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Albert Abilov
Researches in
Telecommunications
at Izhevsk State Technical
University
Seminar at Chair of Telecommunications,
TU Dresden
October 21, 2008
What would i like to tell today 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 Dresden
 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 months
3
Where do i live and work
My District and City
Udmurt Republic: www.udmurt.ru
Izhevsk: www.izh.ru
 Udmurt Republic is one of 85
districts of Russia
 Izhevsk is Capitol of
Udmurt Republic
 Izhevsk is located
about 1 100 km from Moscow
 Population of Izhevsk is
about 650 000 people
4
Where do i live and work
My University
Izhevsk State Technical University:
 Izhevsk State Technical
University is one of 4 State
universities in Izhevsk
 It was created in 1952
 There are about 10 000
students and 14 faculties in
the most of technical areas.
 University has cooperation and
student/researcher exchanges
with many Russians and
abroad universities.
5
www.inter.istu.ru
Where do i live and work
Chair of Telecommunication
Networks and Systems:
Our Chair
http://www.istu.ru/unit/prib/net
Equipments and methods
of quality control
Radio Engineering
Telecommunication
networks and systems
Faculty of
Instrumentation
Engineering
Laser systems
 Department (Chair) was created
– Telecom networks and
switching systems
– Transmit telecom systems
Electrical Engineering
Design of radio-equipment
at 1998
 Specialities for students
Physics
 Labs
– Switching systems
– Electronics lab
– Communication networks
6
Several words about me
ALBERT ABILOV
Candidate of Science, Docent
in Izhevsk State Technical University
My contacts
Address:
7, Studencheskaya str.
Izhevsk, 426069, RUSSIA
Office:
Izhevsk State Technical University
Building 1, Floor 4, Room 403
Phone/fax: +7 3412 580399
Mobile: +7 9128 562202
E-mail: [email protected]
WWW: http://www.istu.ru/unit/prib/net/abilov
7
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science (PhD) theses
Design and research of digital signal estimation and optimal utilization of
frequency resource algorithms in mobile telecommunication system
The main tasks:
 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
8
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Math model of digital mobile communication channel



X k 1 g   k  1, k xk g   k  1, k u k g 



Z k 1 g   X k 1 g   Bk  1, k Wk g 

X  g  – state vector;

W g  – errors vector;


Z  g  – estimation vector; u k  g  – control vector;
D – delay;
Channel А

u k g 

Wk 1 g 
Source of control
k  1, k 
Source of information
codewords
Channel В
Source of errors

x k 1 g 

X k 1 g 

Z k 1 g 
For estimation
Ak  1, k 
9
D
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Model of control channel searching in mobile system

x k 1  g 
Source of information
codewords
A(k+1,k)
Sources of errors
 

Wk01  g 
0
Pош
 f D0
B(k+1,k)
 
Control
unit
0

X k 1  g 

Wk11  g 
Backward
channel
D
D
1
Pош
 f D1

u mb 1  g 

u ma 1  g 
1

Wks1  g 
Quality of channels
estimation
Qm S
Quality
analysis
i
ˆ
Wks1  g 

Z ks1  g 
tm
Receive
B(k+1,k)

S 1
Pош
 f D S 1
i
D


WkS11  g 
Errors
estimation
if (s = i)
ˆ
X ki 1  g 
Criterion of channel quality is
minimum of bit errors ratio
(BER)
S 1
0
S-1
0
B(k+1,k)
t search t syn
tl
K 1
1
D
0
n 1
1
tk
10
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Algorithm of digital information receiving in signaling channels
of “VOLEMOT” mobile system. Results of simulation
Codeword
structure
Algorithm which was:
compare of two nearby
codewords during fix time
11111000
P ,
0.993507
пп
Correct receive for
former algorithm
1
Pпп1
Pлт
0.8
pps_sg
pls_sg
Correct receive for
Pпп2 offered algorithm
i
0.6
i
ppm1_sg
False receive for
2
offered algorithm Pлт
i
0.4
plm1_sg
i
False receive for 1
former algorithm Pлт
0.2
0
Pош
0
3
Correct receive for
offered algorithm with
reduced probability
of false receive
Information
1 10
3
1 10
0.01
Pe
i
0.1
Bit error probability
11
0.998
Probability of codeword receive
Probability of codeword receive
Synchronization
Offered and realized algorithm:
voting method
11
P ,
0.993619
пп
1
Pлт
0.8
Pпп4
0.6
Pпп1
pps_sg
i
pls_sg
i
ppm2_sg
i
3
Pлт
plm2_sg 0.4
i
ppm3_sg
i
Pпп3
1
Pлт
0.2
0
Pош
0
3
1 10
3
1 10
0.01
Pe
i
Bit error probability
0.1
11
0.998
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Algorithm of digital information receiving in signaling channels
of “VOLEMOT” mobile system. Results of simulation
Former Codeword
structure
Offered synchronization byte: 01111110
11111000
Offered
Information
Codeword
structure
01111110
Synchronization
Information
Probability of codeword receive
Synchronization
Pпп1
1
0.8
pps1_sg
i
Pпп6
0.6
ppm3_sg
i
Correct receive for
former algorithm with
former synchro-byte
3
пп
P
ppm6_sg 0.4
i
Pпп1
0.2
0
Correct receive for
offered algorithm with
new synchro-byte
Correct receive for
offered algorithm with
former synchro-byte
Pош
0
3
1 10
3
1 10
0.01
Pe
i
Bit error probability
12
0.1
11
0.998
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Simulation model of control channel searching in mobile system
BS1
0
x ПС
D1
DПС
BS4
x1
BS2
x4
D4
BS3
x2
D2
x3
x max
x
D3
13
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 Simulation model of control channel
i
F0.04
searching in mobile system
0.04
0.035
Criterion of efficiency: average bit
errors ratio on the simulation
interval
M 1
Fсрi 
0.025
0.02
F3
0.015
 Qmi B
0.01
m 0
0
0
0
M
5
10
0
20
25
x , км
24.888889
0.04
i=1
0.03
= 0,01
F2
i
0.025
F4
0.02
i=4
i=3
i=2
i
Fпор
0.015
0.01
= 0,002832
Fсрi .п
0.005
0
0
0
5
10
БС1
БС4
0
Offered control algorithm:
F
15
i
0.035
Former control algorithm:
i
ср.п
аPerr
Fi
Fgr
F
Fсрi .д
0.005
Threshold for changing channel:
i
ср.д
i=3
F1
i
0.04
Fпор
i=2
i=1
0.03
= 0,001587
14
бPerri
15
20
БС2
БС3
25
x , км
24.888889
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system
 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)
15
Supervisor: Prof. Vladimir V. Khvorenkov
Candidate of Science theses
Design and research of algorithms of digital signal estimation and optimal
utilization of frequency resource in mobile telecommunication system

Realization and operational testing (trial)
of algorithms on real system

0,04
i = 11
0,007
i = 14
i = 12
0,03
0,006
0,025
0,005
Average BER
BER
0,035
How threshold for changing channel influence on
average BER and gain (results of simulation and
experiment)
0,02
0,015
i
ср.д
F
0,01
0,004
0,003
0,002
0,001
0,005
0
0
1
5
Fсрi .д
9
13
17
21
= 0,004809
25
29
33
37
41
45
49
53
0
57
0,005
0,01
0,015
0,02
0,025
0,03
0,025
0,03
Treshold for changing channel
Measurements, m
а Former control algorithm
Simulation
Trial test
4,5
4
0,04
3,5
0,035
BER
i = 15
Gain
3
i = 11
0,03
i = 14
i = 12
2,5
0,025
2
0,02
1,5
1
i
Fпор
0,015
Gain:
0,01
0,5
0
Fсрi .п
0,005
1
F
5
9
13
0,01
0,015
0,02
Threshold for changing channel
0
i
ср.п
0,005
17
21
= 0,002538
25
29
33
37
41
45
49
53
Measurements, m
б Offered control algorithm
57
k выигр 
16
Fсрi .д
i
ср.п
F
Simulation
Trial test
Average BER for former algorithm
Average BER ratio for offered algorithm
Co-author: Roman Semieshin
Applications for network planning
Tool for cellular radio subsystem planning
Parameters of network
 Realization of model in network planning tool
Features of tool:
•
approximate coverage of cell calculation;
•
network configuration planning
Interface
Base station parameters
Factors of Hata model
Switching center parameters
17
Co-author: Alexey Susekov
Applications for network planning
Tool for urban and rural telephone networks planning
 Realization of famous models in network planning tool
Features of tool:
•
traffic calculation;
•
trunk lines calculation;
•
for urban and rural applications;
•
network planning and
traffic forecasting.
Interface
It is now utilized for:
educational process
Switching station parameters
Types of traffic
18
Advisor and Principal Investigator: Albert Abilov
Telecom infrastructure development
Research 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
Basic objectives and tasks of the conception:
 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
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
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 World trends of info-communications development
– General analysis of info-communications development
Latin
America
9%
3,5
Main telephone lines
Mobile cellular subscribers
Internet users
Broadband subscribers
2,5
2
1,5
Oceania
1%
USA and
Canada
17%
Europe
23%
Asia
46%
1
0,5
Key ICT indicators in dynamic
а) Developed economies
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
1991
Subscribers, billion
3
Africa
4%
Percentages of Internet users over the world (2007 year)
b) Developing economies
20
c) Poor economies
10
10
а) Developed economies
8
6
4
2
0
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
21
Colombia
Thailand
Czech Republic
Hungary
Estonia
Slovak Republic
Poland
Lithuania
Mexico
Chile
Latvia
Russia
Venezuela
Malaysia
Turkey
Argentina
Romania
Brazil
Bulgaria
Belarus
0
Telephone lines density, %
20
Ethiopia
30
Nepal
50
Rwanda
40
Uganda
60
Telephone lines density, %
70
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
c) Poor economies
Nicaragua
Afghanistan
Nigeria
Mongolia
Yemen
Pakistan
Senegal
Zambia
Kenya
Benin
Ghana
Cambodia
Guinea
Mozambique
Tanzania
Gambia
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
Norway
Iceland
Switzerland
Ireland
Denmark
United States
Sweden
Netherlands
Austria
Finland
United Kingdom
Australia
Japan
Belgium
France
Canada
Germany
Kuwait
Italy
United Arab Emirates
New Zealand
Spain
Hong Kong, China
Cyprus
Greece
60
70
50
30
40
10
20
0
b) Developing economies
Telephone lines density, %
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 World trends of info-communications development
– Wired telephone communication dynamics
80
80
c) Poor economies
1991
2007
2005
2003
2001
1999
1997
1995
1993
22
Ethiopia
Nicaragua
Afghanistan
Nigeria
Mongolia
Yemen
Pakistan
Senegal
Zambia
Kenya
Benin
Ghana
Cambodia
Guinea
Mozambique
Tanzania
Gambia
Uganda
Nepal
Rwanda
а) Developed economies
80
60
70
40
50
20
30
0
10
Mobile cellular density, %
Czech Republic
Hungary
Estonia
Slovak Republic
Poland
Lithuania
Mexico
Chile
Latvia
Russia
Venezuela
Malaysia
Turkey
Argentina
Romania
Brazil
Bulgaria
Belarus
Thailand
Colombia
Mobile cellular density, %
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
Emirates
New Zealand
Spain
Hong Kong, China
Cyprus
Greece
Norway
Iceland
Switzerland
Ireland
Denmark
United States
Sweden
Netherlands
Austria
Finland
United Kingdom
Australia
Japan
Belgium
France
Canada
Germany
Kuwait
Italy
United Arab
100
90
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Mobile cellular density, %
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 World trends of info-communications development
– Mobile cellular communication dynamics
b) Developing economies
45
40
35
30
2007
2006
2005
2004
2003
2002
2001
2000
23
25
20
15
10
5
0
Broadband subscribers density, %
50
Czech Republic
Hungary
Estonia
Slovak Republic
Poland
Chile
Lithuania
Mexico
Latvia
Russia
Venezuela
Malaysia
Turkey
Argentina
Romania
Brazil
Bulgaria
Belarus
Thailand
Colombia
Internet users density, %
2007
2005
2003
2001
1999
1997
а) Developed economies
1995
1993
c) Poor economies
Norway
Iceland
Switzerland
Ireland
Denmark
United States
Sweden
Netherlands
Austria
Finland
United Kingdom
Australia
Japan
Belgium
France
Canada
Germany
Kuwait
Italy
United Arab Emirates
New Zealand
Spain
Hong Kong, China
Cyprus
Greece
100
90
80
70
60
50
40
30
20
10
0
1991
2007
2005
2003
2001
1999
1997
1995
1993
1991
Greece
Norway
Iceland
Switzerland
Ireland
Denmark
United States
Sweden
Netherlands
Austria
Finland
United Kingdom
Australia
Japan
Belgium
France
Canada
Germany
Kuwait
Italy
United Arab Emirates
New Zealand
Spain
Hong Kong, China
Cyprus
80
70
50
60
40
30
10
20
0
b) Developing economies
Internet users density, %
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 World trends of info-communications development
– Internet dynamics
100
90
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 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
Developed
coun
tries
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
The Spearmen ranking method enables to
estimate, how close the parameters
interrelation is.
n
ρ  1
6   ( Ri  R j ) 2
k 1
n(n 2  1)
were k – sequence number of country; n
–
number
of
countries
under
examination; Ri, Rj – country ranks
according to respective indicators.
* 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
Developed
countr
ies
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
Indicator
24
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 ICT and Economics
Germany
Russia
Telephone lines density, %
China
USA
Japan
Czech Rep.
Brazil
Saudi Arabia
10
India
Namibia
Zimbabwe
Nigeria
1
1000
Denmark
Russia Czech Rep.
Mobile cellular density, %
100
100
China
Germany
Brazil
Nigeria
Saudi Arabia Japan
Denmark
USA
Namibia
India
10
Rwanda
Zimbabwe
1
Rwanda
0
100
1000
10000
0
100
100000
1000
GDP per capita, $
Czech Rep.
Internet users density, %
Brazil
USA
Denmark
Germany
China
Russia
10
Saudi Arabia
Nigeria
Namibia
1
Rwanda
100
Broadband subscribers density, %
100
Zimbabwe
100000
GDP per capita, $
Japan
India
10000
0
Denmark
Germany
Japan
Chech Rep.
USA
10
Brazil
China
Venezuela
Saudi Arabia
Russia*
1
India
0
100
1000
10000
100000
100
GDP per capita, $
1000
10000
GDP per capita, $
25
100000
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 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
0,888
0,861
0,850
0,864
Spearmen Index ρ
Dynamics of Spearmen’s Index
Spearmen's index
1
0,8
0,6
0,4
0,2
0,8
0,6
0,4
0,2
0,8
0,6
0,4
0,2
Interrelation between Internet Users Density and GDP per capita
26
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
1990
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
Interrelation between Mobile Cellular Density and GDP per capita
1
Spearmen's index
1994
1993
1992
1991
1990
1989
1988
1987
1985
Interrelation between Telephone lines Density and GDP per capita
1986
0
0
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
Spearmen's index
1
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 ICT and Educational level
Germany
100
Denmark
1000
USA
Czech Rep. Japan
Russia
Czech Rep. Germany Denmark
Saudi Arabia
Brazil
Saudi Arabia
10
India
Namibia
Zimbabwe
Nigeria
1
100
Mobile cellular density, %
Telephone lines density, %
China
USA
Brazil Japan
Namibia
China
Nigeria
India
10
Rwanda
Zimbabwe
1
Rwanda
0
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0
0,1
0,2
0,3
Edication Index
0,6
0,7
0,8
0,9
1
100
Denmark
Germany
Broadband subscribers density, %
Internet user's density, %
0,5
Education Index
USA Denmark
Japan
Germany
Czech Rep.
Brazil
Saudi Arabia
100
0,4
India
Zimbabwe
10
Russia
Nigeria
China
Namibia
Rwanda
1
Japan
Czech
Rep.
10
USA
China
Brazil
Saudi Arabia
Russia
1
India
0
0
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0,1
0,2
0,3
0,4
0,5
0,6
Education Index
Education Index
27
0,7
0,8
0,9
1
Advisor and Principal Investigator: Albert Abilov
Impact economics & education on ICT
Research Project № 07-07-07009:
Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/)
 ICT and Educational level
Indicators of interrelation
Indicators of mutual influence
of info-communication (2007) and Educational Index (2006)
Telephone lines density
Mobile subscr. density
Internet users density
Broadband subscr. density
0,0212e7,6275x
1,7416e4,0555x
0,0565e6,6709x
5E-5e11,924x
0,854
0,721
0,794
0,789
Equation of correlation line y
Spearmen Index ρ
Dynamics of Spearmen’s Index
1
0,8
Spearmen's Index
Spearmen's Index
1
0,6
0,4
0,2
0
0,8
0,6
0,4
0,2
0
2000
2001
2002
2003
2004
2005
2006
2007
Interrelation between Telephone lines Density and EI
2000
2001
2002
2003
2004
2005
2006
2007
Interrelation between Internet Users Density and EI
1
0,864
Spearmen's Index
Broadband subscr. Density
0,789
0,8
0,850
Internet users density
0,794
0,6
0,861
Mobile cellular density
0,4
0,721
0,888
0,854
Telephone lines density
0,2
0,5
0
0,6
0,7
0,8
Spearmen's Index
2000
2001
2002
2003
2004
2005
2006
2007
Interrelation between Mobile Cellular Density and EI
Education Index UNDP
28
GDP per capita
0,9
1
Co-author: Vladimir Prozorov
Educational tool for telecom courses
Signalization in telecommunication networks
 The main goal is to give the best understanding of signalization principles by
means texts, pictures and animations
Several examples: Channel associated signalization
29
Co-author: Vladimir Prozorov
Educational tool for telecom courses
Signalization in telecommunication networks
 The main goal is to give the best understanding of signalization principles by
means texts, pictures and animations
Several examples: Common channel signalization №7
30
Albert Abilov
Models and algorithms for
live streaming networks
with feedback
Seminar at Chair of Telecommunications,
TU Dresden
October 21, 2008
What would i like to tell today 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
This research has been supported be Swedish Institute and DAAD
2
Multimedia streaming conceptions
Main approaches for live streaming
Application level

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
Server
Client
Router
IP level
Application level
Server
Peer
Router
IP level
3
Problems and approaches for P2P streaming
Main problems for P2P streaming
 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 approaches are considered today by research community
 Push Method
– Single-Tree-Based Overlays


Routing based Overlay
Peer-Based Overlay
…are not considered as perspective
– Multiple-Tree-Based Overlays
 Pull Method
– Mesh-Based Overlays
4
Problems and approaches for P2P streaming
Push Method: Single-Tree-Based Overlay
Application level
 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
5
Server
Programmable
Router
Join/Disjoin
Routing-Based Overlay for single-tree structure
Application level
Server
Disjoin
Join
Leaves
Join
Peer-based Overlay for Single-Tree Streaming
Problems and approaches for P2P streaming
Push Method: Multiple-Tree vs Single-Tree-Based Overlay
 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
 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
Problems and approaches for P2P streaming
Push Method: Download (DB) and Upload (UB) Bandwidth of the Peers
UB/SB = N
 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
7
DB SB
…
UB
SB – Stream bandwidth
DB – Download Bandwidth
UB – Upload Bandwidth
UB/SB ≥ N
SB
DB
UB
…
Problems and approaches for P2P streaming
Pull Method: Mesh-Based Overlay
 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
 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
2
1
4
3
5
Segment 1
Segment 2
Block
1
2
3
4
5
6
7
8
9
Segment 1
Segment 2
Robustness in P2P streaming networks
Robustness in conditions of node churns
 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
Disjoin
Search a
new peer
…
…
…
No stream during
searching 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
Robustness in P2P streaming networks
Forward Error Correction (FEC) for P2P Streaming
 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
10
Data
Segments
Redundant
Segments
Robustness in P2P streaming networks
Multiple-Tree-Based Case for UB = SB
 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 Structure
 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
11
Robustness in P2P streaming networks
P2P Streaming Structure with feedback (three approaches)
 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
Feedback methods for the P2P streaming
be overloaded
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
Robustness in P2P streaming networks
Packet Loss Rate Measurement and Control System
 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 output
2. Proportional control system
– Number of redundant packets depends on the difference between the calculated
and desired loss packet rate
13
Mathematical Models for Streaming System
Models of direct data streaming channel
 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
14
Mathematical Models for Streaming System
Model of direct data streaming channel without FEC and feedback
 Channel
 description on the base of the states equation approach
– X  g  – Data Vector which defined on the Galois Field of the second order GF(2) and
describes one block of packets

– W  g  – Error Vector which describes the loss packet process





X
g
W
and  g  by rule of module 2
Z  g  – Estimation Vector is result of summation


X k 1 g   Ak  1, k X k g 
Description of the Data Stream Source



Z k 1 g   X k 1 g   Bk  1, k Wk g 
Description of the Direct Channel
where Ak  1, k  – transition matrix of data source; Bk  1, k  – 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
15
Mathematical Models for Streaming System
Model of direct data streaming channel without FEC and feedback
 Models of the Direct Channel and Data Streaming Source
– The model describes the streaming
process in dynamics
 Example of the Data Streaming
Source Model:
Model of the channel
16
Mathematical Models for Streaming System
Model of direct channel with fixed FEC-redundancy and without feedback
 The Streaming Source Model
– 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:


Dg  – the Data Vector; R  g  – 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 R  g  has one resolved combination only

– “1” in the position of R  g  denotes a presence of redundant packet in the block
17
Mathematical Models for Streaming System
Model of direct channel with fixed FEC-redundancy and without feedback
 The Streaming Source Model
– Equation of the streaming source with taking
into account the redundancy:



X k 1 g   Ak  1, k Dk g   Ck  1, k Rk g 
where Ck  1, k  – transition matrix of
redundancy source
– The format of Streaming Vector is
represented as:
Model of the streaming source
– The example of the streaming vector presentation:
This model does not describe the
control algorithm generation of
the redundancy vector
– “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
18
Mathematical Models for Streaming System
Packet loss rate measurements
 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

Feedback timing (two approaches)
1.
2.
Feedback timing structure
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 system
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
19
Mathematical Models for Streaming System
Control system for redundancy
 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 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 Streaming System
Model of the streaming with feedback from leafs (simple case)
 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):



X k 1 g   Ak  1, k Dk g   Ck  1, k Rc g 
P2P Streaming with feedback
from leafs
21
Mathematical Models for Streaming System
Model of the streaming with feedback from leafs (simple case)
 Model of the channels
– Model of the channels from root
to leafs (Estimation Vectors):





1

1
1
Z k 1 g   X k 1 g   B k  1, k Wk g 
2

2
2
Z k 1 g   X k 1 g   B k  1, k Wk g 
S

S
S
Z k 1 g   X k 1 g   B k  1, k Wk g 
– General model of the channels:

 Z k11 g 

 2

Z k 1 g    Z k 1 g 

 Z kS1 g 


Structure of P2P streaming network with
feedbacks from leafs
22
Mathematical Models for Streaming System
Model of the streaming with feedback and aggregation of loss packet rates
 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



 Z k111 g  Z k121 g  Z k1I 1 g 

 22
 2I
  21

Z k 1 g    Z k 1 g  Z k 1 g  Z k 1 g 



 Z kS11 g  Z kS21 g  Z kSI1 g 


23
Structure of P2P streaming network with
feedbacks and aggregation of loss rates
Mathematical Models for Streaming System
Model of the streaming with FEC
 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 Y  g  will describe the FEC


– The role of Y  g  is to compensate the Error Vector W  g 
– The compensation ability depends on redundancy (the more redundancy, the mere
ability for Error Vector’s compensation)




– Equation for the Estimation Vector: Z  g   X  g   W  g   Y  g 


R


Y
g
– The Vector
depends on redundancy vector  g  and it is defined as follow:
X
X

W g  if  wk   rk

k 1
k 1
Y g  = 
X
X
0
if  wk   rk

k 1
k 1
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
24
Estimation and Feedback control algorithms
Packet Loss Rate (PLR) estimation
 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
 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
W   wj
j 1
25
Estimation and Feedback control algorithms
Packet Loss Rate (PLR) estimation
 The PLR before FEC
– Sum of the weights of all Error Vectors
in a measurement period is Packet Los
M X
Rate indicator:
Q   w j
k 1 j 1
–
Estimation of the packet loss probability before FEC is defined as Q divided by number of all packets
sent during measurement interval:
 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: W 
M
Q
X
 Q   y j
FEC
– PLR after FEC is described sa follow:
k 1
–
– Estimation of the packet loss probability after FEC:
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j 1
X
y
j 1
j
Estimation and Feedback control algorithms
Control System (close-loop feedback)
 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
 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
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Estimation and Feedback control algorithms
Control System (close-loop feedback)
 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
– 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:
 S M X

w
  j 
R   i1 k 1 j 1 
SM






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Estimation and Feedback control algorithms
Control System (close-loop feedback)
 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
p̂FEC
– Relation between the output ∆R and
input e is given by a proportional
factor γ
– The input-output function is:
∆Rc+1 = γ · ec
– The number of redundancy is defined as follow:
The proportional factor γ
can be defined by
simulations
Rc+1 = Rc + ∆Rc+1
– This approach uses reaction of the control system for changing redundancy
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Simulations (for the simple case)
Case for the simulation

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 FEC
2.
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
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Simulations (for the simple case)
Case for the simulation

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
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The 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 you