Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 Bk 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 Ak 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 Wk01 g 0 Pош f D0 B(k+1,k) Control unit 0 X k 1 g Wk11 g Backward channel D D 1 Pош f D1 u mb 1 g u ma 1 g 1 Wks1 g Quality of channels estimation Qm S Quality analysis i ˆ Wks1 g Z ks1 g tm Receive B(k+1,k) S 1 Pош f D S 1 i D WkS11 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 Ak 1, k X k g Description of the Data Stream Source Z k 1 g X k 1 g Bk 1, k Wk g Description of the Direct Channel where Ak 1, k – transition matrix of data source; Bk 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: Dg – 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 Ak 1, k Dk g Ck 1, k Rk g where Ck 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 Ak 1, k Dk g Ck 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 k11 g 2 Z k 1 g Z k 1 g Z kS1 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 k111 g Z k121 g Z k1I 1 g 22 2I 21 Z k 1 g Z k 1 g Z k 1 g Z k 1 g Z kS11 g Z kS21 g Z kSI1 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: 26 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 27 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 i1 k 1 j 1 SM 28 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 29 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 30 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 31 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