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(1)AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications. Ph.D. Dissertation Jakub Rachwalski. Analysis of Packet Loss Pattern for Concatenated Transmission Channels Using Burst Ratio Parameter Supervisor: Prof. dr hab. in˙z. Zdzisław Papir.

(2) AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Telecommunications al. Mickiewicza 30, 30-059 Kraków, Poland tel. +48 12 6345582 fax +48 12 6342372 http://www.agh.edu.pl http://www.iet.agh.edu.pl http://www.kt.agh.edu.pl. c Jakub Rachwalski, 2016 Copyright All rights reserved LATEXtemplate by Rafał Stankiewicz.

(3) Acknowledgements. This thesis would not have been possible without the support that I received from a number people. I would like to express my appreciation to all of them and thank a few individually. First of all I would like to express my sincere gratitude to my supervisor, Professor Zdzisław Papir, for his continuous guidance, valuable advisory, and immense knowledge. I feel truly honoured that I had opportunity to perform my research under his supervision. I would like to thank Krzysztof Rusek, PhD, who introduced me, with his broad expertise, to analysis and simulation of the Markov process. It provided me with essential foundations required for the part of my research related to Markov chains. I am grateful to Professor Sebastian Möller, Professor Alexander Raake and Blazej Lewcio, PhD, for the seminar that took place at Telekom Innovation Laboratories. The seminar resulted in the design of the validation methodologies which are a fundamental part of the dissertation. Moreover, I would like to thank my family for all their love and encouragement, especially my parents who inspired me and supported me every step of the way. It is their enormous efforts that brought me to this point. Finally, my greatest gratitude is expressed towards my loving, encouraging and patient wife Minako. She has always stood behind me sacrificing her own priorities and encouraged me in the times of doubt. Undoubtedly, this dissertation would not be possible without Minako’s unconditional support..

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(5) Abstract. V oIP is a commonly used technology in voice transmission and real-time communication. In comparison to legacy analogue communication, V oIP enables richer functionality as well as lower costs of deployment. On the other hand, providing high quality of end-user experience in V oIP applications is a non-trivial task as it requires careful design of the underlying IP network as well as continuous network monitoring. In order to achieve this, all key factors affecting voice quality, e.g. codec, jitter and packet loss, must be taken into consideration. One of the factors that influence quality perceived by the end user is the packet loss pattern. The perception of V oIP quality changes if the packet loss takes place in bursts or is scattered. Packet loss patterns are quantified by the Burst Ratio parameter. Its significance is indicated by the fact that Burst Ratio is one of the input parameters used by the ITU -T E-model, widely used for analytical voice quality assessments. Thus far, Burst Ratio has only been defined for end-to-end transmission scenarios. In this case, in order to calculate the Burst Ratio of a transmission, the characteristics of the complete, end-to-end transmission path must be measured. This dissertation describes the development of a new model which defines the end-to-end value of the Burst Ratio parameter when the transmission is carried over multiple concatenated transmission channels and only the characteristics of each individual intermediate channel are known. In order to define the model, a thorough analysis of concatenated packet transmission using packet loss models has been conducted. The resulting model is based on two theorems that describe the Burst Ratio parameter when the transmission is carried over multiple concatenated transmission channels. The dissertation also includes the validation of the theorems R using Matlab and the N S2 network simulation tool. The verification measures errors introduced by the theorems and analyses the factors which influence their accuracy..

(6) vi. All verification results demonstrate a high accuracy of the theorems. They prove that the model precisely determines the value of the Burst Ratio parameter when the transmission path consists of multiple concatenated channels. As a result, the model can support precise packet network design and monitoring, and therefore it enables a higher quality of V oIP applications. Keywords vice, V oIP. Burst Ratio, BurstR, bursty packet loss, E-model, Quality of Ser-.

(7) Streszczenie. V oIP jest powszechnie używaną technologią do transmisji głosu i komunikacji w czasie rzeczywistym. W stosunku do tradycyjnej komunikacji analogowej, V oIP posiada szerszą gamę funkcji, a także niższe koszty instalacji. Z drugiej strony, dostarczanie aplikacji V oIP charakteryzujących się wysoką jakością dźwięku nie jest łatwym zadaniem, ponieważ wymaga to odpowiednio zaprojektowanej sieci IP, a także ciągłego jej monitoringu. Aby to osiągnąć, wszystkie kluczowe parametry mające wpływ na jakość odczuwaną przez końcowego użytkownika, np. kodek, jitter oraz straty pakietów, muszą zostać wzięte pod uwagę. Jednym z parametrów wpływających na odczuwaną jakość jest strukura strat pakietów. Odczuwanie jakości zmienia się w zależności od tego, czy stracone pakiety grupują się czy też są rozproszone. Struktura strat pakietów mierzona jest za pomocą parametru Burst Ratio. Jego wagę podkreśla fakt, iż jest on jednym z parametrów wejściowych E-model-u, powszechnie używanej analitycznej metody szacowania jakości dźwięku stworzonej przez ITU -T . Do tej pory, Burst Ratio został zdefiniowany jedynie dla przypadku, gdy ścieżka transmisyjna rozpatrywana jest jako jedna integralna całość. W rozprawie opisany jest nowy model umożliwiający obliczenie wartości parametru Burst Ratio dla całkowitej transmisji, gdy prowadzona jest ona przez wiele łączonych szeregowo kanałów transmisyjnych, a charakterystyki każdego kanału transmisyjnego są mierzone osobno. W celu stworzenia modelu została dokonana dogłębna analiza łączonych kanałów transmisyjnych za pomocą modeli strat pakietów. Zaprezentowany model korzysta z dwóch twierdzeń opisujących parametr Burst Ratio w transmisjach prowadzonych przez wiele łączonych kanałów transmisyjnych. W rozprawie twierdzenia te są nie tylko udowodnione, ale także R zweryfikowane za pomocą symulacji przeprowadzonych w Matab i symulatorze N S2. W czasie weryfikacji obliczony został błąd wprowadzony przez twierdzenia, a także analizowane były parametry mające wpływ na ich dokładność..

(8) viii. Zaprezentowanie wyniki wskazują na dużą dokładność twierdzeń. Fakt ten dowodzi, że stworzony model pozwala na precyzyjne obliczenie wartości parametru Burst Ratio w przypadku, gdy transmisja prowadzona jest przez wiele łączonych kanałów transmisyjnych. Dzięki temu stworzony model może zostać użyty do precyzyjnego projektowania i monitorowania sieci pakietowych, a przez to wpłynąć na uzyskiwanie lepszej jakości dźwięku w aplikacjach V oIP . Słowa kluczowe Burst Ratio, BurstR, grupowe straty pakietów, E-model, jakość usług, QoS, V oIP.

(9) Contents. Contents. ix. List of Figures. xi. List of Tables. xv. List of Abbreviations. xvii. 1 Introduction 1.1 Thesis and Goal of the Research 1.2 Methodology . . . . . . . . . . . 1.3 Publications and Cooperation . . 1.4 Structure of the Dissertation . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 1 3 4 5 6. 2 Quality of VoIP 2.1 Voice Quality Assessment . . . . 2.1.1 Subjective Tests . . . . . 2.1.2 Objective Tests . . . . . . 2.2 VoIP Quality Related Parameters 2.3 Conclusions . . . . . . . . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. 9 10 11 12 15 25. 3 Packet Loss Patterns in Concatenated Channels 3.1 Packet Loss Models . . . . . . . . . . . . . . . . . . 3.1.1 Bernoulli Model . . . . . . . . . . . . . . . 3.1.2 Two-State Markov Chain Model . . . . . . 3.1.3 Gilbert Model . . . . . . . . . . . . . . . . . 3.1.4 Gilbert-Elliot Model . . . . . . . . . . . . . 3.1.5 Four-State Markov Chain Model . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. 27 27 30 33 36 39 42.

(10) x. Contents. 3.2. 3.1.6 k-State Markov Chain Model . . . . . . . . . . . . . . . . . 47 3.1.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Burst Ratio in Concatenated Channels . . . . . . . . . . . . . . . . 52. 4 Verification 4.1 Numerical Verification . . . . . . . . 4.1.1 BurstRΣ Verification . . . . . 4.1.2 BurstR0 Σ Verification . . . . 4.2 Verification via NS2 Simulations . . 4.2.1 BurstRΣ Verification . . . . . 4.2.2 BurstR0 Σ Verification . . . . 4.2.3 Model Verification for Codecs 4.3 Conclusions . . . . . . . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. 61 62 65 69 73 76 85 94 101. 5 Summary. 103. Appendices. 109. A Stationary State Analysis A.1 Two-State Markov Chain Model . . . . . . . . . . . . . . . . . . . A.2 Four-State Markov Chain Model . . . . . . . . . . . . . . . . . . A.3 k-State Markov Chain Model . . . . . . . . . . . . . . . . . . . . B Transition Probabilities of Two Concatenated Channels. 109 . 110 . 111 . 112 115. C NS2 Simulations 123 C.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 C.2 Results analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 References. 125.

(11) List of Figures. 1.1. Burst Ratio in concatenated channels. . . . . . . . . . . . . . . . .. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8. Categorization of voice quality assessment techniques . End-to-End connection referred by E-model. . . . . . . Mapping between E-model R rating and M OS values Quality factors of IP telephony services . . . . . . . . Model of voice transmission over packet networks. . . Voice quality vs. one way delay . . . . . . . . . . . . . Voice quality vs. packet loss rate . . . . . . . . . . . . Voice quality vs. Burst Ratio . . . . . . . . . . . . . .. 3.1 3.2 3.3. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. Packet transmission sequence . . . . . . . . . . . . . . . . . . . . . Probability of losing packets in series for Bernoulli packet loss model Packet transmission sequence generated for Bernoulli packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Two-state Markov chain packet loss model. . . . . . . . . . . . . . 3.5 Packet transmission sequence generated for two-state Markov chain packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Gilbert packet loss model . . . . . . . . . . . . . . . . . . . . . . . 3.7 Packet transmission sequence analysed with Gilbert packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Packet transmission sequence generated for Gilbert packet loss model 3.9 Gilbert-Elliot packet loss model . . . . . . . . . . . . . . . . . . . . 3.10 Packet transmission sequence analysed with Gilbert-Elliot packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Packet transmission sequence generated for Gilbert-Elliot packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3 10 13 14 16 18 19 23 24 28 31 32 33 35 37 38 39 40 41 42.

(12) xii. List of Figures. 3.12 Four-state Markov chain packet loss model. . . . . . . . . . . . . 3.13 Packet transmission sequence analysed with four-state Markov chain packet loss model . . . . . . . . . . . . . . . . . . . . . . . 3.14 Packet transmission sequence generated for four-state Markov chain packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.15 k-state Markov chain packet loss model. . . . . . . . . . . . . . . 3.16 Packet transmission sequence analysed with k-state Markov chain packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.17 Packet transmission sequence generated for k-state Markov chain packet loss model . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.18 Burst Ratio in concatenated channels. . . . . . . . . . . . . . . . 3.19 Two merged channels considered as one Markov chain. . . . . . . 3.20 Concatenation of k + 1 channels. . . . . . . . . . . . . . . . . . . 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19 4.20 4.21 4.22 4.23 4.24 4.25 4.26. Overview of numerical verification method. . . . . . . . . . . . . Distribution of δΣ for Scenario 1 . . . . . . . . . . . . . . . . . . Distribution of δΣ for Scenario 2 . . . . . . . . . . . . . . . . . . Distribution of δΣ for Scenario 3 . . . . . . . . . . . . . . . . . . Distribution of δΣ for Scenario 4 . . . . . . . . . . . . . . . . . . 0 Distribution of ∆BurstRΣ for Scenario 1 . . . . . . . . . . . . . 0 Distribution of ∆BurstRΣ for Scenario 2 . . . . . . . . . . . . . 0 Distribution of ∆BurstRΣ for Scenario 3 . . . . . . . . . . . . . 0 Distribution of ∆BurstRΣ for Scenario 4 . . . . . . . . . . . . . Generic topology used in N S2 simulations . . . . . . . . . . . . . Distribution of δΣ probability for N S2 simulations . . . . . . . . Relationship between δΣ and packet loss rate . . . . . . . . . . . Relationship between δΣ and packet loss rate P pl < 10% . . . . . Relationship between δΣ and number of packets . . . . . . . . . . Relationship between δΣ and number of packets less than 15,000 Relationship between δΣ and duration of simulation . . . . . . . Relationship between δΣ and BurstR . . . . . . . . . . . . . . . Relationship between δΣ and number of concatenated channels . Relationship between δΣ and packet size . . . . . . . . . . . . . . Relationship between δΣ and bandwidth . . . . . . . . . . . . . . Relationship between δΣ and packet interarrival time . . . . . . . 0 Distribution of ∆BurstRΣ probability for N S2 simulations . . . 0 Relationship between ∆BurstRΣ and packet loss rate . . . . . . 0 Relationship between ∆BurstRΣ and packet loss rate P pl < 10% 0 Relationship between ∆BurstRΣ and number of packets . . . . . 0 Relationship between ∆BurstRΣ and number of packets less than 15,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 43 . 44 . 46 . 47 . 48 . . . .. 50 53 54 57. . . . . . . . . . . . . . . . . . . . . . . . . .. 63 66 67 67 68 70 71 71 72 74 77 78 79 80 80 81 82 83 83 84 84 86 87 88 88. . 89.

(13) xiii. List of Figures 0 4.27 Relationship between ∆BurstRΣ and duration of simulation . . 0 4.28 Relationship between ∆BurstRΣ and BurstR . . . . . . . . . . . 0 and number of concatenated 4.29 Relationship between ∆BurstRΣ channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4.30 Relationship between ∆BurstRΣ and packet size . . . . . . . . . 0 4.31 Relationship between ∆BurstRΣ and bandwidth . . . . . . . . . 0 4.32 Relationship between ∆BurstRΣ and packet interarrival time . . 4.33 Relationship between δΣ and number of concatenated channels for G.711 codec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.34 Relationship between δΣ and number of concatenated channels for G.723.1 codec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.35 Relationship between δΣ and number of concatenated channels for G.729A codec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.36 Relationship between δΣ and number of concatenated channels for GSM.EFR codec . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4.37 Relationship between ∆BurstRΣ and number of concatenated channels for G.711 codec . . . . . . . . . . . . . . . . . . . . . . . 0 4.38 Relationship between ∆BurstRΣ and number of concatenated channels for G.723.1 codec . . . . . . . . . . . . . . . . . . . . . . 0 4.39 Relationship between ∆BurstRΣ and number of concatenated channels for G.729A codec . . . . . . . . . . . . . . . . . . . . . . 0 4.40 Relationship between ∆BurstRΣ and number of concatenated channels for GSM.EFR codec . . . . . . . . . . . . . . . . . . . .. . 89 . 90 . . . .. 91 92 92 93. . 95 . 96 . 96 . 97 . 98 . 98 . 99 . 99. A.1 Two-state Markov chain packet loss model. . . . . . . . . . . . . . 110 A.2 Four-state Markov chain packet loss model. . . . . . . . . . . . . . 111 A.3 k-state Markov chain packet loss model. . . . . . . . . . . . . . . . 112 B.1 B.2 B.3 B.4. Two transmission channels building up one transmission path. Two-state Markov chain packet loss model. . . . . . . . . . . Markov chains for two merged channels. . . . . . . . . . . . . Two merged channels considered as one Markov chain. . . . .. . . . .. . . . .. . . . .. 115 116 117 119.

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(15) List of Tables. 3.1 3.2. 4.1 4.2 4.3 4.4 4.5 4.6. Symbols of packet loss probability and Mean Burst Length calculated using parameters of each packet loss model . . . . . . . . . . 29 Parameters of packet loss models used to analyse a packet transmission sequence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Numerical simulation settings . . . . . . . . . . . . . . . . . . . . . Statistical parameter values for numerical model validation . . . . Statistical parameter values for numerical validation of simplified model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of codecs and their transmission characteristics . . . . . . . . Relative error of BurstRΣN calculation – summary of statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 Simplification error of BurstRΣN – summary of statistical analysis. 65 68 73 94 101 102. B.1 Relationship between state of both concatenated channels and complete transmission path . . . . . . . . . . . . . . . . . . . . . . 116 B.2 Relationship between chain states in detailed and aggregated approaches of transmission path analysis . . . . . . . . . . . . . . . . 119 C.1 N S2 simulation parameters . . . . . . . . . . . . . . . . . . . . . . 123.

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(17) List of Abbreviations. Abbreviations ABR ACR AM R CBR CCR DCR Dif f Serv F EC FQ GSM A IntServ IP ITU -T LBR LP C LT E M OS N AM N S2 P CL P ESQ P LC P OLQA P ST N QoS. Adaptive Bit Rate Absolute Category Rating Adaptive Multi-Rate Constant Bit Rate Comparison Category Rating Degradation Category Rating Differentiated Services Forward Error Correction Fair Queuing GSM Association Integrated Services Internet Protocol ITU Telecommunication Standardization Sector Low-Bitrate Redundancy Linear Predictive Coding Long Term Evolution standard Mean Opinion Scale Network Animator Network Simulator 2 Packet Loss Concealment Perceptual Evaluation of Speech Quality Packet Loss Concealment Perceptual Objective Listening Quality Assessment Public Switched Telephone Network Quality of Service.

(18) xviii. List of Abbreviations. RED ROHC RT P SF Q SN R T CP U DP V AD V BR V oIP V oLT E VoWi-Fi. Random Early Detection Robust Header Compression Real-time Transport Protocol Stochastic Fair Queuing Signal-to-Noise Ratio Transmission Control Protocol User Datagram Protocol Voice Activity Detection Variable Bit Rate Voice over IP Voice over LTE Voice over WiFi. Definitions 0 ∆BurstRΣ δΣ 0 δΣ µ Π σ A b BL BLi Bpl BurstR BurstRi BurstR1+2 0 BurstRΣN. BurstRΣN C Dr Ds. 0 simplification error of BurstRΣN relative error of BurstRΣN calculation 0 relative error of BurstRΣN calculation mean Markov chain states probabilities matrix standard deviation Advantage Factor packet loss probability within B state of packet loss model packet loss Burst Length length of ith packet loss burst packet loss robustness factor Burst Ratio Burst Ratio of the ith channel Burst Ratio of complete transmission path consisting of two channels Burst Ratio of complete transmission path consisting of N concatenated channels calculated using the simplified model Burst Ratio of complete transmission path consisting of N concatenated channels mean plus one standard deviation of the length of packet loss bursts observed in the trace sensitivity of the user interfaces at receive side sensitivity of the user interfaces at send side.

(19) List of Abbreviations. g Id Ie Ie-eff IS k m M M BL M BLb M BLg M BLG M BLM M BLR M BLGE M BLMkCh M BLM4Ch Nc OLR p pij P (i) ppl pplM pplG pplGE pplM4Ch pplMkCh P pl P pli P plΣk Pr Ps q R R0. packet loss probability within G state of packet loss model delayed impairment factor equipment impairment factor effective equipment impairment factor simultaneous impairment factor number of packets transmitted number of packets lost transition probability matrix Mean Burst Length Mean Burst Length of B state Mean Burst Length of G state Mean Burst Length of Gilbert model Mean Burst Length of two-state Markov chain model Mean Burst Length of Bernoulli model Mean Burst Length of Gilbert-Elliot model Mean Burst Length of k-state Markov chain model Mean Burst Length of four-state Markov chain model circuit noise [dBm0p] overall loudness rating [dB] probability of transition from F to L state of two-state Markov chain model probability of transition from i to j state of Markov chain probability of the i-state of Markov chain packet loss probability packet loss probability for two-state Markov chain model packet loss probability for Gilbert model packet loss probability for Gilbert-Elliot model packet loss probability for four-state Markov chain model packet loss probability for k-state Markov chain model packet loss rate packet loss rate of the ith channel packet loss rate of transmission over k concatenated channels A-weighted sound pressure level of room noise at receive side [dB(A)] A-weighted sound pressure level of room noise at send side [dB(A)] probability of transition from L to F state of two-state Markov chain model transmission quality rating basic signal-to-noise transmission rating factor. xix.

(20) xx. List of Abbreviations. RLR SLR SM T R T Ta T ELR Tr W EP L. receive loudness rating [dB] send loudness rating [dB] sidetone masking rating [dB] mean one-way delay of talker echo path [ms] overall delay [ms] talker echo loudness rating [dB] round-trip delay for listener echo [ms] weighted echo path loss for listener echo [dB].

(21) Well-informed people know it is impossible to transmit the voice over wires. Even if it were, it would be of no practical value. Boston Post, 1865.

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(23) 1. Introduction. Since its invention telephony has been undergoing constant development and improvement in order to meet increasing needs of connectivity and quality expectations. In the early 21st century, telephony started to utilize Internet Protocol (IP ) technology. Voice over IP (V oIP ) accelerated the pace of innovation in communications by reducing the cost and enabling the development of completely new products and services. Since then, voice services have been incorporated into computer programs and a range of devices such as televisions and smart watches. Currently, wired voice communication is served by packet networks and the well-established Public Switched Telephone Network (P ST N ). Even if end users are connected to the PSTN network, in many cases packet networks are facilitated for trunk voice transmission and internetworking with other service providers. Moreover, many countries are considering resigning (USA [2]) or have resigned (Austria [1]) from copper networks and are switching completely to packet networks operated over fibre or wireless technologies. Despite the fact that the number of fixed phone subscribers (1,099 million [21]) still outnumbers V oIP subscribers (224 million [53]), the trend of switching completely to packet-based voice transmission is clear. A similar transformation is taking place in wireless voice communication, albeit at a faster pace. The Long Term Evolution standard (LT E) [14] was lunched publicly in 2009 [3]. It is the first mobile telecommunication standard to define voice transmission using packet switching. Since then the LT E has reached 480 million subscribers worldwide in 2014 and this number is forecast to grow by 350% by 2019 [73]. As V oIP is a well-established and successful technology, solutions relying on V oIP continue being developed. The GSM Association (GSM A) defined Voice over LTE (V oLT E) [37] which uses V oIP in LT E networks and results in simplified network design. Moreover, mobile network operators are launching.

(24) 2. 1. Introduction. Voice over WiFi (VoWi-Fi) [62], which is intended to offload cellular networks and help delivering voice services in buildings, where cellular network coverage is frequently limited. It is clear that the multi-billion dollar business of voice communication is heavily dependent on V oIP technology and the dependency will continue to grow. Therefore, it is essential for services based on V oIP to meet all customer quality requirements. Unlike P ST N , where all voice channels are separated from each other and the required resources are reserved, V oIP itself does not provide any resource reservation or channel separation. In IP networks voice packets are transmitted in the same manner as other data packets and they are not prioritized in any way by default. In order to assure customer satisfaction, IP networks must be designed appropriately and the quality of voice communication needs to be constantly monitored. In order to control the quality of voice communication, the quality must be measured first. Several different techniques of automatic voice quality assessment have been developed, including signal-based models such as Perceptual Evaluation of Speech Quality (P ESQ) [8] and Perceptual Objective Listening Quality Assessment (P OLQA) [20]. In signal-based models the voice quality is assessed by comparing a signal which has been transmitted through the network with its original, unmodified form. Another approach are parameter-based models, such as the ITU Telecommunication Standardization Sector (ITU -T ) E-model [19]. In this technique voice quality is assessed by measuring multiple network parameters including packet loss and delay. None of the automatic voice quality assessments are universal and can be used for all purposes; however, when combined they are all used in networks design, monitoring voice quality in V oIP services, thus assuring customer satisfaction. Analysis of transmission networks ([27], [28], [38], [76]) reveals bursty packet loss. In the E-model this effect is quantified with the Burst Ratio (BurstR) parameter, which describes the tendency of packet loss to take place in bursts. In order to protect a transmission from the negative impact of packet loss burstiness on the quality of media transmitted, techniques such as interleaving [74] have been developed. When interleaving is used, data is shuffled before being sent. As a result, if the transmission encounters bursty packet loss, the data after deinterleaving shows a more uniform loss distribution. Burst Ratio has been defined precisely for the end-to-end scenario, when the complete transmission path is treated as a single channel [51]. However, packet transmission in real life takes place over multiple concatenated channels, each influencing the overall value of the Burst Ratio. It has not yet been documented how the Burst Ratio value propagates in such an environment. This research aims to analyse packet loss burstiness based on the Burst Ratio parameter and answer questions on its behaviour in concatenated channels..

(25) 3. 1.1 Thesis and Goal of the Research. In order to guarantee the quality of V oIP services, all factors that affect the quality of voice transmission must be recognized and monitored. This research aims to improve our understanding and enhance analysis of packet loss patterns, one of the factors influencing the quality of digital voice communication in packet networks. As a result, the research helps provide higher quality V oIP services and assure customer satisfaction.. 1.1. Thesis and Goal of the Research. The following thesis is proposed and demonstrated in the dissertation: It is possible to determine the overall value of the Burst Ratio parameter for transmission over multiple concatenated channels of known individual characteristics. The goal of this research is to develop a model for analysing packet loss patterns in concatenated channels using the Burst Ratio formula. Until now research has focused on investigating the influence of packet loss burstiness on voice quality (e.g. [47], [70]). Studies have also been carried out on creating packet loss models that accurately reflect packet loss patterns, including burstiness (e.g. [33], [46], [42] and [65]). However, no research has been carried out into the behaviour of packet loss burstiness (Burst Ratio) when the transmission path consists of multiple concatenated channels.. Ppl1 BurstR1. Ppl2 BurstR2. PplN BurstRN. Channel 1. Channel 2. Channel N. Ppl∑N BurstR∑N = ? Fig. 1.1: Burst Ratio in concatenated channels. The main question this research addresses concerns the value of the Burst Ratio parameter when the transmission is carried over multiple concatenated.

(26) 4. 1. Introduction. channels. The problem is depicted in Fig. 1.1. The end-to-end Burst Ratio value (BurstRΣN ) is determined based on the characteristics of each individual channel: packet loss rate (P pl1 , P pl2 , ... P plN ) and Burst Ratio (BurstR1 , BurstR2 , ... BurstRN ). The result of the research is a model for calculating the Burst Ratio of an end-to-end transmission BurstRΣN , when the characteristics of individual intermediate channels are known. The model is based on two developed theorems that make it possible to calculate Burst Ratio regardless of the number of intermediate channels. Moreover, the research analyses the correlation between transmission parameters and the accuracy of the developed model. To the best of the author’s knowledge, this research is the first to analyse packet loss burstiness for concatenated transmission, focusing on the Burst Ratio parameter. Better understanding of packet loss burstiness is of great importance in network design, optimizing and monitoring. As packet loss burstiness has a considerable impact on the quality of voice transmission, the results of this research provide an innovative contribution to higher quality V oIP services.. 1.2. Methodology. This research provides an approach for analysing packet loss patterns for transmissions over multiple concatenated channels of known individual characteristics, as depicted in Fig. 1.1. The approach is based on a model which has been formulated by analysing the Burst Ratio parameter using two-state Markov chain packet loss models. The research focuses on packet transmissions of V oIP applications over wired channels. Therefore, it considers packet loss caused by congestion and does not analyse the effect of handovers or low Signal-to-Noise Ratio (SN R), which typify wireless transmissions. It also does not take into consideration the effect of packet retransmissions which are not used in real-time applications. Other packet loss recovery techniques, including Packet Loss Concealment, Forward Error Correction and Low-Bitrate Redundancy, may help improve the quality of V oIP application but they do not influence original packet loss patterns and therefore they are not considered within the research. The validity of the formulated model has been verified using two different approaches, both of which analysed the error induced by the model. The error was calculated by comparing the Burst Ratio value observed with the Burst Ratio value calculated using the formulated model. R The first approach used to validate the model was performed in Matlab and included numerical analysis. Packet loss of a transmission channel was simulated with two-state Markov chain. Each simulation contained up to ten channels and the parameters of Markov chains were randomly selected for each. The simulations demonstrated the high precision of the model..

(27) 1.3 Publications and Cooperation. 5. The first verification approach is based on the assumption that a transmission channel can be simplified by a two-state Markov chain. In order to assess the accuracy of the model in a real environment, another verification using the Network Simulator 2 (N S2) tool was performed. N S2 is well recognized in simulating the behaviour of real packet networks. In each iteration all simulation parameters were randomly selected in order to verify the performance of the model in a wide range of possible scenarios. Despite the fact that the model was developed based on a two-state Markov chain, N S2 simulations demonstrated that the model is valid in real packet networks. In order to guarantee the validity of model verification, two different simulation tools were used. They included millions of iterations, each with randomized simulation parameters. Both simulation methods resulted in the conclusion that the proposed model provides a high accuracy in determining Burst Ratio values in concatenated channels, which verifies the problem stated in the dissertation thesis.. 1.3. Publications and Cooperation. During the research, the author published the following papers which include results presented in the dissertation: • [59] Jakub Rachwalski and Zdzisław Papir. Burst Ratio in Concatenated Markov-based Channels. Journal of Telecommunications and Information Technology, (1):3–9, 2014 • [60] Jakub Rachwalski and Zdzisław Papir. Analysis of Burst Ratio in Concatenated Channels. Journal of Telecommunications and Information Technology, (4):65–73, 2015 Publication [59] introduces the problem of packet loss burstiness in concatenated channels. The paper contains a mathematical analysis of Burst Ratio in two transmission channels simplified with two-state Markov chains. It provides a new approach to model packet loss burstiness in two channels, based on the Burst Ratio formula. Further analysis of the proposed model introduces a generalized model for Burst Ratio in n-concatenated channels and a simplified version of that model. The paper also includes results of the numerical validation of the new model. R Simulations performed in Matlab were based on two-state Markov chains. They demonstrate that the model and its simplified versions are accurate and can be used to analyse packet loss burstiness based on the Burst Ratio parameter. They also show that the precision of the model depends on the number of transmitted packets as well as the number of channels the complete transmission path consists of..

(28) 6. 1. Introduction. Publication [60] is an extension of the previous paper, but it mainly focuses on the accuracy of the proposed model. The validation performed in this paper is based on NS2, which is a tool widely used to simulate the environment of packet networks. In the simulations, the V oIP transmission competes for resources with background User Datagram Protocol (U DP ) and Transmission Control Protocol (T CP ) traffic. As bandwidth is a scarce resource, transmission channels are congested, which generates packet loss both in V oIP and background traffic. The simulations verified the high precision of the model in determining the value of the Burst Ratio parameter in multichannel scenarios. Moreover, paper [60] contains an analysis of the relationship between the error of the model and the network and transmission parameters such as the number of transmitted packets or the number of channels. Jakub Rachwalski is also a co-author of a chapter included in the following monography: • [58] Jakub Rachwalski and Maciej Bartkowiak. Ocena jakości sygnałów fonicznych. In Cyfrowe przetwarzanie sygnałów w telekomunikacji, [Digital Signal Processing in Telecommunication: Audio Signals Quality Assessment]. PWN, 2014 Although the chapter does not provide new research results in the field of packet loss burstiness analysis, it presents different methods of audio signal quality assessment. The E-model is a method which helps in the assessment of the quality of transmitted voice. The E-model is based on multiple network and transmission parameters, including packet loss burstiness quantified with Burst Ratio.. 1.4. Structure of the Dissertation. The dissertation consists of four main parts. The first part (Chapter 1, Chapter 2) provides the theoretical background on the research. Chapter 1 is a general introduction to the research topics. Chapter 2 focuses on the quality of voice transmitted over IP networks. It introduces the E-model which is a voice quality assessment method and describes different factors affecting the perceived quality of V oIP , including packet loss patterns (Burst Ratio). Chapter 3 describes the development of a new model for Burst Ratio in concatenated channels. This part of the dissertation starts with an introduction of different packet loss models, which are later used in the analysis of Burst Ratio in concatenated channels. It goes on to include a mathematical analysis of Burst Ratio based on two-state Markov chains. The result of the analysis are two theorems that define the value of Burst Ratio in concatenated channels. The first theorem describes the Burst Ratio of the complete transmission, while the second is a simplification of the model, developed for transmissions characterized with.

(29) 1.4 Structure of the Dissertation. 7. a low packet loss rate. The simplified model indicates that the Burst Ratio of the complete transmission path can be approximated with a harmonic mean of BurstR of all intermediate channels weighted with the packet loss rate of each intermediate channel. Chapter 4 focuses on the validation of the presented model. During the validation, two types of simulations were run in order to calculate the error introduced by the model. The first type is based on the numerical analysis R performed in Matlab . The simulations facilitate the packet loss model based on the two-state Markov chain. The second type of simulations are performed using the N S2 tool which do not depend on any packet loss mode but simulate real packet networks. As well as verifying the accuracy of the formulated model, it also analyses the dependency of the accuracy of the model on the simulation parameters. The final part of the chapter provides an analysis of how the selection of the V oIP codec determines the accuracy of the formulated model. The research is summarized in Chapter 5, which outlines all the goals achieved in the study. The dissertation concludes with Appendices which present auxiliary analysis of Markov chains and describe the techniques used to analyse and validate the simulation results..

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(31) 2. Quality of VoIP. V oIP technologies play a crucial role in connecting people and businesses around the world. It is virtually impossible for individuals and organizations to function without a reliable telephony service, frequently alongside videoconferencing and other communication functionalities. V oIP is used successfully around the world due to the feature-rich services it provides and the cost-effectiveness it guarantees. This success is confirmed by numbers: 224 million of subscribers [53], projected 480 million users of V oLT E in 2018 [69] and $69.6 billion worldwide revenue [53]. Although using simple V oIP applications within an existing packet network is relatively easy, securing their undisturbed operation is a non-trivial task. V oIP applications are fragile to any interruptions generated at the source of the connection, during transmission, or at the destination. Therefore, in order to make a V oIP application reliable, the underlying network needs to be designed robustly and the QoS [16] levels of V oIP service need to be monitored constantly. Designing networks for V oIP applications requires an in-depth understanding of all voice quality deterioration factors such as packet loss or delay and their impact on the perceived quality. Meaningful QoS measurements need to be performed using a standardized voice quality assessment technique. An in-depth understanding of both elements that guarantee the quality of V oIP applications is essential when analysing packet loss patterns, as presented later in the dissertation. This chapter outlines the quality of V oIP services. It introduces the voice quality concept and the techniques used to analyse and assess voice quality, focusing on the ITU -T E-model. It also presents an overview of network and transmission factors that influence the perceived quality of V oIP applications, including packet loss patterns. The chapter develops the foundations essential to the understanding of the importance of packet loss patterns and their effect on voice quality..

(32) 10. 2.1. 2. Quality of VoIP. Voice Quality Assessment. Describing voice quality is a complex task because voice perception is highly subjective. According to [44] quality is "the result of judgment of the perceived composition of an entity with respect to its desired composition". When voice is the entity being investigated, the perceived and desired compositions refer to the auditory characteristic of perceived and desired voice signals. During voice perception, the listener builds up their expectations regarding the anticipated quality based on the environmental characteristics, context and their previous experience. This anticipation affects the desired composition and perception of the perceived signals. As a result, voice quality can vary greatly depending on the listener, their emotions and the context. When designing and monitoring transmission networks, high quality of voice within V oIP applications must be guaranteed. Due to the fact that quality is a personal impression, voice quality requirements are based on an average perceived quality of transmitted voice, often provided as a Mean Opinion Scale (M OS) rating. M OS has been defined in [7] as "the mean of opinion scores, i.e. of the values on a predefined scale that a test subject assigns to their opinion of the performance of the telephone transmission system". In order to reliably analyse the quality of voice transmission, voice assessment techniques have been developed.. sŽŝĐĞYƵĂůŝƚǇƐƐĞƐƐŵĞŶƚ. ^ƵďũĞĐƚŝǀĞ. hƚŝůŝƚĂƌŝĂŶ. KďũĞĐƚŝǀĞ. ŶĂůǇƚŝĐĂů. ^ŝŐŶĂůͲďĂƐĞĚ. WĂƌĂŵĞƚĞƌͲďĂƐĞĚ. Fig. 2.1: Categorization of voice quality assessment techniques. Voice quality assessment techniques can be categorized as presented in Fig. 2.1. They can be divided into two main categories: subjective and objective, depending on whether the assessment is performed by a group of test subjects or performed using an algorithm, respectively. A detailed survey of all methods has been provided in [57]..

(33) 2.1 Voice Quality Assessment. 2.1.1. 11. Subjective Tests. In subjective tests a group of test subjects judges the quality of voice being transmitted. It is the most direct and precise method of voice assessment, as the result depends purely on the assessment being performed by users of a test system. However, subjective tests are costly and time-consuming as they require a group of test subjects to thoughtfully assess the quality of test recordings. Moreover, as audio perception is a personal matter, subjective tests may provide variable results depending on the test subjects. Therefore, results achieved during different subjective tests may be difficult to compare. Although the idea behind objective tests is simple, a wide range of subjective test methods has been defined, both utilitarian and analytical.. Utilitarian methods In utilitarian methods, the aim of the test is to assess the total quality of the tested system, as perceived by an end user. The result is a one-dimensional value of the quality which is based on the assessments provided by the test subject. One type of subjective utilitarian tests are comprehension tests. In these tests the quality of voice is measured by testing the intelligibility of sounds perceived by the test subject. The samples can be as short as single syllables [43] or as long as whole sentences [24]. In these tests the subjects are asked to describe or assess what they hear. The results correspond to the percentage of correct answers. A different type of subjective utilitarian tests are conversation tests, described in [5] and [13]. They focus on the conversational qualities of the tested system and therefore they are regarded as the most relevant for telecommunication systems. Conversational qualities depend not only on the voice signal deterioration but also on the transmission delay and echo. In conversational tests two or more test subjects are given a task, e.g. ordering food over a phone. After achieving the goal the subjects assess the quality of voice. Although conversation tests are highly relevant for telecommunication system assessment, they are not commonly used due to their challenging design and execution. The next category of subjective utilitarian tests are listening-only tests. Although conversational tests provide a higher relevance and realism, listening-only tests are easier to perform and therefore they are the most widely used subjective tests. In this category of tests, the test subject assesses a set of short voice signals on a predefined scale. The final result of the test is the mean value of all scores. Different approaches of listening-only tests include methods and procedures described in [5], [6]: Absolute Category Rating (ACR), Degradation Category Rating (DCR), Comparison Category Rating (CCR) and Continuous Evaluation [36] and Isopreference Tests [66]..

(34) 12. 2. Quality of VoIP. Analytical methods The total quality perceived by a user is the outcome of the multiple quality features of the tested system. Therefore, analytical methods do not aim to assess the total quality of the system, but rather to describe all quality features that influence quality perception. As a result, results given by analytical methods are shown in a multidimensional scale, which captures all features and can be related to the total quality of a system. Different approaches used in analytical assessment have been described in [57].. 2.1.2. Objective Tests. Objective tests are used in order to assess quality of voice using mathematical algorithms. As a result, objective tests do not require test subjects and can be executed quickly and automatically. Importantly, they are designed to present a high correlation to the results of subjective assessments. Objective methods are preferable over subjective tests, which are expensive and time consuming. There is no universal objective test that can be used to assess quality of voice in every scenario. Every objective method is defined for a range of applications and should be used only for those scenarios. Some objective tests are useful during network design when voice quality assessment is performed even though the transmission network has not been deployed yet. In this case, assessment helps in the design of networks appropriate for transmitting V oIP packets. Other objective tests are recommended to test new codecs or transmission system elements. Objective tests are also widely used to monitor networks, as they provide quick and up-to-date information about the network state. Signal-based methods Signal-based subjective methods of voice assessment use a specially prepared signal which is injected and transmitted through the system. At the end of the transmission the degraded signal is analysed by comparing it to its initial form, the reference signal. Some signal-based methods do not use a reference signal, but analyse signal degradations using Linear Predictive Coding (LP C) techniques [9]. Signal-based methods were initially designed in order to analyse a single characteristic of a system, e.g. codec or noise. They have since been improved, so now they can be used to assess the quality of an end-to-end system. The advantage of signal-based methods is the fact that the assessment is performed in the real system. However, signal-based methods usually need special equipment to be set up in order to generate and analyse traffic. Moreover, the methods do not test the conversational features of voice quality. The most commonly used.

(35) 13. 2.1 Voice Quality Assessment. signal-based methods include Perceptual Evaluation of Speech Quality (P ESQ) [8] and Perceptual Objective Listening Quality Assessment (P OLQA) [20]. Parameter-based methods In parameter-based methods the quality of voice is derived from the values of network and transmission parameters. As the voice quality is not calculated based on the actual degradations of a transmitted signal these methods are fit for the purpose of network design. Moreover, parameter-based methods can also be used for continuous network monitoring as they do not increase traffic and can be incorporated within existing network elements, e.g. switches and routers. ITU -T E-model [19] is a widely used parameter-based objective method of voice quality assessment. The result of the assessment is based on the values of the network and transmission parameters, e.g. packet loss or codec, as well as parameters of the end user side, e.g. microphone quality or noise. An end-to-end connection with degradation factors considered by the E-model is presented in Fig. 2.2.. ^ĞŶĚƐŝĚĞ. KǀĞƌĂůůůŽƵĚŶĞƐƐƌĂƚŝŶŐ͗K>Z ^ĞŶĚůŽƵĚŶĞƐƐƌĂƚŝŶŐ͗^>Z. ͲsĂůƵĞŽĨ ƚĞůĞƉŚŽŶĞ͗ Ɛ. ZĞĐĞŝǀĞƐŝĚĞ. ZĞĐĞŝǀĞůŽƵĚŶĞƐƐƌĂƚŝŶŐ͗Z>Z. tĞŝŐŚƚĞĚĞĐŚŽƉĂƚŚůŽƐƐ͗ tW>. ͲsĂůƵĞŽĨ ƚĞůĞƉŚŽŶĞ͗ ƌ. ZŽƵŶĚͲƚƌŝƉĚĞůĂǇ͗dƌ. ZŽŽŵŶŽŝƐĞ͗WƐ. dĂůŬĞƌĞĐŚŽ ůŽƵĚŶĞƐƐ ƌĂƚŝŶŐ͗d>Z. ŽĚŝŶŐͬĚĞĐŽĚŝŶŐ͗/Ğ͕Ɖů YƵĂŶƚŝǌĂƚŝŽŶĚŝƐƚŽƌƚŝŽŶ͗ƋĚƵ WĂĐŬĞƚůŽƐƐƌĂƚĞ͗WƉů ƵƌƐƚZĂƚŝŽ͗ƵƌƐƚZ ŝƌĐƵŝƚEŽŝƐĞ͗EĐ. DĞĂŶŽŶĞͲǁĂǇĚĞůĂǇ͗d ďƐŽůƵƚĞĚĞůĂǇ͗dĂ. ZŽŽŵŶŽŝƐĞ͗Wƌ. ^ŝĚĞƚŽŶĞŵĂƐŬŝŶŐ ƌĂƚŝŶŐ͗^dDZ. ĚǀĂŶƚĂŐĞĨĂĐƚŽƌ͗. Fig. 2.2: End-to-End connection referred by E-model [19]..

(36) 14. 2. Quality of VoIP. In principle, the E-model relies on the concept that different types of distortions such as delay or lossy encoding affect voice quality independently. As a result, separate values of different deteriorations can be combined. This approach is reflected in the main E-model formula: R = R0 − IS − Id − Ie-eff + A. (2.1). where: • R - transmission quality rating • R0 - basic Signal-to-Noise Ratio degradation group, which includes the impact of circuit and room noise • IS - degradations such as non-optimal sidetone or quantization distortion • Id - all range of delay degradations • Ie-eff - quality degradation caused by codec usage and packet loss • A - advantage factor which considers the increase of voice quality perception caused by challenging access methods, i.e. mobile networks or satellite transmission 5 4.5. Mean Opinion Score. 4 3.5 3 2.5 2 1.5 1 0. 20. 40. 60. 80. 100. R rating. Fig. 2.3: Mapping between E-model R rating and M OS values.

(37) 2.2 VoIP Quality Related Parameters. 15. Equation (2.1) shows that every degradation group has an independent impact on the transmission quality rating. For example, due to the specific value of transmission delay, the quality is degraded to the same extent, regardless of the presence of other degradations such as packet loss or sidetone. The E-model has been validated extensively [52] and can be applied as long as the distortion values are within certain limits, e.g. the E-model can be used for packet loss not higher than 20% only. However, the E-model should carefully be used to analyse combinations of specific degradations, e.g. low bit-rate codecs with other types of degradations, as these scenarios have not been verified extensively [19]. As shown by Eq. (2.1), the result of the E-model is the transmission quality rating (R). However, results on the R scale can be easily converted into the M OS scale. Fig. 2.3 depicts the mapping between the E-model R rating and M OS values. It shows that the highest value designed for the basic E-model is 4.5 on the M OS scale.. 2.2. VoIP Quality Related Parameters. When analysing the quality of V oIP applications it must be remembered that it is influenced by a wide spectrum of factors originating from different sources such as terminals, transmission path and the environment. Therefore, it is necessary to comprehend all the factors that are responsible for quality degradation in order to fully understand the quality of V oIP applications. The network and terminal factors that influence the quality of V oIP are presented in Fig. 2.4. Figure 2.4 indicates that listening and conversational quality are influenced by different factors. Listening quality is influenced by distortions only. The magnitude of distortions in V oIP depend on packet loss within the network as well as coding distortions and overflow of jitter buffer at the end-user terminal. Conversational quality is affected not only by the distortions but also by loudness, echo and end-to-end delay. Echo can be diminished by effective echo suppression and cancellation techniques, while delay can sometimes be reduced but never completely eliminated. All network and terminal quality parameters can be controlled to some extend by network and terminal design. At the terminal the quality of V oIP can be improved by selecting the appropriate codec, jitter buffer and packet size. In the network the parameters that have a direct impact on quality perception are IP packet loss and delay as well as link utilization and jitter. They are all discussed in more detail below..

(38) 16. 2. Quality of VoIP. >ŝƐƚĞŶŝŶŐƋƵĂůŝƚǇ. ŝƐƚŽƌƚŝŽŶ. :ŝƚƚĞƌďƵĨĨĞƌ ŽǀĞƌĨůŽǁ. ŽŶǀĞƌƐĂƚŝŽŶĂůƋƵĂůŝƚǇ. >ŽƵĚŶĞƐƐ. ĐŚŽ. ŽĚŝŶŐ ĚŝƐƚŽƌƚŝŽŶ. ĐŚŽ ĐĂŶĐĞůĂƚŝŽŶ. ĞůĂǇ. :ŝƚƚĞƌďƵĨĨĞƌ ĚĞůĂǇ. dĞƌŵŝŶĂůƋƵĂůŝƚǇƉĂƌĂŵĞƚĞƌƐ EĞƚǁŽƌŬ WĂĐŬĞƚůŽƐƐ. EĞƚǁŽƌŬ ĚĞůĂǇ EĞƚǁŽƌŬƋƵĂůŝƚǇƉĂƌĂŵĞƚĞƌƐ. ŽĚĞĐ. /WƉĂĐŬĞƚĚĞůĂǇ. :ŝƚƚĞƌďƵĨĨĞƌ. /WƉĂĐŬĞƚůŽƐƐ. WĂĐŬĞƚƐŝǌĞ. ĞůĂǇũŝƚƚĞƌ >ŝŶŬƵƚŝůŝǌĂƚŝŽŶ. dĞƌŵŝŶĂůĚĞƐŝŐŶ ƉĂƌĂŵĞƚĞƌƐ. EĞƚǁŽƌŬ ĚĞƐŝŐŶĂŶĚ ŵĂŶĂŐŵĞŶƚƉĂƌĂŵĞƚĞƌƐ. /WŶĞƚǁŽƌŬ /WƚĞůĞƉŚŽŶĞ ƚĞƌŵŝŶĂů. Fig. 2.4: Quality factors of IP telephony services [72] ..

(39) 2.2 VoIP Quality Related Parameters. 17. Codec In V oIP transmission the choice of the codec plays a crucial role as it influences the perceived quality of voice and the amount of consumed bandwidth, which is a scarce resource in digital networks. Coding of multimedia content makes it possible to remove redundant information and reduce its size while guaranteeing an acceptable quality of the content after decoding. It is also important that the encoded media are more resistant to quality degradation during transmission. Each codec can be characterized by the following characteristic: • transmission bit rate • complexity • delay • quality The transmission bit rate is an important characteristic of a codec, as the data is transmitted over IP networks which are concurrently used by other transmissions. Therefore, it is important that an efficient codec is used. A codec may be Constant Bit Rate (CBR), which means that media are coded with a fix bit rate, for example 64 kbps for a G.711 audio codec [4]. If the bit rate depends on the input media and varies in time, it is a Variable Bit Rate (V BR) codec. It codes more challenging parts of the media with a higher bit rate and simpler parts with a lower bit rate. If the bit rate varies in time depending on transmission conditions, it is an Adaptive Bit Rate (ABR) codec. Moreover, bandwidth utilization can be reduced using Voice Activity Detection (V AD). With V AD enabled, the bit rate generated during periods of silence is significantly reduced in order to save bandwidth. Complexity is also a crucial characteristic of a codec. High complexity codecs require more processor power and memory, making the user terminal more expensive. They also use more power and therefore generate higher usage costs and a lower battery life of mobile terminals. However, higher complexity frequently means a higher quality with a lower bit rate. Examples of high complexity audio codecs are G.729 [17] and iLBC [22]. The delay introduced by a codec is the time needed by the terminal to encode and decode the media stream. Therefore the delay is directly influenced by the codec complexity: more complex codecs generate a greater processing delay in the system. Quality is an extremely important characteristic of a codec as it is the major factor influencing end user satisfaction. The high quality of a codec is frequently paid for by a higher transmission bit rate or complexity. Generally, the highest quality is delivered by wideband codecs, e.g. G.722 [15]..

(40) 18. 2. Quality of VoIP. IP packet delay The total end-to-end delay observed in V oIP is actually a combination of three categories of delay: propagation, processing and queuing. When packet delay becomes a problem within a network, each category should be investigated separately in order to identify the root cause of the issue. Propagation delay is the time needed by the signal to travel within the medium. In wireless communication signal travels at the speed of 3 × 108 m/s, while in copper wire the speed is approximately 2 × 108 m/s. Due to the fact that propagation delay is a physical limitation of a medium, it is bound to the connection distance and cannot be reduced. During voice transmission, the signal is coded and packetized by the sending application. At the receiving application the reverse process takes place: deencapsulation retrieves the information from separate packets which is later decoded back into analogue information. This complete process is presented in Fig. 2.5.. WĂĐŬĞƚŶĞƚǁŽƌŬ sŽ/WƉŚŽŶĞ. ŶĂůŽŐƵĞ ƐŝŐŶĂů. sŽ/WƉŚŽŶĞ. ŝŐŝƚĂů ƐŝŐŶĂů. ŝŐŝƚĂů ƐŝŐŶĂů. sŽŝĐĞƉĂĐŬĞƚƐ. ŶĂůŽŐƵĞ ƐŝŐŶĂů ZĞĐŽŶƐƚƌƵĐƚŝŽŶ. &ŝůƚĞƌŝŶŐ. ĞĐŽĚŝŶŐ. ĞĐŽŵƉŽǌŝƚŝŽŶ. ĞͲĞŶĐĂƉƐƵůĂƚŝŽŶ. ŶĐĂƉƐƵůĂƚŝŽŶ. ŽŵƉƌĞƐƐŝŽŶ. ŶĐŽĚŝŶŐ. YƵĂŶƚŝǌĂƚŝŽŶ. ^ĂŵƉůŝŶŐ. Fig. 2.5: Model of voice transmission over packet networks..

(41) 19. 2.2 VoIP Quality Related Parameters. Each process marked in Fig. 2.5 needs time to be executed. The total time needed to execute all processes is known as the processing delay. The processing delay depends on the processing power of the endpoint and on the coding used for the transmission: the delay introduced by G.711 is 20 ms, by G.729 — 25 ms, and by iLBC — 30 ms. When a packet is transmitted through an IP network, it is forwarded by multiple intermediate devices. Each device needs to perform packet queuing and forwarding, which consumes time. Moreover, the queuing time can be longer in the event of link or node congestion. Time needed to queue and forward a packet through the complete transmission path is known as the queuing delay and depends on the number of intermediate devices within the transmission.. 100 90 80 70. R. 60 50 40 30 20 10 0. 0. 100. 200 300 One way delay [ms]. 400. 500. Fig. 2.6: Voice quality (E-model R rating) vs. one way delay [19].. Fig. 2.6 shows the relationship between voice quality and one way delay. It is based on the E-model [19] and therefore uses the R quality rating. It can be observed that for delays shorter than 150 ms, the reduction of quality is moderate, while when it exceeds 150 ms the perceived quality is significantly degraded..

(42) 20. 2. Quality of VoIP. Delay jitter At the sending end-point V oIP packets are sent with constant bit rate and interarrival time. However, due to IP network characteristics, the packets may be delivered with variable interarrival times and in an altered order. The magnitude of interarrival time variation is quantified as delay jitter. Multiple factors may cause jitter, e.g.: • Alteration of routing during transmission may affect the propagation delay as well as packet order due to the fact that some of the packets are routed over different paths. • Congestion of intermediate links and nodes may affect the queuing delay of each packet. • Variation of processing power available at the end point may influence the processing delay. Due to the fact that V oIP is a constant bit service, jitter may seriously degrade the perceived quality of V oIP [41]. Jitter should be compensated for, as it may introduce a high packet loss rate. Jitter buffer Jitter buffer is used at the packet receiving end-point in order to compensate for jitter and reorder packets. Jitter buffer stores packets received and forwards them to be played out at constant intervals, which eliminates jitter. However, storing packets by a jitter buffer introduces additional delays to the transmission. If the jitter buffer is too small, jitter buffer overflow may take place which leads to packets being discarded. On the other hand, too large jitter buffer increases delay which also leads to perceived quality degradation. Two types of jitter buffers are used: static and adaptive. In static jitter buffers, the buffer size stays invariant. On the other hand, the size of adaptive jitter buffer can be adjusted dynamically in order to compensate for variable network conditions [61], [63]. As a result, adaptive jitter buffer is used in order to find the optimal value of the jitter buffer, which trades off transmission delay against packet loss and therefore helps achieve a higher quality of V oIP applications. Packet size Voice signal is processed within the sending application by sampling, quantization, encoding, compression and encapsulation. Although encapsulation is the final process, it is just as important for the perceived quality of voice as other stages..

(43) 2.2 VoIP Quality Related Parameters. 21. Finding an optimum value of packet size is challenging, since packets that are too small or too large can have a negative impact on voice quality. V oIP applications use the Real-time Transport Protocol (RT P ) in order to send encoded voice. Each RT P packet includes IP , U DP and RT P headers, which in total make a 40-byte long header. Header size can be reduced significantly using compression techniques, e.g. Robust Header Compression (ROHC) [29]. However, this requires more processing power at end points and may increase the processing delay. Selecting the payload size is also important. Choosing a smaller payload size means a higher number of packets sent and therefore higher bandwidth use, a higher probability of congestion and a higher probability of packet loss. As a result, a large payload size may seem appealing to V oIP application designers. However, using a larger payload size means that packets need to be send with greater intervals which increases end-to-end packet delay. Moreover, losing a single large packet means a large amount of data is lost, which has a significant impact on perceived quality. Payload size cannot be freely selected by V oIP application designers because the sample size is defined by a codec: for G.711 [4] the codec sample size is 80 bytes, while for iLBC [22] it is either 38 or 50 bytes. Nevertheless, the sending application may adjust payload size by combining multiple samples into a single packet. Since packet size may significantly influence the perceived V oIP quality, it should be selected carefully in order to balance all the advantages and potential issues.. Link utilization Link utilization is a function of link bandwidth, the V oIP stream bit rate and the number of concurrent streams. In badly managed networks high link utilization leads to congestion and packet loss in V oIP streams. In order to handle high link utilization in a network, admission control and Quality of Service mechanisms are introduced. In V oIP services with admission control enabled, a limit of concurrent V oIP calls is defined. All calls exceeding the limit are rejected by the call control elements of the service. In the Cisco Unified Communications Manager admission control is applied using the concept of locations [18]. Quality of Service can be applied in a network using Differentiated Services (Dif f Serv) [25] or Integrated Services (IntServ) [30] mechanisms. In Dif f Serv trusted elements classify application traffic using a set of categories. Network elements provide priorities for every category. In the event of congestion, packets belonging to a low priority category are discarded. In IntServ every application.

(44) 22. 2. Quality of VoIP. that requires a QoS guarantee reserves resources in the network element along the transmission path. Neither congestion control nor Dif f Serv or IntServ can help provide the best quality in networks of insufficient bandwidth. In such a case, if more bandwidth efficient codecs cannot be used, upgrading links to higher bandwidth limits increases the capacity and quality of the V oIP service. Packet loss In V oIP compressed voice samples are encapsulated into IP packets and transmitted over the network. During the transmission packets may be lost due to congestion or transmission medium impairments. As a result, data transmitted in lost packets is not delivered to the receiver and the voice sample is not played out, which causes voice quality degradation. Packet loss may significantly degrade the perceived quality depending on the importance of the lost information. However, the degree of degradation depends not only on the packet loss rate (P pl) but also on other factors, including packet size, coding technique and packet loss distribution. Mobile networks are characterized by variable channel conditions due to terminal mobility and the effects of fast fading. Rapidly changing channel conditions make the packet loss rate highly variable in mobile networks. In order to protect vulnerable V oIP transmissions, in V oLT E an Adaptive Multi-Rate (AM R) codec is used. The AM R codec encodes voice with a variable bit rate depending on network conditions. When a high packet loss rate is seen, the AM R codec decreases the bit rate of the encoded signal and increases the bandwidth used specifically for error correction. The relationship between voice quality and packet loss rate is captured by the Ie−ef f parameter of the E-model: Ie−ef f = Ie + (95 − Ie ) ·. P pl . + Bpl. P pl BurstR. (2.2). Using this formula, the relationship between voice quality and packet loss rate for different codecs is presented in Fig. 2.7. Since the plot has been generated using the E-model, it presents the quality using the R transmission rating factor. Packet loss resistance of different codecs has been calculated using equipment impairment factor (Ie) and packet loss robustness factor (Bpl) values, as defined in [12]. Another factor which affects the impact of packet loss is packet loss pattern. For the same packet loss rate the perceived quality may be completely different depending on the packet loss pattern. If the number of audio packets lost sequentially is sufficiently low to not be noticed by the human cognitive system,.

(45) 23. 2.2 VoIP Quality Related Parameters. 100 G.711 G.711PLC G.723.1 G.729A. 90 80 70. R. 60 50 40 30 20 10 0. 0. 1. 2. 3. 4. 5 Ppl [%]. 6. 7. 8. 9. 10. Fig. 2.7: Voice quality (E-model R rating) vs. packet loss rate (P pl) [19].. or it can be concealed by the Packet Loss Concealment (P LC) technique [55], then the event has no impact on the perceived quality. In contrast, long sequences of lost packets can easily be perceived as an annoying quality deterioration. Packet loss patterns have been the subject of extensive research, including [26], [27], [28], [38] and [76]. Moreover, new methods of V oIP quality measurement in environments characterized by different packet loss patterns have been analysed in [45] and [56]. Packet loss patterns can be analysed and quantified in different ways. A common method is analysing the number of packets lost consecutively, e.g. as described in [34]. In this approach transmission is divided into two time periods. A burst is a period with a high number of lost or discarded packets, while a gap is a period with a low number of lost or discarded packets. In this analysis the packet loss burst is quantified by two parameters: burst density and burst length. Burst density describes the percentage of lost or discarded packets within the burst, while burst length describes the duration of the burst. An analysis of packet loss burstiness using this method has been presented in [11]. A different approach of quantifying packet loss patterns is Burst Ratio (BurstR), which quantifies how many packets are lost in bursts. Burst Ratio has been defined.

(46) 24. 2. Quality of VoIP. in [51] and described in [10] as the ratio of the measured average length of packet loss bursts to the expected length of the bursts in the event of ’random’ loss: BurstR =. average measured burst length expected burst length for random loss. (2.3). where the burst length is the number of consecutively lost packets. Burst Ratio is widely used as one of the input parameters of the ITU -T E-model [19]. The relationship between voice quality and Burst Ratio for different packet loss rates is presented in Fig. 2.8. It shows that the impact of packet loss patterns (measured by BurstR) depends heavily on the packet loss rate. For P pl = 1% the influence is barely noticeable, while for P pl = 5% the quality of voice is reduced drastically. It demonstrates that analysis of packet loss patterns plays a crucial role in delivering reliable V oIP applications. 100 Ppl = 1% Ppl = 2% Ppl = 5%. 90 80 70. R. 60 50 40 30 20 10 0. 1. 2. 3. 4. 5. 6. 7. 8. BurstR. Fig. 2.8: Voice quality (E-model R rating) vs. Burst Ratio [19]. The relationship is presented for three values of the packet loss rate P pl = 1%, 2%, 5%. In order to analyse specific packet loss patterns accurately, an appropriate packet loss model is required. An overview of the most important packet loss models is presented in Chapter 3..

(47) 2.3 Conclusions. 2.3. 25. Conclusions. This chapter presented the importance of research into V oIP technology, especially its QoS aspect. It introduced the concept of voice quality and presented an overview of different techniques used to assess it, focusing on the ITU -T E-model [19]. It is one of the most widely recognized and used analytical techniques for voice quality assessment. The E-model predicts voice quality based on network, codecs and terminal characteristics, helping network designers predict the quality of voice transmissions based on planned network performance before the network is build. It also enables operations teams to monitor the quality of V oIP transmissions in the network continuously and non-intrusively. The chapter also described V oIP quality related parameters including transmission delays, jitter and codecs. They all need to be taken into consideration when analysing the quality of V oIP applications. One of the parameters is packet loss, which has a significant impact on the perceived quality of transmitted voice. The impact of packet loss is related to loss characteristics, including rate and distribution of packet loss. Packet loss distribution, which describes packet loss patterns, can be quantified using Burst Ratio, one of the E-model parameters. Therefore, precise assessment of V oIP quality degradation due to pocket loss patterns requires in-depth analysis of Burst Ratio. The information presented in Chapter 2 is essential for the main part of this research: analysis of packet loss patterns using the BurstR parameter. Chapter 3 presents a new model for Burst Ratio analysis when the transmission path consists of multiple concatenated channels of known individual characteristics. Precise analysis of Burst Ratio in such environments makes it possible to assess voice quality more accurately, resulting in the delivery of higher quality V oIP services..

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(49) 3. Packet Loss Patterns in Concatenated Channels. Chapter 3 presents a model for analysing packet loss patterns in concatenated channels, based on the Burst Ratio concept. Originally, Burst Ratio was defined for single channel scenarios where end-to-end transmission is described with one set of characteristics. This research analyses Burst Ratio in scenarios where the transmission path consists of a series of channels, each described by an individual set of characteristics, including packet loss rate and Burst Ratio. The research is motivated by the fact that packetized transmission is always carried out over a combination of links and network nodes, including switches, routers and access points. Each link and node can be described by its own characteristics of packet loss. Being able to analyse packet loss patterns in a concatenated environment improves our understanding of the nature of packet loss and the influence of a single transmission channel on the end-to-end packet loss pattern. In this study each network node and link is regarded as a separate transmission channel. Therefore, an end-to-end transmission carried over links and nodes is analysed as a transmission over a series of concatenated channels. The analysis is performed using the two-state Markov chain packet loss model. The analysis of a concatenated transmission is preceded by a survey of the most widely used packet loss models.. 3.1. Packet Loss Models. Packet loss patterns are an important factor when considering QoS for V oIP applications. They are strongly dependent on the nature of packet loss. In wired networks packet loss usually occurs due to congestion in network nodes. When a router is not capable of forwarding all the received packets because of congestion, some of the packets are discarded. In turn, packet loss in wireless channels occurs.