k

X

jjwQ

1 1

The PLR after FEC– FEC-redundancy recovers the lost packets– PLP after FEC (QFEC) is difference between lost packets before FEC and packets recovered after FEC in the measurement interval

– The Correction Vector:

where– The weight of the Correction Vector is the sum of its “1” elements:

– PLR after FEC is described sa follow:– – Estimation of the packet loss probability after FEC:

X

jjyW

1

M

k

X

jjFEC yQQ

1 1

– Estimation of the packet loss probability before FEC is defined as Q divided by number of all packets sent during measurement interval:

Estimation and Feedback control Estimation and Feedback control algorithmsalgorithms

The two type of control system

– Open-loop system No feedbacks Control unit is used to obtain desirable response

– Close-loop system The feedback is used Measured output of system is compared with desired

value Control system affects to minimize the difference

Control System (close-loop feedback)

27

The questions about the control algorithms

– When the feedbacks must be sent?

– When the system must react on the changing network state

– How the system must react

Estimation and Feedback control Estimation and Feedback control algorithmsalgorithms On-off control method

– The control system change redundancy in stepwise manner

– Ste-by-step increment or decrement of the controller output (redundancy)

– The max and min desirable thresholds are given beforehand

Proportional method– The rounded up average

number of the lost packets per block before FEC is evaluated


28

– The controller compares this estimation with the current redundancy

– The difference is required number of the redundancy packets to add

– The redundancy is defined as follow:

SM

w

R

S

i

M

k

X

jj

1 1 1

Estimation and Feedback control Estimation and Feedback control algorithmsalgorithms

The control system with given target– The controller tries to make closer the channel

state to the desired value– The proportional controller is used– Error of control e is the difference between desired

packet loss probability p and estimated one

– The main goal is to minimize e

– Relation between the output ∆R and input e is given by a proportional factor γ


29

FECp̂

– The input-output function is:

∆Rc+1 = γ · ec

– The number of redundancy is defined as follow:

Rc+1 = Rc + ∆Rc+1

– This approach uses reaction of the control system for changing redundancy

The proportional factor γ can be defined by simulations

Simulations (for the simple case)Simulations (for the simple case)

The conditions of the simulation– Only leafs send periodically feedback updates directly to the root– The root averages the updates and makes the decision on changing FEC redundancy– The stream rate is 160 kbps– The two cases are compared:

1. Fixed FEC2. Adaptive FEC

– The size of fixed block is 20 packets (16 for data and 4 for redundancy– The number of leafs is 20)– Feedback delay is 0 sec– Measurement interval the PLR and control interval are 5 sec (interval is 100 packets)– Given Packet Loss Probability is changed by SIN function from 0 to 0.5– The simulation period is 5 min

Case for the simulation

30

Simulations (for the simple case)Simulations (for the simple case)

The results of the simulation– The packet loss probability before

FEC is shifted to right than given one– There is random deviation is because

of inaccuracy of measurements– In general the packet and block loss

probabilities after FEC for adaptive FEC are less than for fixed FEC

– Adaptive changing redundancy reflects the work of the control system

Case for the simulation

31

The questions for the researchThe questions for the research

Update the mathematics for the mesh-based and network redundancy cases Introduce new algorithms Compare average (in time) loss probabilities for fixed and adaptive FEC cases Comparable performance evaluation both without redundancy and with constant redundancy: - dependencies of packet loss probability estimation on join and disjoin rate of nodes for case without

FEC; - dependencies of packet loss probability estimation after FEC on layer of network for dif-ferent join

and disjoin rate of nodes and redundancy; - dependencies of packet loss probability estimation after FEC on given packet loss prob-ability for

different redundancy and layers of network; - other performances. Comparable performance evaluation both without redundancy and with variable (adaptive) redundancy: - dependencies of gain (ratio of packet loss probability after FEC with fixed and adaptive redundancy)

on given packet loss probability with fixed measurement period; - dependencies of gain on measurement period with other fixed parameters; - dependencies of gain on number of nodes (layers of network) with other fixed parameters; - comparative QoS performances with taking account packet delay and feedback; - other performances. Considered cases for mesh-based and network redundancy models and algorithms:

32

Thank youThank you

researches in telecommunications at izhevsk state technical university

Documents