Researches in Telecommunications at Izhevsk State Technical University

1. Researches in Telecommunications at Izhevsk State Technical University Albert Abilov Seminar at Chair of Telecommunications, TU Dresden October 21, 2008

2. 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

3. 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

4. Where do i live and work Udmurt Republic: www.udmurt.ru Izhevsk: www.izh.ru My District and City • 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

5. Where do i live and work Izhevsk State Technical University: www.inter.istu.ru My 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

6. Telecommunication networks and systems Faculty of Instrumentation Engineering Where do i live and work Chair of Telecommunication Networks and Systems: http://www.istu.ru/unit/prib/net Equipments and methods of quality control Our Chair Radio Engineering Physics Laser systems Electrical Engineering • Department (Chair) was created at 1998 Design of radio-equipment • Specialities for students • Telecom networks and switching systems • Transmit telecom systems • Labs • Switching systems • Electronics lab • Communication networks 6

7. 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: abilov@udm.ru WWW: http://www.istu.ru/unit/prib/net/abilov 7

8. 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 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: 8

9. Math model of digital mobile communication channel 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 – state vector; – errors vector; – control vector; – estimation vector; D – delay; Source of errors Source of control Source of information codewords For estimation 9

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 • Model of control channel searching in mobile system Source of information codewords Quality of channels estimation Sources of errors Errors estimation Criterion of channel quality is minimum of bit errors ratio (BER) 10

11. Algorithm of digital information receiving in signaling channels of “VOLEMOT” mobile system. Results of simulation Supervisor: Prof. Vladimir V. Khvorenkov Codeword structure Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Algorithm which was: compare of two nearby codewords during fix time Offered and realized algorithm: voting method Correct receive for offered algorithm with reduced probability of false receive Correct receive for former algorithm Correct receive for offered algorithm False receive for offered algorithm Probability of codeword receive Probability of codeword receive False receive for former algorithm Bit error probability Bit error probability 11

12. Algorithm of digital information receiving in signaling channels of “VOLEMOT” mobile system. Results of simulation Supervisor: Prof. Vladimir V. Khvorenkov Codeword structure Codeword structure Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Offered synchronization byte: 01111110 Former Correct receive for offered algorithm with new synchro-byte Correct receive for offered algorithm with former synchro-byte Correct receive for former algorithm with former synchro-byte Probability of codeword receive Offered Bit error probability 12

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 searching in mobile system 13

14. Simulation model of control channel searching in mobile system Supervisor: Prof. Vladimir V. Khvorenkov = 0,002832 = 0,001587 Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Criterion of efficiency: average bit errors ratio on the simulation interval Threshold for changing channel: = 0,01 Former control algorithm: Offered control algorithm: 14

15. 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) 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 15

16. Supervisor: Prof. Vladimir V. Khvorenkov = 0,004809 = 0,002538 Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system • How threshold for changing channel influence on average BER and gain (results of simulation and experiment) • Realization and operational testing (trial) of algorithms on real system Former control algorithm Gain: Average BER for former algorithm Offered control algorithm Average BER ratio for offered algorithm 16

17. Realization of model in network planning tool Co-author: Roman Semieshin Applications for network planning Tool for cellular radio subsystem planning Parameters of network • Features of tool: • approximate coverage of cell calculation; • network configuration planning Base station parameters Interface Factors of Hata model Switching center parameters 17

18. Realization of famous models in network planning tool Co-author: Alexey Susekov Applications for network planning Tool for urban and rural telephone networks planning • 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

19. 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 • 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

20. 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 Key ICT indicators in dynamic Percentages of Internet users over the world (2007 year) а) Developed economies b) Developing economies c) Poor economies 20

21. 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 b) Developing economies а) Developed economies c) Poor economies 21

22. 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 а) Developed economies c) Poor economies 22

23. 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 b) Developing economies а) Developed economies c) Poor economies 23

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/) • 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 The Spearmen ranking method enables to estimate, how close the parameters interrelation is. 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 24

25. 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 25

26. 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/) Indicators of mutual influence of info-communication (2007) and economics (2006) • ICT and Economics Dynamics of Spearmen’s Index 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 26

27. 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 27

28. 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/) Indicators of mutual influence of info-communication (2007) and Educational Index (2006) • ICT and Educational level Dynamics of Spearmen’s Index Interrelation between Telephone lines Density and EI Interrelation between Internet Users Density and EI Interrelation between Mobile Cellular Density and EI 28

29. The main goal is to give the best understanding of signalization principles by means texts, pictures and animations Co-author: Vladimir Prozorov Educational tool for telecom courses Signalization in telecommunication networks Several examples: Channel associated signalization 29

30. The main goal is to give the best understanding of signalization principles by means texts, pictures and animations Co-author: Vladimir Prozorov Educational tool for telecom courses Signalization in telecommunication networks Several examples: Common channel signalization №7 30

31. Models and algorithms for live streaming networkswith feedback Albert Abilov Seminar at Chair of Telecommunications, TU Dresden October 21, 2008

32. 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

33. Application level Application level Server Server Peer Client Router Router IP level IP level Multimedia streaming conceptions Main approaches for live streaming • 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 3

34. 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 • Multiple-Tree-Based Overlays • Pull Method • Mesh-Based Overlays …are not considered as perspective 4

35. Server Server Application level Application level Programmable Router Disjoin Join Join/Disjoin Leaves Join Problems and approaches for P2P streaming Push Method: Single-Tree-Based Overlay • 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 Routing-BasedOverlay for single-tree structure Peer-based Overlay for Single-Tree Streaming 5

36. 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

37. UB/SB≥N UB/SB=N SB SB DB DB … … UB UB Problems and approaches for P2P streaming Push Method: Download (DB) and Upload (UB) Bandwidth of the Peers • 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 ≥ Nthen 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 SB –Stream bandwidth DB – Download Bandwidth UB – Upload Bandwidth 7

38. Block … 2 1 2 1 3 4 3 4 5 6 5 7 8 9 Segment 1 Segment 2 Segment 1 Segment 2 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

39. Disjoin Search a new peer … … … No stream during searching a new peer 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 • 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

40. Data Segments Redundant Segments 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

41. 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

42. 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 • 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 Feedback methods for the P2P streaming • 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 • 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

43. 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/Xonly • The more blocks is used for measurement, the better accuracy of packet loss estimation • If the period of updates is Mblock (X packets) then estimation accuracy is 1/MX • Main approaches for the control system (two approaches) • On-off control system • Based on step by step increments or decrements of controller output • Proportional control system • Number of redundant packets depends on the difference between the calculated and desired loss packet rate 13

44. 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 momentsk; where v – packet rate 14

45. 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 • – 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 Description of the Data Stream Source Description of the Direct Channel 15

46. Mathematical Models for Streaming System Model of direct data streaming channel without FEC and feedback • The model describes the streaming process in dynamics • Models of the Direct Channel and Data Streaming Source • Example of the Data Streaming Source Model: Model of the channel 16

47. Mathematical Models for Streaming System 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 • The Streaming Source Model 17

48. 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: where – transition matrix of redundancy source • The format of Streaming Vector is represented as: Model of the streaming source This model does not describe the control algorithm generation of the redundancy vector • 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 18

49. 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 structure • Feedback timing (two approaches) • 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 tfvalue 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

50. 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