Deliverable D 2.2 IP impairments models (revised version)

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Deliverable D 2.2 IP impairments models

(revised version)

Reviewed: YES Document history Revision Date Description 1 2 Apr. 2020 First issue 2 30 November 2020 New version with results 3 14 December 2020 Revised version 4 22 December 2020 Final version with integration of all corrections after review 5 23 March 2021 Revised version after reviewer comments 6 19 April 2021 Accepted version

Report contributors Name Beneficiary Short Details of contribution

Project acronym: EMULRADIO4RAIL Starting date: 01/12/2018 Duration (in months): 18 Call (part) identifier: H2020-S2R-OC-IP2-2018-03 Grant agreement no: 826152 Due date of deliverable: 30/11/2020 Actual submission date: 25/03/2021 Responsible/Authors: Alessandro Vizzarri – RADIOLABS(RDL), Laurent Clavier

(ULIlle) Dissemination level: PU Status: V6.0 accepted

Ref. Ares(2021)2610582 - 19/04/2021

http://www.inaf.it/it/sedi/sede-centrale-nuova/direzione-scientifica/relazioni-internazionali/nuovo-logo-horizon-2020/view

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Name Alessandro Vizzari, Franco Mazenga

RDL Contribution regarding Satellite aspects

Laurent Clavier, Sofiane Kharbech

ULille Contribution regarding WI-FI and LTE aspects

Marion Berbineau Univ Eiffel Contribution on objectives, introduction, general conclusion and global reviewing

Raul Torrego IKL Description of tests and reviewing José Soler, Ying Yan DTU Reviewers of the final version


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Table of Contents 1. Executive Summary .................................................................................................................................................. 6

2. Abbreviations and acronyms ................................................................................................................................ 7

3. Background ................................................................................................................................................................. 9

4. Objectives .................................................................................................................................................................. 10

5. Introduction .............................................................................................................................................................. 11

6. IP impairments known in the literature .......................................................................................................... 12

6.1. Definitions ....................................................................................................................................................... 12

6.2. Impairment Models at IP level in the literature ................................................................................. 13

6.2.1. TIA-921 ............................................................................................................................................................. 13

6.2.2. ITU G.1050 ....................................................................................................................................................... 14

7. IP Impairments in the Emulradio4Rail platform .......................................................................................... 17

7.1. IP impairments for the Satellite subsystem ........................................................................................ 18

Delay distribution ......................................................................................................................................... 23

Specific end-to-end delay model ........................................................................................................... 23

Packet loss ....................................................................................................................................................... 24

7.2. IP impairment obtained with Wi-Fi and LTE bearers ....................................................................... 24

Introduction .................................................................................................................................................... 24

Wi-Fi results .................................................................................................................................................... 25

Tests with LTE ................................................................................................................................................. 30

8. Conclusions and perspectives ............................................................................................................................ 38

9. References ................................................................................................................................................................. 39

10. Annex – complete set of statistical analysis. ........................................................................................... 43


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Table of Figures FIGURE 7.1: HIGH LEVEL VISION OF THE EMULRADIO4ERAIL PLATFORM ........................................................................................ 17 FIGURE 7.2: LOW LEVEL DESIGN OF THE EMULRADIO4RAIL PLATFORM ......................................................................................... 17 FIGURE 7.3: IP EMULATION ASSESSMENT WORKFLOW. .............................................................................................................. 18 TABLE 7.1: EXAMPLE OF DELAY BUDGET FOR A TSS TYPE BSM ................................................................................................... 19 FIGURE 7.4: GENERAL ARCHITECTURE OF A BROADBAND SATELLITE MULTIMEDIA (BSM) [13]. ........................................................ 19 FIGURE 7.5: THROUGHPUT, JITTER AND PACKET LOSS IN THE CASE OF FLAT FADING ......................................................................... 26 FIGURE 7.6: JITTER AS A FUNCTION OF THROUGHPUT IN THE CASE OF FLAT FADING .......................................................................... 27 FIGURE 7.7: PROBABILITY DENSITY FUNCTION AND CUMULATIVE DISTRIBUTION OF THE JITTER (DATA, GAMMA DISTRIBUTION, α-STABLE

DISTRIBUTION) .......................................................................................................................................................... 28 FIGURE 7.8: LINK BETWEEN JITTER AND THROUGHPUT. IN THAT CASE WE HAVE A=1/0.0633=15.8 AND B=1.42/0.0633=22.4. ......... 28 FIGURE 7.9: 𝑵𝑵 = 𝑻𝑻 + 𝒂𝒂. 𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝑱𝑱 − 𝒃𝒃 AND ITS PROBABILITY DENSITY FUNCTION (FROM THE DATA – CIRCLE, BLACK – AND THE FITTED

GAUSSIAN MODEL – RED). ........................................................................................................................................... 29 FIGURE 7.10: PER, JITTER AND IN DL TRANSMISSION AT 200 KM/H WITH 1 MBITS/S SENT IN THE CASE OF THE HILLY I (3 TAPS) ............. 31 FIGURE 7.11: JITTER AS A FUNCTION OF BANDWIDTH IN THE CASE OF HILLY I (3 TAPS) ..................................................................... 32 FIGURE 7.12: PROBABILITY DENSITY FUNCTION AND CUMULATIVE DISTRIBUTION OF THE JITTER (DATA, GAMMA DISTRIBUTION, α-STABLE

DISTRIBUTION) .......................................................................................................................................................... 33 FIGURE 7.13: LINK BETWEEN JITTER AND PER. IN THAT CASE WE HAVE A=0.45 AND B=-0.628. ....................................................... 33 FIGURE 7.14: 𝑵𝑵 = 𝑻𝑻 − 𝒂𝒂. 𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝑱𝑱 − 𝒃𝒃 AND ITS PROBABILITY DENSITY FUNCTION (FROM THE DATA – CIRCLE, BLACK – AND THE FITTED

GAUSSIAN MODEL – RED). ........................................................................................................................................... 34 FIGURE 7.15: THROUGHPUT, JITTER AND PER IN UL TRANSMISSION AT 200 KM/H WITH 1 MBITS/S SENT IN THE CASE OF THE HILLY I (3

TAPS) ...................................................................................................................................................................... 36 FIGURE 7.16: JITTER, PER AND THROUGHPUT REPRESENTED IN FUNCTIONS OF EACH OTHER. THE QUANTIFIED VALUES OF THROUGHPUT BUT

ALSO JITTER CAN CLEARLY BE OBSERVED .......................................................................................................................... 36 FIGURE 7.17: PROBABILITY DENSITY FUNCTION OF THE JITTER (DATA, LOG-NORMAL DISTRIBUTION) ................................................... 36


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Table of Tables TABLE 6.1: ITU-T G.1050 PROFILES LIST. .............................................................................................................................. 15 TABLE 6.2: ITU-T Y.1541 PROFILE A. ................................................................................................................................... 15 TABLE 6.3: ITU-T Y.1541 PROFILE B. ................................................................................................................................... 16 TABLE 6.4: ITU-T Y.1541 PROFILE C. ................................................................................................................................... 16 TABLE 7.1: EXAMPLE OF DELAY BUDGET FOR A TSS TYPE BSM ................................................................................................... 19 TABLE 7.2: TEST SUMMARY OVER THE WI-FI EMULATION PLATFORM ........................................................................................... 25 TABLE 7.3: RESULTS OF THE STATISTICAL ANALYSIS AND MODEL SELECTION OF THE JITTER FOR SLOW FADING CASE ................................ 27 TABLE 7.4: PARAMETERS FOR THE DIFFERENT MODELS OF THE WI-FI KPIS (JITTER AND THROUGHPUT) ............................................... 30 TABLE 7.5: TEST SUMMARY OVER THE LTE EMULATION PLATFORM .............................................................................................. 31 TABLE 7.6: RESULTS OF THE STATISTICAL ANALYSIS AND MODEL SELECTION OF THE JITTER. ................................................................ 32 TABLE 7.7: PARAMETERS FOR THE DIFFERENT MODELS OF THE LTE KPIS ....................................................................................... 37


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1. Executive Summary The European EMULRADIO4RAIL Project aims to develop an innovative emulation platform for tests and validation of various radio access technologies (RATs) like Wi-Fi, LTE, LTE-A and Sat-Coms. The project considers the integration of both channel emulators and network emulators into a single emulation platform so that the proposed testbed can offer a complete (from physical layer to IP level) test environment. This deliverable is organized as follows. First a state of the art of IP impairment models considered in the literature is given. Second, we provide an original possible modelling approach of IP metrics (jitter, PER, bandwidth, E2E delay) versus time, in a set of Railway environments based on the experimental assessments obtained in Task 3.2 [D3.2] and in new long term measurements carried out in order to have more statistically representative data. The results were obtained with the different emulation solutions detailed in [D2.1] and [D3.1]. The aim of the modelling task is to be able to use the experimental models at IP level, to test the industrial prototypes from X2RAIL-3 WP3. In the last part, the models considered for the emulation of the satellite link at IP level are presented. Finally, conclusions and perspectives are given.


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2. Abbreviations and acronyms

Abbreviation / Acronyms Description ACS Adaptable Communication System ADC Analog- to- Digital Converter AP Access Point AWGN Additive White Gaussian Noise BB Base Band CCDF Complementary Cumulative Distribution Function CDF Cumulative Distribution Function CPU Central Processing Unit DAC Digital to Analog Converter DL Downlink DUT Device Under Test EPC Evolved Packet Core ESG Signal Generator FDD Frequency Duplex Division FPGA Field-Programmable Gate Array GCG Ground Communications Group GSM Global System for Mobile communications GSM-R GSM - railway GUI Graphical User Interface I/O Input / Output port IF Intermediate Frequency IP Internet Protocol KPI Key Performance Indicator KVM Kernel-based Virtual Machine LTE Long Term Evolution LTE-A Long Term Evolution Advanced LXC Linux Containers MIMO Multiple Input Multiple Output MPLS Multiprotocol Label Switching MXG Mid-range, eXtra Good signal generator NICS Network Interface Cards OAI Open Air Interface OPNET OPtimized Network Engineering Tool PC Personal Computer PL Programmable Logic PLMN Public Line Mobile Network PS Processing System PSG Performance Signal Generator PXA Extra performance Signal analyser PXB Base Band Generator QoS Quality of Service


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RF Radio Frequency Sat-Coms Satellite Communications SDR Software- Defined Radio SIM Subscriber Identity Module SITL System In The Loop SMA SubMiniature version A STD. DEV. Standard Deviation SW Software TBF Token Bucket Flow TDL Tap Delay Line TM Transmission Mode UL Uplink USB Universal Serial Bus USRP Universal Software Radio Peripheral VM Virtual Machine Wi-Fi Wireless Fidelity WLAN Wireless Local Area Network WP Work Package


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3. Background The present document constitutes the Deliverable D2.2 “IP impairments models” according to Shift2Rail Joint Undertaking programme of the project titled “EMULATION OF RADIO ACCESS TECHNOLOGIES FOR RAILWAY COMMUNICATIONS” (Project Acronym: EMULRADIO4RAIL, under Grant Agreement No 826152). In December 2018, the European Commission awarded a grant to the EMULRADIO4RAIL consortium of the Shift2Rail / Horizon 2020 call (H2020-S2RJU-OC-2018 S2R-CFM-IP2-01-2015). EMULRADIO4RAIL is a project connected to the development of a new Communication System planned within the Technical Demonstrator TD2.1 of the 2nd Innovation Programme (IP2) of Shift2Rail JU: Advanced Traffic Management & Control Systems. The IP2 “Advanced Traffic Management & Control Systems” is one of the five asset-specific Innovation Programmes (IPs), covering all the different structural (technical) and functional (process) sub-systems related to control, command and communication of railway systems.


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4. Objectives The objective of this Deliverable is to present several Models of IP impairments to be used in a network emulator to mimic the impact of the air gap, over traffic at IP level.

The satellite emulator is IP native, this is not the case of the Emulradio4Rail platforms for the terrestrial bearers (LTE and Wi-Fi). Consequently, two different approaches have been considered:

• In order to study, develop and set up various models of “IP impairments” versus various railways scenarios, we have performed modelling activities in Task 2.2 based on experimental results obtained in Task 3.2 and in new long-term measurements. In this task, the Emulradio4Rail RF channel emulators have allowed obtaining, in a controlled environment (Railway scenarios), experimental data concerning the assessment of the LTE and Wi-Fi bearers. Thanks to the IPERF tool, we have obtained for different IP traffic, the variations of IP metrics such as jitter, packet error rate, effective throughput and end-to end delay versus time. Thanks to long emulation testing (30 min) in a selected set of Railway channels, we have obtained statistical characterisation of the IP metrics, which we have termed “IP impairment models”. These final models for the transmission impairments at IP level can be then considered with the industrial prototypes. Unfortunately, due to covid-19, this was not possible at the time of finalization of this Deliverable for the LTE and Wi-Fi bearers.

• Regarding the satcom link, due to difficulties in physically emulating the satellite radio link in Task 3.2 of WP3, the corresponding modelling of IP impairments are based on a theoretical/simulated approach, over realistic propagation scenarios.

Important remark: considering existing statistical Railway channel models in the literature, for the terrestrial link, it is not possible to link the models to the train position. It is though possible to decide artificially, with a predefined designed trajectory, that the train is during a given interval in a cutting environment, then in a tunnel, then on a viaduct, then in a station, etc.


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5. Introduction The QoS measurement activities are strictly related to the IP impairments. The possibility to inject disturbs at IP level (such as additional delay, packet loss or jitter) offers the possibility to test in laboratory, network models under realistic conditions. For this reason, it is very important to understand the IP impairments, their effects on end-to-end transmission and how the network and the network elements react to them. In order to do so, we need to generate these IP impairments (or the conditions that lead to them) in such a way that allows us to assess their impact on the performance of transmissions The Deliverable is organised as follows. First the IP impairment models considered for the satellite emulator are presented. They are extracted directly from literature and standards for the emulation at IP level. The second section of the Deliverable presents the methodology to derive statistical models of IP impairment from the measurements performed, within Task 3.2, with the Radio channel emulators (Wi-Fi and LTE) and the results obtained. Finally conclusions and perspectives are provided.


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6. IP impairments known in the literature

6.1. Definitions Recommendation ITU-T Y.1542 [1] describes a general Framework for achieving end-to-end IP performance objectives. In particular, the ITU document defines the main approach and methodology to adopt in order to guarantee acceptable levels of IP network performance. To do that, the ITU recommendation provides some use cases and related impairments can occur during the end-to-end transmission chain. This Recommendation focuses on the impact of impairments on the performance at Internet Protocol (IP) layer 3. IP streams, from any type of network device, can be evaluated using this model. The following are parameters and impairments that affect quality of service (QoS) and IP network performance:

• Network architecture; • Types of access links; • QoS-controlled edge routing; • Maximum Transmitting Unit (MTU) size; • Network faults; • Link failure; • Route flapping; • Reordered packets; • Packet loss (frame loss); • One-way delay (latency); • Variable delays (Jitter); and • Background traffic (i.e network load).

The performance of a network is of vital importance to both the service providers and the customers. Performance can be measured with parameters such as transmission rate, delay, jitter and packet loss. Currently, there is no standard for QoS performance measurement, hence various methods are used:

- actively by insertion of test traffic. - passively by observing user-generated traffic.


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6.2. Impairment Models at IP level in the literature

6.2.1. TIA-921 The Telecommunications Industry Association (TIA) is the leading trade association for the information and communications technology (ICT) industry. TIA represents the communications sector of the Electronic Industries Alliance (EIA). http://www.tiaonline.org . TIA-921 “Network Model for Evaluating Multimedia Transmission Performance Over the Internet Protocol” was created by TIA TR-30.3 Subcommittee on Data Communications Equipment Evaluation and Network Interfaces and released June 2006 [2]. This standard specifies an IP network model and scenarios for evaluating and comparing communications equipment, connected over a converged wide-area network. The IP network model consists of different impairment combinations that are scenario based and time varying. IP streams from any type of network device can be evaluated using this model. The test scenarios combine LAN, access and core network elements in a realistic way, to create Layer 3 IP network impairments that cause packets to experience varying delay or loss. These scenarios are based on actual network data, provided by anonymous IP service providers and IP network equipment manufacturers. This Standard is broadly applicable to the evaluation of any equipment that terminates or routes traffic using the Internet Protocol. This Standard can also be used to evaluate media streams or other protocols carried over IP networks. Examples of the types of equipment that can be evaluated using this model include: IP-connected endpoints: IP network devices (such as: user agents, call agents, media servers, media gateways,

application servers, routers, switches, etc.); IP video (IPTV, video conferencing, telepresence, etc.); IP phones (including soft phones); IAF (Internet-aware fax)

IP/TCP connected endpoints: Peer-to-peer HTTP Adaptive bit-rate video

PSTN-connected devices through IP gateways: POTS through voice-over-IP (VoIP) gateways; ITU-T T.38 facsimile devices and gateways; ITU-T V.150.1 and ITU-T V.152 (voiceband data, VBD) modem-over-IP gateways; TIA-1001 and ITU-T V.151 textphone-over-IP gateways.


http://www.tiaonline.org/

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The IP network model can be used to test an IP stream in real time using an emulation of the network impact, based on the model. The IP network model can be used to study and to understand:

the interaction of different traffic mixes; the effects of QoS and queuing on different types of traffic; packet delay variation and packet loss.

6.2.2. ITU G.1050 ITU G.1050 Recommendations specify an IP network model, based on scenario-based impairment combinations. These time varying IP network impairments provide a significant sample of transmission conditions [3]. Emphasis is given to the fact that manufacturers of communications equipment and service providers are interested in a specification that accurately models the IP network characteristics that determine performance. Evaluators desire a definitive set of simple tests that properly measure the performance of communications devices from various manufacturers. Therefore, the objective of this Recommendation is to define a technology-independent model that is representative of the IP network, that can be simulated at reasonable complexity, and that facilitates practical evaluation times. The following table 6.1 describes service test profiles and the applications, node mechanisms and network techniques associated with them. This categorization is based on ITU-T Y.1541 [4], although a one-to-one mapping to these service profiles may not be possible.


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Table 6.1: ITU-T G.1050 profiles list.

The following three test profiles are used in a generic IP network model that can be associated with service level agreements (SLAs):

• Well-managed network (profile A) – A network with no over-committed links that employs

QoS edge routing. • Partially-managed network (profile B) – A network that minimizes over-committed links and

has one or more links without QoS edge routing. • Unmanaged network (profile C) – An unmanaged network such as the Internet that includes

over-committed links and has one or more links without QoS edge routing.

Tables 6.2 to 6.4 represent end-to-end impairment levels. ITU-T Y.1541 Profile A Well-Managed Network Impairment Ranges Y.1541 QoS Level – suitable for high quality video and VoIP Most Service Providers will be supporting this SLA for IPTV

Table 6.2: ITU-T Y.1541 profile A.

ITU-T Y.1541 Profile B Partially-Managed Network Impairment Ranges Y.1541 QoS Level – suitable for Lower quality video and VoIP


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Table 6.3: ITU-T Y.1541 profile B.

ITU-T Y.1541 Profile C Un-Managed Network Impairment Ranges Y.1541 QoS Level – Internet little or no guarantees

Table 6.4: ITU-T Y.1541 profile C.

These models are the reference point in general for industrial partners during the test. It depends on the IP traffic. At the moment we are not aware of the IP traffic type. Thus, we need to consider all the three profiles.


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7. IP Impairments in the Emulradio4Rail platform The Emulradio4Rail platform is composed of two LTE platforms built with OAI, 1 Wi-Fi platform and 1 satellite platform [D3.1]. The LTE and Wi-Fi platforms are connected at RF level to a channel emulator, able to mimic the statistical radio behaviour in different railways environments selected in [MS3]. The satellite platform is IP native and connected directly at IP level. The high level concept of the Emulradio4Rail platform is given in Figure 7.1.

Figure 7.1: High level vision of the Emulradio4ERail platform

The low level design extracted from [D3.1] is given on Figure 7.2.

Figure 7.2: Low level design of the Emulradio4Rail platform


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The methodology followed is recalled in the diagram given in Figure 7.3.

Figure 7.3: IP emulation assessment workflow.

We will first detail the satellite part and then the terrestrial one.

7.1. IP impairments for the Satellite subsystem Satellite Communication has been recognized a suitable telecom bearer of the Adaptable Communication System (ACS) defined by X2Rail team, including the possibility of its integration with 3GPP architectures under investigation (especially for 5G networks). The satellite bearer is considered in the FRMCS architecture that identifies two classes of telecom bearer, respectively classified as 3GPP family and NO-3GPP as actual satcom systems, Wifi, and so on. Even if satcom solutions are evolving and might be in the future compatible with 3GPP standard, they are already used [43] and nowadays are also compatible with the ACS architecture in combination with other bearers. The Emulradio4Rail Satellite Emulator implemented by Radiolabs allows to assess the overall performance of satcom independently from the specific air interface implemented by individual satcom operators. The Emulradio4Rail satellite subsystem is IP-native. The Netem emulator [5] is used for emulating the satellite link at IP level. To have more realistic features and according to ITU Recommendations G.1050, we also implemented the IP Impairment models as Additional delay and Packet loss (%) through statistical distributions [6] [7] [8] [9]. From a delay point of view, according to [6] there are three main components of latency: propagation delay, transmission delay, and queuing delay. In case of GEO SatCom, the main component is the propagation delay. In literature, an end-to-end propagation delay around 250 ms [7] is experienced in case of one-way GEO satellite transmission, although the value can increase


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up to 270 ms if interleaves for Forward Error Correction (FEC) are used (around 8-10%) [6]. If we consider the two-way GEO satellite transmission, “The propagation delay for a message and the corresponding reply defined as one round-trip time or RTT will be at least 500 ms to 600 ms.” [7]. According to ETSI document [7] a typical delay budget in case of GEO satellite transmission is summarized in Table 7.1.

Table 7.1: Example of delay budget for a TSS type BSM

As described in D3.2, the typical end-to-end delay of the GEO satellites is around 500 ms up to 600 ms [6] [7] [16]. The variations of end-to-end delay at physical layer are usually lower than 500 ms. They depend on the physical propagation in the medium. The satellite behavior can be modelled through a “two-state model”: ON and OFF mode. In case a good coverage (satellite in sight and in “ON mode”) the satellite signal at physical layer (in the transmission section including the two earth stations) is quite stable, together with the capacity offered to the end user and the corresponding performance at IP level. If the satellite is obstructed (satellite not in sight and in “OFF mode”) the end user cannot receive any satellite radio signal, then he has not a capacity from satellite [17]. From the impairment point of view, the most important ones depend on the data transmission over IP in the section (a LAN or WAN link) including the satellite ground station and the rail application server. Since the WAN link passes through Internet (as shown in Figure 7.4 [13]), the transmission at IP level can be affected by several disturbs, depending on the specific conditions, such as network traffic conditions and network congestion.

Figure 7.4: General architecture of a Broadband Satellite Multimedia (BSM) [13].

The internet connection can suffer from strong fluctuations and causes several aleatory disturbs to


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the transmission, such as delays, packet loss and jitter of IP packets. This last delay is not measurable in any way because it depends on the IP network that is crossed, and it is independent from the satellite link. For this reason, in scientific literature of communication networks the recognized method is to apply statistical distributions to the IP traffic through different statistical models. The Emulradio4Rail satellite emulator can implement the following statistical models at IP level: normal, uniform, Pareto and Pareto generalized for delays. The packet loss can be also set through a percentage value. Moreover, it can apply the statistical models of delays and the percentage values of packet loss in all the three transmission segments: terminal-satellite, satellite-gateway and gateway-server. The statistical distributions describe the trend of delays (often very onerous) and critical fluctuations around the average value. These models can evaluate the behaviour of the network even in extreme conditions and also well outside of realistic scenarios, in order to consider "worst case" scenarios, sometimes unattainable in practice. According to [34], the network traffic can be studied through two different approaches: empirical studies and statistical studies. The first ones are based on experimental trials carried out in a real context and with real equipment. The obtained results are useful for the enhancement of network protocol but cannot be generalized to model the Internet behaviour. In [35] the authors propose an empirical model for wide-area network. Around three million of TCP connections was collected and analysed in 15 wide area environments. However, the results showed the traffic in wide-area traffic cannot be expressed with a statistical model. The statistical studies consider the statistical model of Internet traffic. The first statistical models were based on Poisson distribution, since it assumed that the packets reach the destination with a Poisson distribution. With the advent of World Wide Web, the packets can arrive to the destination not with a Poisson process, although TCP connections can be modelled with a self-similar behaviour. In the following some scientific works on IP traffic models and impairments are described. In [14] some congestion control algorithms of TCP are analysed in order to improve the performance. The satellite links are also simulated, being characterized by delays around the 250ms [15]. In this situation, we need a long time to recover the best size of the TCP window. Thanks to a discrete event-based network simulator (such as NS2), the author has the flexibility to choose the percentage of the packets to be delayed and the distributions of the delays in a random way. The author considers a Web-like Traffic, characterised by a short-term flow with N packets sent after T seconds from the start. N is modelled with a uniform distribution (between 10 and 20) and the T period with a Pareto distribution. The following figure shows the results obtained using an IP traffic modelled with statistical distributions of delays. The fluctuations of throughput (Mbps) around the mean value are shown. The need of packet reordering is simulated by a random choose of packets through a uniform distribution and a subsequent delay of them. The packet delay is also based on a normal distribution with mean 25 ms and variance 8 ms. In [18] the authors realized a link emulation platform for aeronautical application. The adopted methodology is based on passive measurements techniques in order to validate the traffic generation process. The traffic generator is successively configured according to different traffic


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profiles through a probability distribution for the packet size and packet inter-arrival time. A final fitting procedure is applied to validate the probability distribution for each parameter. The delay variation of geostationary satellites is performed through a normal distribution around the mean value of 250 ms. The authors consider a Pareto-Normal distribution (with a mean value of 200 ms and standard deviation of 200 ms) to model the inter-packet gaps. In [19] the authors site the main features of Netem for traffic emulation. It can emulate variable delay, loss, duplication and re-ordering. Due to the non-uniform behaviour of the network, the distribution of delay follows statistical models as Normal, Pareto, etc.. In [20] the authors perform QoS network measurements using a real video traffic passing through NetEm emulator which delay and jitter. In [21] the authors focus on the end-to-end QoS of IP over terrestrial and Satellite Network (NGSN) using FTP protocol. In [22] the main features of Netem are described together with the statistical distribution for impairment of IP traffic. In [23] the authors focus on the need of an accurate and realistic description and model of a network. There are many difficulties due to the different factors, parameters and scale of Internet. In order to emulate a realistic traffic over a communication network, the authors compare several network emulators, included Netem emulator. Regarding the generating model for IP traffic, packets are generated and treated with a distribution model. Among the configurable parameters, the network emulator proposed by the authors provides the data flows according to different random statistical distributions used for model the size of the packets., such as Constant, Uniform, Exponential, Pareto. In [24] the authors provide a review of the network emulators known in literature, based on Linux Operating System. It can impair the data flows and can apply some queuing disciplines. In [25] the author presents the main approach for accelerating the satellite connection in a WAN environment, together with satellite simulation for testing procedures to run on virtual machines. Netem network emulator is used. In [26] the author provides a full overview and explanation of Traffic Analysis methodologies and traffic models for analysing the requirements of a communication network at IP level. The traffic analysis enables a collection of data such as the average load or the bandwidth requirements for the specific applications, trough measurable metrics called key Performance Indicator (KPI). The traffic models provide important information for network planning and dimensioning to guarantee the required quality and to predict the network performance. An IP traffic ca be modelled as single entities arriving from a source to destination. Its mathematical representation is Point Process: it is formed by subsequent time intervals (T0, T1, T2,.., Tn). Point processes can be modelled as a Counting Process or an Inter-Arrival Time (IAT) Process. The first model is a stochastic process with a continuous time and non negative values, while the second one is process characterized by a non-negative sequenced values representing the length of the time interval between two entities [27]. In [27] the Poisson Distribution Model is described, in terms of probability distribution function and density function. The Pareto Distribution Process, the Weibull Distribution Process, the ON-OFF model, the Interrupted Poisson Process (IPP) and the Markov Modulated Poisson Process (MMPP) are described in [28]. In [29] the authors describe the methodologies and results obtained from fitting the data collected on a real network and validated with different traffic models, such as uniform, normal, lognormal, Pareto, Weibull and gamma. The uniform and lognormal gives a good balance of length and size


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of collected data. In [30] the authors propose a new model for predict the connection bandwidth, based on statistical parameters of the collected data and available KPIs. They considered a Gaussian distribution (or normal distribution) as traffic model. In [31] and [32] the implementation of Gaussian distribution is also considered to model the Internet traffic. In [33] the authors propose an IP traffic model for the analysis of IP-based Internet traffic. In [36] the author remarks the necessity to analyse the performance of satellite connections. They are crucial in zones where Internet is not available. In this sense, the satellite offers to the end user the connectivity to Internet. However, congestion and long round-trip times (RTT) issues can be experienced with the satellite connections. The simulation methodology is presented, together with the simulation tool: Iperf as packet generator and Netem as network emulator. Thanks to the cooperation with Pacific ISP, the author models the data flow through the following statistics: (i) median size: under 500 bytes, (ii) a mean size: around 50 kB, and (iii) a maximum: around 1 GB. Not only the scientific community has been interested to traffic analysis and network performance, including the IP impairments disturbing the IP communications, but also the industrial communications. In fact, in the last years they introduced in the market several commercial emulator equipment. Most of them implement the IP impairment generation according to the TIA-921 and ITU G.1050, in order to apply the IP impairment values included in the range described by the standard. The statistical distributions are also available for random IP impairments. In [37] the manufacturer describes the main feature of IP emulator equipment. It is compliant with the values indicated by TIA-921 and ITU G.1050 standard. The values of delay can be set as Gaussian distribution or in terms of specific mean value and standard deviation settings. In [38] the equipment vendor cites the main IP impairments affecting the communication networks, such as: (i) frame loss and packet Loss (caused by noise, dirty fiber or connector, congestion,), (ii) static delay (caused by propagation due to distance, congestion, queuing or processing), and (ii) delay variation (caused by packet Jitter, congestion, queuing or processing). Random impairments can be generated through the involvement of VLAN, MPLS, MAC address and, IP address. Aleatory impairments are implemented using several statistical distributions, as periodic, Poisson, Gaussian and uniform. In [39] the author gives an overview of how a network can be emulated with network emulators based on Linux Operating System. The “tc” (Traffic control), already present in Netem open source tool, offers many features for network emulator, including the IP impairment and disturbs. A quick tutorial on tc tool and the most important line commands are presented. The different distribution models for delay variation are also described, such as normal, pareto, pareto-normal. In [40] the vendor describes the technical solutions for testing and emulating the satellite connections and applications, while in [41] the network impairment emulator is presented in order to simulate the real conditions of an IP network. It means that network latency, network delay variation (jitter), bandwidth, and packet loss values can be added to the transmission. In [42] the vendor gives the possibility to customize the traffic load with different statistical distributions, such as Uniform, Ramp, Sawtooth, Fixed, Normal, Step, and Step-Sawtooth. Given the IP traffic injected into the Emulradio Satellite Emulator, it can disturb the traffic modifying the behaviour of IP packets, in terms of delay statistical distributions and packet loss percentage.


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Both statistical models for delay variation and packet loss percentage can be set for each satellite transmission segment: (i) terminal-to-satellite, (ii) satellite-gateway, and (iii) gateway-server. The following section summarizes the statistical distributions for delay variations implemented within the Emulradio Satellite Emulator. The Packet Loss can be set as percentage value.

Delay distribution The distribution models for the implemented IP impairments are as follows. We considered the classical following distributions. Normal Distribution

𝑓𝑓(𝑥𝑥) = 1𝜎𝜎 √2𝜋𝜋

𝑒𝑒−12𝑥𝑥− µ𝜎𝜎

2

(7.1)

where µ is the mean or expectation of the distribution (and also its median and mode), σ is its standard deviation, 𝜎𝜎2 is the variance of the distribution Pareto distribution

𝑓𝑓(𝑥𝑥) = 1 − 𝑘𝑘𝑥𝑥𝛼𝛼

(7.2)

were x is the random variable, k is the lower bound of the data, α is the shape parameter Pareto generalized

𝑓𝑓(𝑥𝑥|𝛼𝛼, 𝑘𝑘) = 𝛼𝛼𝑘𝑘𝛼𝛼

𝑥𝑥𝛼𝛼+1 (7.3)

k≤ x< ∞; α,k >0 Uniform distribution

𝑓𝑓(𝑥𝑥) = 1

(𝑏𝑏−𝑎𝑎) 𝑥𝑥 ∈ [𝑎𝑎, 𝑏𝑏]

0 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 (7.4)

Specific end-to-end delay model In the Emulradio4Rail satellite subsystem it is also possible to put a specific additional model as IP impairment according to particular experimental data campaign. For example, RDL team defined a specific end-to-end (E2E) delay model of a satellite communication system for railway environment. In particular, in [10] the authors from RDL team propose a two states model allowing for reproducing the main behavioural characteristics of the end-to-end delay, as observed


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experimentally during the 3inSAT research project. In fact, the experimental activities have been developed in the framework of the ESA ARTES 20 3InSat Project [11]. The project focused on the realization of a railway testbed for testing satellite navigation and communication technologies for rail applications, under real operational conditions. Data was recorded over the railway line connecting the towns of Cagliari and Olbia in the Sardinia Island (Italy). The considered railway line is about 300 km long and the maximum allowed train speed was 150 km/h, actually limited to 130 km/h (which is the maximum speed of the Minuetto Diesel traction trains used on the line). The E2E delay model proposed in [10] correlates the receive time Ri of the generic i-th message with the corresponding transmit time Ti:

𝑅𝑅𝑖𝑖 = 𝑇𝑇𝑖𝑖 + 𝜏𝜏𝑖𝑖 , 𝑒𝑒 = −∞, … , +∞ (7.5) where 𝜏𝜏𝑖𝑖 is the E2E delay. The proposed model allows for generating the stochastic process 𝜏𝜏𝑖𝑖 modelling the occurrence of the event of packet receiving.

Packet loss From a packet loss point of view, the Emulradio4rail satellite subsystem is able to provide a specific packet loss percentage for a few seconds and change it for the rest of the emulation session [D3.2]. The choice can be also made randomly, a random packet loss generation is possible as IP impairment. It depends on the IP transmission considered.

7.2. IP impairment obtained with Wi-Fi and LTE bearers

Introduction As indicated in Figure 7.3, in Task 3.2 we have performed experimental assessment with Wi-Fi and LTE bearers, in some specific scenarios during long period, compared to the results reported in D3.2. These results are processed to derive statistical models for the different metrics measured: Packet Error Rate (PER), jitter and effective transmission rate. As explained in [D3.2], effective transmission rate, jitter and packet loss metrics are measured with the Wi-Fi and LTE emulation systems with UDP traffic. Several radio channel models have been considered. We propose here a methodology based on the results obtained with a set of channel models. In the following, we use the channel models proposed in D1.3, to emulate the railway environment. We also include a Wi-Fi interferer following the TYPE I interference addition proposed in D1.2 [D1.2]. The aim in D2.2 is not to present exhaustive results but to present the methodology followed. The obtained results constitute a proof of concept, regarding the possibility to obtain IP impairments models as proposed initially to be able to translate, at IP level, the influence of Radio channel models. Due to Covid-19 we had no possibility to test with industrial prototypes the effects of these obtained IP impairment models. A full description of the obtained results is given in annex. Only one example is given in this section to highlight the methodology and results are summarized in


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Table 7.4 and Table 7.7. We draw the attention on two points:

• It is important to consider rare events, which will impact the choice of the distribution used for modelling. For instance, we consider the Jitter and it is important to take into account that large values can happen, but only rarely. They threaten a targeted quality of service. Heavy tailed distributions will prove to be better models.

• It is also important to consider joint modelling: parameters are dependent and good models have to account for that.

A future step in the modelling consideration will be to take into account the time evolution of the KPIs. Two packets transmitted closely in time will face channel conditions that can be more or less correlated depending on the time between the transmissions, the channel characteristics and the train speed.

Wi-Fi results In the following we propose a methodology to derive models from the experimental results obtained with the Emulradio4Rail platforms. The methodology is quite general and can be easily extended to more complex models if necessary. However, the proposed models (statistical and linear relationship as will be explained) result in a good fit in almost all the encountered cases. Fourteen different tests have been carried out in the Wi-Fi emulation platforms described in [D2.1] and [D3.1], where the IPERF tool has been used in order to obtain the target KPIs (effective throughput jitter and PER). Different IPERF runs have been carried out, in which different channel models (extracted from [D1.3]) or channel setups have been used. A test with a Wi-Fi interferer is also presented. The following table 7.2 sums up the characteristics of each test.

Tests number Test characteristics T0 Reference test. Flat channel with 50 dB attenuation. 500 data points T1 Hilly I channel model with no Doppler. 500 data points per test T2 Hilly I channel model with 10 km/h speed (22 Hz Doppler) 500 data points T3 – Hilly I Hilly I channel model with 100 km/h speed (220 Hz Doppler) 500 data points T3bis Repetition of the previous test with new Doppler seed. 500 data points T4 Rural II channel model with no Doppler. 500 data points T5 Rural II channel model with 100 km/h speed (220 Hz Doppler) 500 data points T6 Viaduct I channel model with no Doppler. 500 data points T7 Viaduct I channel model with 100 km/h speed (220 Hz Doppler) 500 data points T7 (1800) Repetition of the previous test with new seed. 1800 data points (30 minutes test). T8 Cutting II channel model with no Doppler. 500 data points T9 Cutting II channel model with 100 km/h speed (220 Hz Doppler) 500 data points Cutting with interference

Cutting II channel model with 50 km/h speed and an additional interferer on the receiver side (fixed channel). 3600 data points (30 minutes)

Table 7.2: Test summary over the Wi-Fi emulation platform


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Figure 7.5 presents the evolution overtime of the throughput the jitter and the packet loss obtained in the case of flat channel without Doppler (Test T0). This case would correspond to a train that is stopped or moving slowly when coming close to a train station.

Figure 7.5: Throughput, jitter and packet loss in the case of flat fading

In the following we are going to model the jitter and the effective throughput jointly. We notice that the packet loss is constant and null. This can be expected in this slowly varying channel. One thing that is important to notice also is that the jitter and the effective throughput are correlated random variables as shown in Figure 7.6.

50 100 150 200 250 300 350 400 450 500

Time

10

15

20

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

5

10

Jitte

r

0 50 100 150 200 250 300 350 400 450 500

Time

-1

0

1

Pack

et L

oss


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Figure 7.6: Jitter as a function of throughput in the case of flat fading

To create the model we will use two steps:

• First, we model the Jitter. We proceed to a model selection. We analysed the acceptability of several distribution (log-normal, Rayleigh, Weibull, Gamma, α-Stable, Nakagami). We then perform a Kolmogorov-Smirnov [12] goodness-of-fit hypothesis test. If a value 0 is obtained, this means that the proposed distribution cannot be rejected.

• Second, we consider a linear relation between the throughput and – log10(jitter). The residue (res=throughput+ log10(jitter)) is then modelled with a Gaussian random variable.

The results of the goodness of fit test are given in Table 7.3. In that case, four distributions pass the test. The highest p-value is obtained with the α-stable distribution. To illustrate the good fit, we present in Figure 7.7 the two distributions with the highest p-value along with the distribution estimated for the data. The good fit is well illustrated and the importance of the tail of the distribution (presence of large jitter but rarely) is shown resulting in the heavy tailed stable best fit.

Distribution KS p-Value Parameters Log-normal 0 0.386 1.113 0.4418 Rayleigh 1 Weibull 1 Gamma 0 0.473 0.6084 Stable 0 0.834 α = 1.67 β = 1 γ = 0.836 δ = 2.98 Nakagami 0 0.154 µ = 1.52 Ω= 13.3

Table 7.3: Results of the statistical analysis and model selection of the Jitter for slow fading case

Slow fading case means that a train is stop or slowly approaching or leaving a train station. KS is the result of the Kolmogorov-Smirnov goodness-of-fit hypothesis test, KS=0 meaning that the proposed law is selected

8 10 12 14 16 18 20 22 24

Throughput

0

2

4

6

8

10

12

Jitte

r


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Figure 7.7: Probability density function and cumulative distribution of the jitter (data, Gamma distribution, α-stable distribution)

The second step is to model the throughput. As noticed in Figure 7.8 a strong correlation exists between throughput and jitter. If T is the Throughput and J the jitter, we propose the following model:

𝑇𝑇 = −𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) + 𝑏𝑏 + 𝑁𝑁 (7.6) where N is a Gaussian noise, a and b are constant that have to be fitted. Figure 7.9 represents −𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) as a function of T. A straight line tendency is observed. The title gives the equation of the mean straight line. Figure 7.8 represents the residue 𝑁𝑁 = 𝑇𝑇 + 𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) −𝑏𝑏, its probability density function (from the data and the fitted Gaussian model).

Figure 7.8: Link between jitter and throughput. In that case we have a=1/0.0633=15.8 and b=1.42/0.0633=22.4.

2 4 6 8x

0

0.1

0.2

0.3

0.4

f X(x

)

2 4 6 8x

0

0.2

0.4

0.6

0.8

1

P(X<

x)

Data

Gamma

stable

8 10 12 14 16 18 20 22 24

Throughput

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

-log

10(J

itter

)

Line: 0.0633.x + -1.42


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Figure 7.9: 𝑵𝑵 = 𝑻𝑻 + 𝒂𝒂. 𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍(𝑱𝑱) − 𝒃𝒃 and its probability density function (from the data – circle, black – and the fitted Gaussian model – red).

To summarize: the statistical model is given by:

1. A distribution for the Jitter with a given set of parameters. 2. A linear relationship between the log of the jitter and the throughput (two coefficients a

and b. 3. The mean and variance of the residual noise.

To generate a new sample, you draw the jitter J from the specified distribution, draw the residual noise and calculate 𝑇𝑇 = −𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) + 𝑏𝑏 + 𝑁𝑁. The following Table 7.4 summarizes different models obtained for different tested configuration in the Wi-Fi cases that have been presented in Table 7.2. Time evolution has also to be further studied. Jitter given in seconds; throughput in Mbits/s.

Environment Jitter model

Parameters Straight line (ax+b)

Gaussian

a b υ σ T0 Stable α=1.67 β=1 γ=0.837 δ=2.98 15.8 22.4 0.6

8 0.239

T1 Log-normal

µ=2.18 σ=0.823

10.0 17.1 -0.13

0.639

T2 Stable α=1.60 β=1 γ=1.24 δ=4.90 17.6 24.0 0.343

0.23

T3 – Hilly I Stable α=1.38 β=1 γ=3.87 δ=13.9 7.58 13.3 -0.5

0.324

50 100 150 200 250 300 350 400 450 500

Time

0

0.5

1

1.5

Res

idue

-0.5 0 0.5 1 1.5 2x

0

1

2

Prob

(resi

due=

x)

Mean : 0.683; Variance : 0.239


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8 T3bis Stable α=1.48 β=1 γ=1.97 δ=10.4 8.3 14.1 -

0.335

0.217

T4 Log-normal

µ=1.07 σ=0.451

14 21.5 0.707

0.226

Stable α=1.48 β=1 γ=0.767 δ=2.69 T5 Log-

normal µ=2.25 σ=0.2

62 12.2 18.3 -

0.178

0.167

Stable α=1.71 β=1 γ=1.57 δ=9.24 T6 Stable α=1.57 β=1 γ=0.949 δ=4.49 14.1 20.1 0.3

39 0.186

Log-normal

µ=1.56 σ=0.341

T7 Stable α=1.74 β=1 γ=7.68 δ=39.6 6.47 12.4 1.32

0.242 Log-normal

µ=3.70 σ=0.298

T7 (1800) Stable α=1.58 β=1 γ=10.1 δ=43.1 5.5 10.9 -1.44

0.116 Log-normal

µ=3.82 σ=0.379

T8 Stable α=1.48 β=1 γ=0.917 δ=3.86 13.4 19.9 0.434

0.205 Log-normal

µ=1.44 σ=0.377

T9 Stable α=1.26 β=1 γ=6.50 δ=24.5 4.73 9.39 -1.08

0.339

Cutting first half

Stable α=1.09 β=1 γ=0.726 δ=1.83 4.28 2.45 -0.58

0.324

Cutting second half

Stable α=1.59 β=1 γ=4.40 δ=23.2 4.26 6.98 0.628

0.162

Table 7.4: Parameters for the different models of the Wi-Fi KPIs (jitter and throughput)

Tests with LTE Different tests have been carried out in the LTE emulation platforms described in [D2.1] and [D3.1], where the IPERF tool has been used in order to obtain the target KPIs (effective throughput jitter and PER). We have considered the Hilly terrain 3 taps channel model extracted from [D1.3] with tests during 40 min. The LTE configuration considered is also described in [D3.2]. The speed of the train is set at 200 km/h. We performed the test in UL and DL with UDP IPERF traffic equal 1 Mbits/s. The following table 7.5 sums up the characteristics of the two tests. The results presented correspond to the OAI software version considered in [D3.2].

Tests number Test characteristics

T0 Testing in DL for band 7 in Hilly terrain with 3 Taps, at 200km/h, UDP traffic 1 Mbits/s, test duration 40 min


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T1 Testing in UL for band 7 in Hilly terrain with 3 Taps, at 200km/h, UDP traffic 1 Mbits/s, test duration 40 min

Table 7.5: Test summary over the LTE emulation platform

LTE Downlink For the LTE system, very similar steps are used for the modelling. We work with similar KPIs: throughput, jitter and packet error rate. In the following, we are going to model the jitter and the throughput jointly. To follow our modelling steps we illustrate it on the results obtained with hilly I (3 taps) channel model scenario (measurement in DL at 1Mbits/s UDP traffic sent with a packet length equals to 1450 bytes 200 km/h during 40 min). Figure 7.10 shows the evolution versus time for the three KPI.

Figure 7.10: PER, Jitter and in DL transmission at 200 km/h with 1 Mbits/s sent in the case of the hilly I (3 taps)

We notice that the throughput in DL is constant during the whole emulation. One thing that is important to notice also is that the jitter and the PER are correlated random variables as shown in Figure 7.11.

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0

0.5

PER

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800

Jitte

r

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904

904.5

905

Thro

ughp

ut


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Figure 7.11: Jitter as a function of bandwidth in the case of hilly I (3 taps)

To create the model we used two steps:

• First, we model the Jitter. We proceed to a model selection. We analysed the acceptability of several distribution (log-normal, Rayleigh, Weibull, Gamma, α-Stable, Nakagami). A perform a Kolmogorov-Smirnov goodness-of-fit hypothesis test. If a value 0 is obtained, this means that the proposed distribution cannot be rejected.

• Second, we consider a linear relation between the PER and log10(jitter). The residue (res=PER - log10(jitter)) is then modelled with a Gaussian random variable

The results of the goodness of fit test are given in Table 7.6. In that case, only the log-normal distribution passes the test. To illustrate the good fit, we present in Figure 7.12 the log-normal distribution but also the α-stable for comparison. The good fit is well illustrated. The Kolmogorov-Smirnov goodness-of-fit hypothesis test, KS=0 meaning that the proposed distribution is not rejected. In this case only the log-normal distribution passes the test. Distribution KS p-Value Parameters Log-normal 0 0.178 4.44 0.614 Rayleigh 1 Weibull 1 Gamma 1 Stable 1 0.0421 alp = 1.15 beta = 1 gam = 25.0 delta =

68.0 Nakagami 1

Table 7.6: Results of the statistical analysis and model selection of the Jitter.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

PER

0

200

400

600

800

1000

Jitte

r


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Figure 7.12: Probability density function and cumulative distribution of the jitter (data, Gamma distribution, α-stable distribution)

The second step is to model the PER. As noticed in Figure 7.13, a strong correlation exists between PER and jitter. If P is the PER and J the jitter, we propose the following model:

𝑃𝑃 = 𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) + 𝑏𝑏 + 𝑁𝑁 (7.7)

where N is a Gaussian noise, a and b are constant that have to be fitted. Figure 7.13 represents P as a function of 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽). A straight line tendency is observed. The equation of the mean straight line is given. Figure 7.14 represents the residue 𝑁𝑁 = 𝑃𝑃 − 𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) − 𝑏𝑏, its probability density function (from the data and the fitted Gaussian model).

Figure 7.13: Link between jitter and PER. In that case we have a=0.45 and b=-0.628.

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0.008

0.01

0.012Jitter - PDF

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Lognormal

stable

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log10 (Jitter)

-0.2

0

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PER

Line: 0.45.x - 0.628


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Figure 7.14: 𝑵𝑵 = 𝑻𝑻 − 𝒂𝒂. 𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍(𝑱𝑱) − 𝒃𝒃 and its probability density function (from the data – circle, black – and the fitted Gaussian model – red).

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2.2

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esid

ue

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(resi

due=

x)



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To summarize: the statistical model is given by: 1. A distribution for the Jitter with a given set of parameters. 2. A linear relationship between the log of the jitter and the throughput (two coefficients a

and b. 3. The mean and variance of the residual noise.

To generate a new sample, you draw the jitter J from the specified distribution, draw the residual noise and calculate

𝑃𝑃 = 𝑎𝑎. 𝑙𝑙𝑜𝑜𝑙𝑙10(𝐽𝐽) + 𝑏𝑏 + 𝑁𝑁. (7.8) LTE UpLink The KPIs obtained over a long measurement period (40 min) are given Figure 7.15. We can observed important variations, then, they are much more difficult to analyse.

0%

20%

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60%

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100%

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(%)

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0,00

500,00

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Figure 7.15: Throughput, jitter and PER in UL transmission at 200 km/h with 1 Mbits/s sent in the case of the hilly I (3 taps)

The Jitter, PER and throughput drawn as function of each other are given on Figure 7.16. It is quite difficult to deduce clear models as we did for the DL transmission. The probability density function of the jitter for instance cannot be well modelled with a continuous random variable (see figure 7.17) and further solutions should be studied. We remind here that with the OAI version considered for the project we observed a lack of performances while considering stress tests with IPERF particularly in UL direction as explained in [D3.2].

Figure 7.16: jitter, PER and throughput represented in functions of each other. The quantified values of throughput but also jitter can clearly be observed

Figure 7.17: Probability density function of the jitter (data, log-normal distribution)

902904906908910912914916918

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Environment Jitter

model Parameters Straight line

(ax+b) Gaussian

a b υ σ LTE – Hilly Downlink

Log-normal

µ=4.44 σ=0.613 0.45 0.628 2.45

0.213

LTE – Hilly Uplink

Stable α=2 β=-0.97 γ=326 δ=2240 0.385 0.396 3.39

0.0846

Table 7.7: Parameters for the different models of the LTE KPIs


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8. Conclusions and perspectives We can draw several conclusions from these tests. The KPI modelling is very similar in Wi-Fi whatever the channel, with or without interference and for the LTE downlink. The results obtained with uplink transmission need further studies with new release of Open Air Interface system as explained in [D3.2]. Nevertheless, the presented methodology is valid.

• First of all, for the Jitter, the α-stable distribution fits most of the time. The log-normal also, but except in rare situations (T5, downlink LTE) its p-value is smaller (which means that the stable model fits better). The main conclusion is that it is important to account for the tail of the distributions that will model rare events. Those events are those that will degrade the communications. Especially if safety is involved, it is very important to take them into account. The use of stable distribution is highly recommended.

• The linear model between the log of the jitter and the bandwidth or the PER is a good fit. It could certainly be improved however, and a special care should be taken to check if the proposed relationship is sufficient to account for all the dependence between the two parameters.

• The interference does not change the model but significantly modifies the parameters and increasing the tail; indeed the characteristic exponent is slightly smaller than one, which corresponds to a significantly heavier tail.

• In most of the cases, the characteristic exponent of the stable distribution modelling the jitter is between 1.35 and 1.75. In some cases, it can be smaller, meaning a stronger impulsiveness in the jitter. This happens in the WIFI case with an interferer. The skewness is always 1, the jitter being always positive. Important variations can be noticed in the dispersion and in the location parameters.

• We can find some similarities in the different residues in the different contexts but further analysis is needed.

The next step to go further in the statistical analysis will be to analyse and model the time evolution of the KPIs but unfortunately it was not possible within the project duration. Nevertheless, we consider that the proof of concept related to the methodology for derivation of IP impairment models from the experimental assessment is done. A large amount of tests with various channel models and various perturbations and various scenarios should be performed to obtain different KPIs models representative of Radio Railway environments at IP level. This constitute a perspective of the project. Then, in case that the RF channel emulators are not included on testing over real traffic, the use of the different statistical equations derived for the different IP impairments can be used to emulate the air-gap impact over real IP traffic. This can be implemented using the Netem tool or similar, which is the same approach followed for the satellite link emulation at IP level, as described in section 7.1.


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9. References [D1.3] Emulradio4rail Deliverable D1.3, Characterization of the railway environment: channel

models & general characteristics, Grant 826152, December 2019 [D1.2] Emulradio4rail Deliverable D1.2, Perturbation in Railway communications (Stream c), Grant

826152, December 2019 [D2.1] Emulradio4rail Deliverable D2.1, Solutions to emulate the Radio Bearer (stream b), Grant

826152, May 2019 [D3.1] Emulradio4rail Deliverable D3.1, High-level design of Radio access emulation tool (stream

b), Grant 826152, October 2019 [D3.2] Emulradio4rail Deliverable D3.1, Experimental assessment, Grant 826152, May 2019 [D3.3] Emulradio4rail Deliverable D3.3, Design and implementation of radio access emulation tool,

Demonstrator, Grant 826152, November 2020 [1] ITU-T Y.1542, ITU G.1050, SERIES G: TRANSMISSION SYSTEMS AND MEDIA, DIGITAL

SYSTEMS AND NETWORKS, “Quality of service and performance – Generic and user-related aspects Network model for evaluating multimedia transmission performance over Internet Protocol”

[2] TIA-921 “Network Model for Evaluating Multimedia Transmission Performance Over the Internet Protocol” was created by TIA TR-30.3 Subcommittee on Data Communications Equipment Evaluation and Network Interfaces and released June 2006 [2] .

[3] ITU G.1050, SERIES G: TRANSMISSION SYSTEMS AND MEDIA, DIGITAL SYSTEMS AND NETWORKS, Quality of service and performance – Generic and user-related aspects Network model for evaluating multimedia transmission performance over Internet Protocol

[4] ITU-T Y.1541, TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (02/2006), SERIES Y: GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS AND NEXT-GENERATION NETWORKS Internet protocol aspects – Quality of service and network Performance Network performance objectives for IP-based services

[5] Netem, https://www.linux.org/docs/man8/tc-netem.html [6] Austin, Osahenvemwen. (2015). “OVERVIEW OF TCP PERFORMANCE IN SATELLITE

COMMUNICATION NETWORKS”, International Journal of Technical Research and Applications e-ISSN: 2320-8163, p. 360-364.

[7] ETSI TR 102 157, “Satellite Earth Stations and Systems (SES); Broadband Satellite Multimedia; IP Interworking over satellite. Performance, Availability and Quality of Service”

[8] Yi Li, Xin Tian, Qi Zhao, Khanh Pham, James Lyke, Erik Blasch, Genshe Chen, “Throughput modeling and analysis for TCP over TCP satellite communications”, Proceedings Volume 11422, Sensors and Systems for Space Applications XIII; 1142209 (2020)

[9] Kaufmann, Christof & Huth, Hans-Peter & Zeiger, Florian & Schmidt, Marco, “IP Link Model for Industrial Satellite Communication”, 70th International Astronautical Congress at Washington D.C., USA, 2019.

[10] F. Mazzenga, R. Giuliano, and A. Vizzarri, “End-to-End Delay Model for Train Messaging over Public Land Mobile Networks”, 2017, MDPI.


https://www.linux.org/docs/man8/tc-netem.html

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[11] 3InSat- https://business.esa.int/projects/3insat [12] Massey, F. J. “The Kolmogorov-Smirnov Test for Goodness of Fit.” Journal of the

American Statistical Association. Vol. 46, No. 253, 1951, pp. 68–78. [13] ETSI TR 102 157 V1.1.1 (2003-07) Technical Report Satellite Earth Stations and

Systems (SES); Broadband Satellite Multimedia; IP Interworking over satellite; Performance, Availability and Quality of Service.

[14] Reddy, A. and Sumitha Bhandarkar. “Congestion control algorithms of tcp in emerging networks.” (2006) (https://cesg.tamu.edu/wp-content/uploads/2012/02/TAMU-ECE-2006-10.pdf)

[15] M. Allman, D. Glover, and L. Sanchez, “Enhancing TCP over satellite channels using standard mechanisms,” RFC 2488, Internet Engineering Task Force, January 1999, Available: http://www.ietf.org/rfc/rfc2488.txt; Accessed: June 05, 2006

[16] Kota S.L., Pahlavan K., Leppanen P. (2004) Satellite TCP/IP: Technical Challenges. In: Broadband Satellite Communications for Internet Access. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8895-9_7

[17] O. Kodheli et al., "Satellite Communications in the New Space Era: A Survey and Future Challenges," in IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 70-109, Firstquarter 2021, doi: 10.1109/COMST.2020.3028247. Fabien Garcia, Alain Pirovano, Mathieu Magnaudet. “Satellite link emulation platform for aeronautical application validation”. ICSSC 2010, AIAA 28th International Communications Satellite Systems Conference, Aug 2010, Anaheim, United States. pp xxx, ff10.2514/6.2010-8794ff. ffhal-01022211f (link: https://hal-enac.archives-ouvertes.fr/hal-01022211/file/191.pdf)

[18] Ramos, J.. “Proactive measurement techniques for network monitoring in heterogeneous environments”, Universidad Autonoma de Madrid, Escuela Politecnica Superior, Departamento de Tecnologia Electronica y de las Comunicaciones, 2013. (link: http://arantxa.ii.uam.es/~jramos/publications/dissertation.pdf)

[19] Roshan, Mujtaba & Schormans, John & Ogilvie, Rupert. (2018). Video-on-demand QoE Evaluation Across Different Age- Groups and Its Significance for Network Capacity. ICST Transactions on Mobile Communications and Applications. 3. 153557. 10.4108/eai.10-1-2018.153557.

[20] Mujtaba Roshan, “QoE Evaluation Across a Range of User Age Groups in Video Applications”, School of Electronic Engineering and Computer Science, Queen Mary University of London, 2018. https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/44049/ROSHAN_Mujtaba_PhD_Final_090718.pdf?sequence=1&isAllowed=y

[21] Audah, L. and H. Cruickshank. “End-to-End QoS Evaluation of IP over LEO / GEO Satellites Constellations for FTP.” (2012).

[22] A. Jurgelionis, J. Laulajainen, M. Hirvonen and A. I. Wang, "An Empirical Study of NetEm Network Emulation Functionalities," 2011 Proceedings of 20th International


https://business.esa.int/projects/3insat

https://cesg.tamu.edu/wp-content/uploads/2012/02/TAMU-ECE-2006-10.pdf

https://cesg.tamu.edu/wp-content/uploads/2012/02/TAMU-ECE-2006-10.pdf

https://hal-enac.archives-ouvertes.fr/hal-01022211/file/191.pdf

https://hal-enac.archives-ouvertes.fr/hal-01022211/file/191.pdf

http://arantxa.ii.uam.es/%7Ejramos/publications/dissertation.pdf

https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/44049/ROSHAN_Mujtaba_PhD_Final_090718.pdf?sequence=1&isAllowed=y

https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/44049/ROSHAN_Mujtaba_PhD_Final_090718.pdf?sequence=1&isAllowed=y

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Conference on Computer Communications and Networks (ICCCN), Lahaina, HI, USA, 2011, pp. 1-6, doi: 10.1109/ICCCN.2011.6005933.

[23] Botta, A. et al. “A tool for the generation of realistic network workload for emerging networking scenarios.” Comput. Networks 56 (2012): 3531-3547.

[24] Dairaine, Laurent & Gineste, Mathieu & Thalmensy, Herve. (2008). “Appendix B Network Emulation Focusing on QoS-Oriented Satellite Communication. 10.1007/978-3-540-79120-1_9.”

[25] REHUŠ, Patrik. WAN optimization for satellite networking [online]. Brno, 2019 [cit. 2021-03-16]. Available from: <https://is.muni.cz/th/bqz3g/>. Master's thesis. Masaryk University, Faculty of Informatics. Thesis supervisor Barbora Bühnová.

[26] Balakrishnan Chandrasekaran, “Survey of Network Traffic Models”, Washington University in St. Louis, McKelvey School of Engineering. (https://www.cse.wustl.edu/~jain/cse567-06/ftp/traffic_models3/index.html)

[27] Victor S. Frost and Benjamin Melamed, "Traffic Modeling for Telecommunications Networks", IEEE Communications, Mar. 1994. http://ieeexplore.ieee.org/document/267444/

[28] Abdelnaser Adas, “Traffic Models in Broadband Networks”, IEEE Communications Magazine, Jul. 1997. http://ieeexplore.ieee.org/document/601746/

[29] Jurkiewicz, Piotr & Rzym, Grzegorz & Borylo, Piotr, “Flow length and size distributions in campus Internet traffic”, 2018.

[30] A. Pras, L. Nieuwenhuis, R. van de Meent, and M. Mandjes, “Dimensioning network links: A new look at equivalent bandwidth,” IEEE Network, 2009.

[31] R. V. D. Meent, M. Mandjes, and A. Pras, “Gaussian traffic everywhere?” in IEEE International Conference on Communications, 2006.

[32] C. Fraleigh, F. Tobagi, and C. Diot, “Provisioning IP backbone networks to support latency sensitive traffic,” in IEEE INFOCOM, 2003

[33] Alasmar, M., & Zakhleniuk, N. (2017). Network Link Dimensioning based on Statistical Analysis and Modeling of Real Internet Traffic. ArXiv, abs/1710.00420.

[34] PATTAVINA, ACHILLE & Tornatore, Massimo, “IP traffic characterization: An overview”, 2002.

[35] V. Paxson and S.Floyd, Wide Area Traffic: The failure of Poisson modeling, IEEE/ACM Transactions on Networking, Vol. 3, No. 3, 1995, pp. 226-244.

[36] Ulrich Speidel “Simulating satellite Internet traffic to a small island Internet provider”, University of Auckland (2017) (https://isif.asia/simulating-satellite-internet-traffic-to-a-small-island-internet-provider/) (https://unidirectory.auckland.ac.nz/profile/u-speidel)

[37] Ixia Black Box, edition 10, Network Impairment (2013) (https://support.ixiacom.com/sites/default/files/resources/blackbook/network_impairment_915-2633-01_revc.pdf

[38] Spirent GEM v3.2 & XGEM v3.0, 10/100/1G/10G Ethernet Network & Impairment Emulators, 2008 https://www.spirentfederal.com/documents/spirent_gem_xgem_presentation_09-2008.ppt


https://www.cse.wustl.edu/%7Ejain/cse567-06/ftp/traffic_models3/index.html

http://ieeexplore.ieee.org/document/267444/

http://ieeexplore.ieee.org/document/601746/

https://isif.asia/simulating-satellite-internet-traffic-to-a-small-island-internet-provider/

https://isif.asia/simulating-satellite-internet-traffic-to-a-small-island-internet-provider/

https://unidirectory.auckland.ac.nz/profile/u-speidel

https://support.ixiacom.com/sites/default/files/resources/blackbook/network_impairment_915-2633-01_revc.pdf

https://support.ixiacom.com/sites/default/files/resources/blackbook/network_impairment_915-2633-01_revc.pdf

https://www.spirentfederal.com/documents/spirent_gem_xgem_presentation_09-2008.ppt

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[39] Jeff Fulkerson, “Traffic Shaping with tc”, Badu Networks Blog, 2017 (https://www.badunetworks.com/traffic-shaping-with-tc/)

[40] GL Communications, Satellite Communications Applications, Testing, & Test Tools” https://www.gl.com/telecom-test-solutions/testing-satellite-communications.html

[41] GL Communications, “Network Impairment Simulator” (https://www.gl.com/telecom-test-solutions/network-impairments-simulation.html)

[42] GL Communications, “Press Release : GL Enhances High-Density Bulk Call Generator for IP, IMS & LTE Networks”, https://www.gl.com/press-release/high-density-bulk-call-generator-ip-ims-lte-networks-press-release.html

[43] F. Rispoli, “The rise of satellite technology appeal for train control systems”, IRSE Australasia Conference 2018, Melbourne, Australia.


https://www.badunetworks.com/traffic-shaping-with-tc/

https://www.gl.com/telecom-test-solutions/testing-satellite-communications.html

https://www.gl.com/telecom-test-solutions/network-impairments-simulation.html

https://www.gl.com/press-release/high-density-bulk-call-generator-ip-ims-lte-networks-press-release.html

https://www.gl.com/press-release/high-density-bulk-call-generator-ip-ims-lte-networks-press-release.html

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10. Annex – complete set of statistical analysis.

This annex gives the full set of results for the measurements made by IKERLAN, Univ. Lille and Univ. Gustave Eiffel in the WIFI and LTE setups. The test procedure is described in the main part of this deliverable. If further details are needed, do not hesitate to contact us. We first give in Table 1 a summary of the results with the best candidates for the Jitter and throughput joint models. Jitters are given in s and bandwidth in Mbits/s.

Environment Jitter model

Parameters Straight line (ax+b)

Gaussian

a b µ σ

T0 Stable α=1.67 β=1 γ=0.837 δ=2.98 15.8 22.4 0.68 0.239 T1 Lognormal µ=2.18 σ=0.823 10.0 17.1 -0.13 0.639 T2 Stable α=1.60 β=1 γ=1.24 δ=4.90 17.6 24.0 0.343 0.23

T3 – Hilly I Stable α=1.38 β=1 γ=3.87 δ=13.9 7.58 13.3 -0.58 0.324 T3bis Stable α=1.48 β=1 γ=1.97 δ=10.4 8.3 14.1 -

0.335 0.217

T4 Lognormal µ=1.07 σ=0.451 14 21.5 0.707 0.226 Stable α=1.48 β=1 γ=0.767 δ=2.69

T5 Lognormal µ=2.25 σ=0.262 12.2 18.3 -0.178

0.167 Stable α=1.71 β=1 γ=1.57 δ=9.24

T6 Stable α=1.57 β=1 γ=0.949 δ=4.49 14.1 20.1 0.339 0.186 Lognormal µ=1.56 σ=0.341


T7 (1800) Stable α=1.58 β=1 γ=10.1 δ=43.1 5.5 10.9 -1.44 0.116 Lognormal µ=3.82 σ=0.379


T9 Stable α=1.26 β=1 γ=6.50 δ=24.5 4.73 9.39 -1.08 0.339

Cutting first half

Stable α=0.955 β=1 γ=0.460 δ=1.37 4.28 2.45 -0.58 0.324

Cutting second half

Stable α=1.59 β=1 γ=4.40 δ=23.2 4.26 6.98 0.628 0.162

LTE – Hilly Downlink

Lognormal µ=4.44 σ=0.613 0.45 0.628 2.45 0.213

LTE - Uplink Stable α=2 β=-0.97 γ=326 δ=2240 0.385 0.396 3.39 0.0846

Table 1: summary of results.


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T0-flat_channel_50db_att

Table 2: Results of the parameter estimation and the Kolmogorov-Smirnov test result for all candidate laws. A KS equal to 0 means we cannot reject the proposed distribution at a

5% significance level.

Distribution KS p-Value Parameters Log-normal 0 0.386 1.1130 0.4418

Rayleigh 1 5.62e-05 2.582 Weibull 1 0.0408 3.7744 2.3842 Gamma 0 0.473 5.4942 0.6084 Stable 0 0.834 alp = 1.67 beta = 1 gam =

0.837 delta =

2.98 Nakagami 0 0.154 mu = 1.52 om =

13.3

Figure 1: measured samples.

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Figure 2: Measured samples – 2 by 2 dependence.

Figure 3: Tested densities for the jitter.

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Figure 4: Relation between Jitter (J) and Throughput (B). B =-a.log10(J)+b+N.

Figure 5: The residue N and its pdf.

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T1-Hilly_I_no_doppler



Distribution KS p-Value Parameters Log-normal 1 9.664e-07 2.1780 0.8231

Rayleigh 1 1.868e-84 14.2928 Weibull 1 1.5602e-

10 13.7344 1.0777

Gamma 1 5.3656e-13

1.3716 9.6685

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23 mu=0.398 omega=408


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T2-Hilly_I_10kmh_doppler





05 6.2890 2.2643

Gamma 0 0.150 6.2702 0.8911 Stable 0 0.830 alp = 1.60 beta = 1 gam = 1.24 delta =

4.90 Nakagami 1 0.00107 Mu=1..593 om=37.5


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T3-Hilly_I_100kmh_doppler_2





09 13.43 2.44

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Mean : -0.335; Variance : 0.217


G A 826152 P a g e 56 | 93

T3-Hilly_I_100kmh_doppler_3_1800points




Rayleigh 1 7.448e-19 13.52 Weibull 1 6.56e-11 19.56 2.223 Gamma 1 3.26e-05 5.1714 3.3375 Stable 0 0.0875 alp = 1.38 beta = 1 gam =

3.87 delta =

13.9 Nakagami 1 5.787e-13 mu=1.38 om=366


200 400 600 800 1000 1200 1400 1600 1800

Time

2

4

6

Thro

ughp

ut

200 400 600 800 1000 1200 1400 1600 1800

Time

10

20

30

40

50

Jitte

r

200 400 600 800 1000 1200 1400 1600 1800

Time

0

2

4

Pack

et L

oss

0 2 4 6

Throughput

0

10

20

30

40

50

60

Jitte

r

0 2 4 6

Throughput

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Pack

et L

oss

0 20 40 60

Jitter

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Pack

et L

oss


G A 826152 P a g e 57 | 93




1 2 3 4 5 6 7 8

Throughput

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-log

10(J

itter

)

Line: 0.132.x + -1.76


G A 826152 P a g e 58 | 93


200 400 600 800 1000 1200 1400 1600 1800

Time

-1.5

-1

-0.5

0

Res

idue

-2 -1.5 -1 -0.5 0 0.5

x

0

0.5

1

1.5

Prob

(resi

due=

x)

Mean : -0.581; Variance : 0.324


G A 826152 P a g e 59 | 93

T4-Rural_II_no_doppler




Rayleigh 1 1.03e-4 2.58 Weibull 1 1.47e-3 3.68 2.09 Gamma 0 0.108 4.94 0.658 Stable 0 0.483 alp = 1.48 beta = 1 gam=0.767 delta =

2.69 Nakagami 1 1.82e-3 mu=1.29 om=13.3


50 100 150 200 250 300 350 400 450 500

Time

81012141618

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

5

10

15

Jitte

r

0 100 200 300 400 500 600

Time

-1

0

1

Pack

et L

oss

5 10 15 20

Throughput

0

2

4

6

8

10

12

14

16

18

Jitte

r

5 10 15 20

Throughput

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Pack

et L

oss

0 10 20

Jitter

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Pack

et L

oss


G A 826152 P a g e 60 | 93




6 8 10 12 14 16 18 20

Throughput

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

-log

10(J

itter

)

Line: 0.0714.x + -1.54


G A 826152 P a g e 61 | 93


50 100 150 200 250 300 350 400 450 500

Time

0

0.5

1

Res

idue

-0.5 0 0.5 1 1.5 2x

0

0.5

1

1.5

2

Prob

(resi

due=

x)



G A 826152 P a g e 62 | 93

T5-Rural_II_100kmh_doppler




Rayleigh 1 5.83e-34 7. 52 Weibull 1 6.88e-8 10.9 3.10 Gamma 0 0.313 14.25 0.691 Stable 0 0.639 alp = 1.71 beta = 1 gam =

1.57 delta =

9.24 Nakagami 0 0.0502 mu=3.46 om=105


50 100 150 200 250 300 350 400 450 500

Time

4

6

8

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

10

20

30

Jitte

r

50 100 150 200 250 300 350 400 450 500

Time

0

1

Pack

et L

oss

4 6 8

Throughput

0

5

10

15

20

25

30

35

40

Jitte

r

4 6 8

Throughput

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Pack

et L

oss

0 20 40

Jitter

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Pack

et L

oss


G A 826152 P a g e 63 | 93




2 3 4 5 6 7 8 9

Throughput

-1.6

-1.5

-1.4

-1.3

-1.2

-1.1

-1

-0.9

-0.8

-0.7

-0.6

-log

10(J

itter

)

Line: 0.0817.x + -1.5


G A 826152 P a g e 64 | 93


50 100 150 200 250 300 350 400 450 500

Time

-1

-0.5

0

Res

idue

-1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4x

0

1

2

3

Prob

(resi

due=

x)

Mean : -0.178; Variance : 0.167


G A 826152 P a g e 65 | 93

T6-Viaduct_I_no_doppler




Rayleigh 1 6.95e-16 1.56 Weibull 1 2.02e-06 5.69 2.67 Gamma 1 3.69e-03 8.44 0.601 Stable 0 0.914 alp = 1.57 beta = 1 gam=0.949 delta =

4.49 Nakagami 1 1.19e-05 mu=2.12 om=29.4


50 100 150 200 250 300 350 400 450 500

Time

5

10

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

5

10

15

Jitte

r

0 100 200 300 400 500 600

Time

-1

0

1

Pack

et L

oss

4 6 8 10 12

Throughput

2

4

6

8

10

12

14

16

18

Jitte

r

4 6 8 10 12

Throughput

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Pack

et L

oss

0 10 20

Jitter

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Pack

et L

oss


G A 826152 P a g e 66 | 93




3 4 5 6 7 8 9 10 11 12 13

Throughput

-1.3

-1.2

-1.1

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-log

10(J

itter

)

Line: 0.0709.x + -1.42


G A 826152 P a g e 67 | 93


50 100 150 200 250 300 350 400 450 500

Time

-0.5

0

0.5

Res

idue

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1x

0

1

2

3

Prob

(resi

due=

x)



G A 826152 P a g e 68 | 93

T7-Viaduct_I_100kmh_doppler




Rayleigh 1 2.51e-23 31.2 Weibull 1 6.76e-04 46.8 3.30 Gamma 0 0.209 11.5 3.675 Stable 0 0.678 alp = 1.74 beta = 1 gam =

7.68 delta =

39.6 Nakagami 1 0.0163 mu=2.97 om=1948



50 100 150 200 250 300 350 400 450 500

Time

1

2

3

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

50

100

Jitte

r

50 100 150 200 250 300 350 400 450 500

Time

0

10

20

30

Pack

et L

oss

0 2 4

Throughput

0

20

40

60

80

100

120

Jitte

r

0 2 4

Throughput

0

5

10

15

20

25

30

Pack

et L

oss

0 50 100

Jitter

0

5

10

15

20

25

30

Pack

et L

oss


G A 826152 P a g e 69 | 93



0 0.5 1 1.5 2 2.5 3 3.5 4

Throughput

-2.1

-2

-1.9

-1.8

-1.7

-1.6

-1.5

-1.4

-1.3

-1.2

-log

10(J

itter

)

Line: 0.155.x + -1.92

50 100 150 200 250 300 350 400 450 500

Time

-2

-1.5

-1

Res

idue

-2.5 -2 -1.5 -1 -0.5 0x

0

0.5

1

1.5

2

Prob

(resi

due=

x)



G A 826152 P a g e 70 | 93



G A 826152 P a g e 71 | 93

T7-Viaduct_I_100kmh_doppler_2_1800points




Rayleigh 1 7.05e-37 38. 2 Weibull 1 2.64e-18 55.56 2.266 Gamma 1 3.00e-05 6.78 7.28 Stable 0 0.0852 alp = 1.58 beta = 1 gam =

10.1 delta =

43.1 Nakagami 1 7.29e-13 mu=1.66 om=2919



200 400 600 800 1000 1200 1400 1600 1800

Time

0

2

Thro

ughp

ut

200 400 600 800 1000 1200 1400 1600 1800

Time

50

100

150

200

250

Jitte

r

200 400 600 800 1000 1200 1400 1600 1800

Time

0

50

Pack

et L

oss

0 2 4

Throughput

0

50

100

150

200

250

300

Jitte

r

0 2 4

Throughput

0

10

20

30

40

50

60

70

80

Pack

et L

oss

0 100 200 300

Jitter

0

10

20

30

40

50

60

70

80

Pack

et L

oss


G A 826152 P a g e 72 | 93



0 0.5 1 1.5 2 2.5 3 3.5 4

Throughput

-2.5

-2

-1.5

-1

-log

10(J

itter

)

Line: 0.182.x + -1.99

200 400 600 800 1000 1200 1400 1600

Time

-3

-2

-1

Res

idue

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

x

0

0.5

1

1.5

Prob

(resi

due=

x)



G A 826152 P a g e 73 | 93



G A 826152 P a g e 74 | 93

T8-Cutting_II_no_doppler




Rayleigh 1 8.507e-12 3.467 Weibull 1 1.51e-4 5.096 2.446 Gamma 1 0.0140 6.918 0.654 Stable 0 0.833 alp = 1.48 beta = 1 gam=0.917 delta =

3.86 Nakagami 1 8.61e-05 mu=1.76 om=24.0



50 100 150 200 250 300 350 400 450 500

Time

6

8

10

12

14

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

5

10

15

Jitte

r

50 100 150 200 250 300 350 400 450 500

Time

0

0.5

Pack

et L

oss

5 10 15

Throughput

0

2

4

6

8

10

12

14

16

18

Jitte

r

5 10 15

Throughput

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pack

et L

oss

0 10 20

Jitter

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pack

et L

oss


G A 826152 P a g e 75 | 93



5 6 7 8 9 10 11 12 13 14

Throughput

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

-log

10(J

itter

)

Line: 0.0748.x + -1.48

50 100 150 200 250 300 350 400 450 500

Time

-0.2

0

0.2

0.4

0.6

0.8

Res

idue

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2x

0

0.5

1

1.5

2

Prob

(resi

due=

x)



G A 826152 P a g e 76 | 93



G A 826152 P a g e 77 | 93

T9-Cutting_II_100kmh_doppler




Rayleigh 1 1.16e-08 24.4 Weibull 1 1.68e-07 35.5 2.295 Gamma 1 2.04e-05 5.34 5.87 Stable 0 0.131 alp = 1.26 beta = 1 gam =

6.50 delta =

24.5 Nakagami 1 1.33e-08 mu=1.43 O

m=1193


50 100 150 200 250 300 350 400 450 500

Time

2

4

Thro

ughp

ut

50 100 150 200 250 300 350 400 450 500

Time

20

40

60

80

Jitte

r

50 100 150 200 250 300 350 400 450 500

Time

0

10

20

Pack

et L

oss

0 2 4

Throughput

10

20

30

40

50

60

70

80

90

Jitte

r

0 2 4

Throughput

0

2

4

6

8

10

12

14

16

18

20

Pack

et L

oss

0 50 100

Jitter

0

2

4

6

8

10

12

14

16

18

20

Pack

et L

oss


G A 826152 P a g e 78 | 93




0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Throughput

-2

-1.9

-1.8

-1.7

-1.6

-1.5

-1.4

-1.3

-1.2

-1.1

-1

-log

10(J

itter

)

Line: 0.211.x + -1.98


G A 826152 P a g e 79 | 93


50 100 150 200 250 300 350 400 450 500

Time

-2

-1.5

-1

-0.5

Res

idue

-2.5 -2 -1.5 -1 -0.5 0x

0

0.5

1

1.5

Prob

(resi

due=

x)



G A 826152 P a g e 80 | 93

cutting5taps_10M-50kmh-60min Next measurements have been taken during a long period of time. Figure 61 clearly show that the evolution of the environment encountered leads to several possible cases. The behavior clearly change in the middle of the test. It is clear also in Figure 63 where the throughput is represented as a function of the jitter in log scale, we have three different behavior. We analyze the two cases where the throughput is about 100 kbytes/s and the jitter varies from 10 to nearly 150 ms. The third case with a very low throughput has few points and the cause of such situations should be further analyzed.



1000 2000 3000 4000 5000 6000 7000

Time

0

500

1000

Band

wid

th

1000 2000 3000 4000 5000 6000 7000

Time

20406080

100120140

Jitte

r

1000 2000 3000 4000 5000 6000 7000

Time

0

50

Pack

et L

oss

0 500 1000

Throughput

0

50

100

150

Jitte

r

0 500 1000

Throughput

0

10

20

30

40

50

60

70

80

90

100

Pack

et L

oss

0 50 100

Jitter

0

10

20

30

40

50

60

70

80

90

100

Pack

et L

oss


G A 826152 P a g e 81 | 93

Figure 63: Measured samples – 2 by 2 dependence - Jitter and throughput. Three different behaviours are observed.

0 200 400 600 800 1000 1200

Throughput

-2.5

-2

-1.5

-1

-0.5

0

0.5

-log

10(J

itter

)


G A 826152 P a g e 82 | 93

Cutting5taps_10M-50kmh-60min - first half.




Rayleigh 1 5.3698e-12

2.636

Weibull 1 0.00173 3.286 1.473 Gamma 1 0.0274 2.410 1.221 Stable 0 0.138 alp = 1.09 beta = 1 gam =

0.726 delta =

1.83 Nakagami 1 7.176e-06 mu =

0.665 Om=13.9


50 100 150 200 250 300

Time

500

1000

Thro

ughp

ut

50 100 150 200 250 300

Time

5

10

15

20

Jitte

r

50 100 150 200 250 300

Time

0

50

Pack

et L

oss

400 600 800 1000

Throughput

0

5

10

15

20

25

Jitte

r

400 600 800 1000

Throughput

0

10

20

30

40

50

60

70

80

Pack

et L

oss

0 10 20

Jitter

0

10

20

30

40

50

60

70

80

Pack

et L

oss


G A 826152 P a g e 83 | 93




200 300 400 500 600 700 800 900 1000 1100

Throughput

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

-log

10(J

itter

)

Line: 0.000281.x + -0.598


G A 826152 P a g e 84 | 93


50 100 150 200 250 300

Time

1.5

2

2.5

3

Res

idue

1 1.5 2 2.5 3 3.5x

0

0.5

1

1.5

Prob

(resi

due=

x)



G A 826152 P a g e 85 | 93

Cutting5taps_10M-50kmh-60min - Second half



Distribution KS p-Value Parameters Log-normal 1 3e_03 3.20 0.300

Rayleigh 1 2.46e-177 19.3 Weibull 1 6.59e-73 28.6 2.55 Gamma 1 4.959e-09 10.5 2.45 Stable 1 7.152e-06 alp = 1.59 beta = 1 gam = 4.40 delta =

23.2 Nakagami 1 1.681e-18 mu=2.43 om=746


500 1000 1500 2000 2500 3000 3500

Time

100

200

Thro

ughp

ut

500 1000 1500 2000 2500 3000 3500

Time

20406080

100120140

Jitte

r

500 1000 1500 2000 2500 3000 3500

Time

0

50

Pack

et L

oss

0 100 200

Throughput

0

50

100

150

Jitte

r

0 100 200

Throughput

0

10

20

30

40

50

60

70

80

90

100

Pack

et L

oss

0 50 100 150

Jitter

0

10

20

30

40

50

60

70

80

90

100

Pack

et L

oss


G A 826152 P a g e 86 | 93




20 40 60 80 100 120 140 160 180 200 220

Throughput

-2.2

-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-log

10(J

itter

)

Line: 0.0024.x + -1.64


G A 826152 P a g e 87 | 93


500 1000 1500 2000 2500 3000 3500

Time

0

0.5

1

Res

idue

-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6x

0

1

2

3

Prob

(resi

due=

x)



G A 826152 P a g e 88 | 93

DLHilly3T1m200kmh_40min




Rayleigh 1 6.85e-50 93.2 Weibull 1 4.44e-14 116 1.49 Gamma 1 1.06e-07 2.59 40.0 Stable 1 0.0421 alp = 1.15 beta = 1 gam = 25.0 delta =

68.0 Nakagami 1 7.02e-27 mu=0.684 om=17364


200 400 600 800 1000 1200 1400

Time

0

0.5

PER

200 400 600 800 1000 1200 1400

Time

200

400

600

800

Jitte

r

200 400 600 800 1000 1200 1400

Time

904

904.5

905

Thro

ughp

ut

0 0.5

PER

0

100

200

300

400

500

600

700

800

900

1000

Jitte

r

0 0.5

PER

904

904.1

904.2

904.3

904.4

904.5

904.6

904.7

904.8

904.9

905

Thro

ughp

ut

0 500 1000

Jitter

904

904.1

904.2

904.3

904.4

904.5

904.6

904.7

904.8

904.9

905

Thro

ughp

ut


G A 826152 P a g e 89 | 93




1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

-log10 (Jitter)

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Thro

ughp

ut

Line: 0.45.x + -0.628


G A 826152 P a g e 90 | 93


200 400 600 800 1000 1200 1400

Time

2.2

2.4

2.6

2.8

3

3.2

Res

idue

1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6x

0

0.5

1

1.5

2

Prob

(resi

due=

x)



G A 826152 P a g e 91 | 93

ULtest1mCuttingRA5T_100kmh_40min




Rayleigh 1 1.38e-194 1617 Weibull 1 2.19e-12 2427 5.5 Gamma 1 9.06e-15 22.2 101 Stable 1 2.45e-11 alp = 2 beta=-0.97 gam = 326 delta=2240

Nakagami 1 3.88e-12 mu=5.92 om=5.23e6



200 400 600 800 1000 1200 1400 1600 1800 2000 2200

Time

0

0.5

PER

200 400 600 800 1000 1200 1400 1600 1800 2000 2200

Time

1000

2000

3000

Jitte

r

0 0.5 1

PER

500

1000

1500

2000

2500

3000

3500

Jitte

r


G A 826152 P a g e 92 | 93



2.9 3 3.1 3.2 3.3 3.4 3.5

-log10 (Jitter)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Thro

ughp

ut

Line: 0.385.x + -0.396

200 400 600 800 1000 1200 1400 1600 1800 2000 2200

Time

3.2

3.4

3.6

3.8

Res

idue

3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4x

0

2

4

6

Prob

(resi

due=

x)



G A 826152 P a g e 93 | 93



Deliverable D 2.2 IP impairments models (revised version)

Documents

Transcript of Deliverable D 2.2 IP impairments models (revised version)