ICT-619555 RESCUE D4.3 Version 1 - CORDIS€¦ · directional analysis of V2V propagation channels...

ICT-619555 RESCUE

D4.3 Version 1.0

Report on channel analysis and modelling

Contractual Date of Delivery to the CEC: 08/2015Actual Date of Delivery to the CEC:

Editor Martin Kaske

Author(s) Martin Kaske, Christian Schneider, Juha Meinila, Gerd Som-

merkorn, Valtteri Tervo

Participants TUIL, UOULU

Work package WP4 - Validation, Integration and Field Trials

Estimated person months 12

Security PU

Nature R

Version 1.0

Total number of pages 60

Abstract: This deliverable describes the analysis and modelling research for vehicle adhoc networks required

for conducting realistic performance validation at the OTA test facility. Two independent V2V channel sound-

ing campaigns from UOULU and TUIL are analysed to characterise the double directional wireless channel

including deep analysis of multipath components grouped in clusters. Special focus has been given to under-

stand the contribution of mobile clusters (scattering and shadowing by other vehicles) to the V2V channel.

Two important extensions of the well-known WINNER channel model to be applicable for VANETs simula-

tion have been detailed: spatial consistency and mobile scatterer. Finally the conceptual approach for a direct

usage of channel sounding data sets for system level simulation by other work packages has been detailed.

Keyword list: V2V, channel sounding, MIMO, VANET, directional channel model, GBSCM

Disclaimer: -

RESCUE D4.3, v1.0

Executive Summary

This deliverable is a report focusing to provide realistic channel data sets for different performance validation

strategies within the RESCUE project. Whereby the Vehicular Ad-hoc NETworks (VANET)s are the considered

use case within this research. The provided Vehicle-To-Vehicle (V2V) channel data sets are derived directly from

the Multiple-Input Multiple-Output (MIMO) channel sounding campaign or based on an extended WINNER chan-

nel model. Both can be applied for system level simulations and practical performance validations. The latter is

one of the main goals of the WP4 within RESCUE. The V2V channel data sets are required to drive the channel

emulators for the Over-The-Air test facility at TUIL, where a Software Defined Radio (SDR) testbed will be used

to validate the Physical Layer (PHY) and Medium-Access-Control (MAC) developed within RESCUE.

An essential prerequisite for accurate and reliable channel modelling and subsequent performance evaluation is

the channel analysis and corresponding parameter derivation. Within this work the data sets from 2 independent

V2V channel sounding campaigns have been analysed. The TUIL data set has been considered for the statistical

analysis of the WINNER like Large-Scale-Parameter (LSP)s, whereby for the first time a double directional anal-

ysis by means of high resolution multipath parameter estimation has been conducted and the results can be used to

parametrize the WINNER channel model. Furthermore with the TUIL data sets the local non-stationarity regions

have been studied, whereby special focus has been spent to identify the region by interacting vehicles (e.g. caus-

ing shadowing and scattering). The UOULU data sets has been analysed to derive new inside into the statistical

parameters of the multipath clusters.

Besides the deep analysis of the two V2V channel sounding campaigns the extensions of the well-known WINNER

channel model to be applicable for VANETs performance evaluation has been detailed. The model has been

extended to allow for a mixture of random and deterministic cluster generation. This is also essential for future

channel model evolution towards 5G mobile communications systems. Within the classical WINNER approach

the spatial consistency for closely co-located users/nodes has been accounted for at the large scale parameter

level and not at the cluster/multipath parameter level. Since in the V2V case many nodes are closely co-located

the spatial consistency must be modelled in a realistic way: by interpolation of cluster parameters. Furthermore

mobile scatterers/clusters (as vehicles) play an important role in particular for the V2V links, hence this has been

introduced to the WINNER model by shadowing of clusters and creation of new dynamic clusters.

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RESCUE D4.3, v1.0

Authors

Partner Name Phone/Fax/e-mail

TUIL Martin Kaske

Phone: +49 3677 69 1123

Fax: +49 3677 69 1113

EMail: [email protected]

TUIL Christian Schneider

Phone: +49 3677 69 1397

Fax: +49 3677 69 1113


TUIL Gerd Sommerkorn

EMail: [email protected] Juha Meinila

EMail: [email protected] Valtteri Tervo


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RESCUE D4.3, v1.0

Table of Contents

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

List of Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2. Analysis of V2V Ilmenau measurement campaign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 LSP Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Impact of moving cars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3. Quasi-Stationarity analysis of TUIL measurement campaign . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1 Analysis based on the Generalized Local Scattering Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Analysis based on the Correlation Matrix Distance (CMD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4. Cluster based analysis of Uoulu measurement campaign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.1 Parameter description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Parameter Estimation Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3 V2V SIMO Channel Sounding Campaign at Oulu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.4 LS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.5 V2V cluster results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5. Extension of WINNER channel model (WIM) for V2V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.1 Overview of WIM Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.2 Extension for node mobility/spatial consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.3 Extension for mobile scatterers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6. Use of Channelsounder Measurement Data for System-Level Simulations . . . . . 55

7. Summary & Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

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List of Acronyms and Abbreviations

BS Base Station

CDF Cumulative Distribution Function

DMC Dense Multipath Components

DoA Direction-of-Arrival

DoD Direction-of-Departure

ToA Time-of-Arrival

GBSCM Geometry based Stochastic Channel Model

ITS Intelligent Transportation Systems

MaxSDR maximum-signal-to-remainder-ratio

MIMO Multiple-Input Multiple-Output

SIMO Single-Input Multiple-Output

OTA Over-The-Air

RF Radio Frequency

Rx receiver

SC Specular Propagation Paths

SCME Spatial Channel Model Extended

SDR Software Defined Radio

SNR Signal-to-Noise Ratio

SPUCA Stacked Polarimetric Uniform Circular Array

Tx transmitter

VANET Vehicular Ad-hoc NETworks

V2I Vehicle-To-Infrastructure

V2V Vehicle-To-Vehicle

GLSF Generalized-Local-Scattering-Function

CMD Correlation-Matrix-Distance

CIR Channel-Impulse-Response

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pdf probability density function

LSP Large-Scale-Parameter

PDP Power-Delay-Profile

PAS Power-Angular-Spectrum

LOS Line-of-Sight

RCS Radar-cross-section

MAC Medium-Access-Control

PHY Physical Layer

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RESCUE D4.3, v1.0

1. Introduction

A detailed characterization of the wireless link is an essential prerequisite for accurate and reliable channel mod-

elling and subsequent performance evaluation. The wireless link under study within this deliverable is the V2V

use case of the RESCUE project 1. Adhoc networks in particular the class of VANET is of particular interest

and extends the traditional view of cellular communication systems. Hence the outcome of this report can be

used to conduct realistic and reliable link and system level simulations of VANETs inside the RESCUE project.

Furthermore the extended channel model will be used to drive the channel emulator inside the OTA test facility to

conduct practical V2V performance validation in a virtual electromagnetic environment representing the real-world

environment [1, 2].

The wireless channel in V2V communication is significantly different to cellular systems, since both link ends

are moving and the antennas are close to the ground level [3]. Furthermore, to be able to research and evalu-

ate advanced beamforming and MIMO techniques for V2V and Vehicle-To-Infrastructure (V2I) communication

it is necessary to allow for embedding of different antenna designs at the vehicle. Therefore basically only ap-

proaches which have the degree of freedom to allow in a flexible manner the embedding of arbitrary antenna

patterns are attractive for future research. Such approaches can be found in the group of ray tracing tools or Ge-

ometry based Stochastic Channel Model (GBSCM)s. The GBSCMs approach is in focus of this research work.

Typically GBSCM allows for modelling of multipath propagation with parameters such as delay, direction of ar-

rival and direction of departure (direction is composed of azimuth and elevation). Furthermore traditionally these

GBSCMs, e.g., WINNER model [4], are based on a cluster concept, where it is assumed that multipath components

arrive/departure in clusters, with similar properties within the considered parameter domains. Therefore reliable

and accurate clustering results are essential.

Recently, there has been discussion to extend this class of models to support V2V communication. Different

surveys on V2V propagation channels from [5, 6, 7, 8] highlight the key challenges to be met for these channels.

To overcome this problem the updates for the model framework should be done, e.g. for modelling dual mobility,

spatial consistency and modelling of mobile scatters [9], [10]. This is subject of research within this contribution.

Within this report two of only few MIMO channel sounding data sets are analysed focusing on the directional prop-

agation including the multipath cluster characteristics and stationarity effects of the V2V channels. Furthermore

first results on identifying propagation effects coming from the moving/interacting vehicles and from the fixed

surrounding buildings and other objects. Most known studies have been mainly focused on the basic propagation

parameters such as path loss, delay spread and/or stationarity parameters. Few studies have been focused on the

directional analysis of V2V propagation channels and none of those, to best of our knowledge, have been focused

on statistical analysis of cluster properties and moving objects characterization.

The results from the UOULU channel sounding campaign focusses on the statistical properties of the clusters

usable in a model. Whereby the TUIL channel sounding campaign is considered for deeper propagation analysis

to study the effect of mobile clusters and to backup the large scale parameters (’non-cluster’) from UOULU given

in [11] and [9]. The cluster results here are thus suitable to complete the standard WINNER model parameters.

1FP7 project ICT-61955 RESCUE (Links-on-the-fly Technology for Robust, Efficient and Smart Communication in Unpredictable Environ-

ments

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2. Analysis of V2V Ilmenau measurement campaign

2.1 Overview

In this chapter the results of the analysis of the V2V channel sounding measurement campaign will be presented.

The measurements were conducted in summer of 2014 on the campus of TUIL. A detailed description of the

campaign can be found in the RESCUE deliverable D4.2 [12].

During the measurements four major measurement tasks were distinguished: T1: Wide Grid, T2: Dense Grid, T3:Moving Cars and T4: Full Dynamic. The data from T1 will be used to analyse the LSPs in section 2.2. While the

data from T3 illustrates the impact of moving scatterers in section 2.3.

L1

L10

L11 L12 L13 L14

L2

L3

L4

L5

L6

L7

L8

L9

R1

R10

R11 R12 R13 R14

R2

R3

R4

R5

R6

R7

R8

R9

L1

L14

R1

R14

Figure 2.1: Schematic map including Tx (red) and Rx (green) locations in the ”T1 WideGrid” [12]

In order to improve the readability of this document a sketch of the transmitter locations in the T1 task is repeated

in Figure 2.1. As stated above further details about the measurement setup can be found in [12].

Large parts of the data analysis (e.g. concerning LSPs ) requires data from high resolution parameter estimation.

Therefore, the data was processed with the estimation framework RIMAX [13]. RIMAX basically decomposes

measured radio channels into three parts: 1) a deterministic part 2) a stochastic part 3) measurement noise. Its data

model can be summarized as follows

x = s(Θs)+d(Θs)+n (2.1)

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The deterministic part s(Θs) in principle resembles electromagnetic waves impinging on the receiver or transmitted

by the transmitter respectively that can be resolved by the estimator. The parameters of the deterministic part are

Time-of-Arrival (ToA),Direction-of-Arrival (DoA),Direction-of-Departure (DoD) (estimation of Doppler-shift was

skipped for the analysis at hand) and the complex polarimetric path weights.

θs =[τ ,ϕRx,ϑRx,ϕT x,ϑT x,γϕϕ ,γϕϑ ,γϑϕ ,γϑϑ

](2.2)

Those parameters provide an antenna independent description of the geometrical propagation conditions in the

channel.

The stochastic part of the data model d(Θs) denotes any residual energy present in the channel that cannot be

resolved by the estimator as a discrete/deterministic component. It is often called DMC which stands for ”Dense

Multipath Components”. Thereby, it refers to a very common cause of DMC, namely very closely spaced multipath

components that cannot be individually resolved. The third part of the data model n is the measurement noise

arising from the RF equipment of the channel sounder. Although it is not a part of the actual radio channel it has

to be taken into account by the parameter estimator as well.

The majority of the measurement data of the V2V campaign has been processed by RIMAX. The only task not

yet processed is T4, the fully dynamic measurements. In order to properly interpret the geometrical results it is

necessary to known the orientation of the transmitter and receiver arrays as the angular parameters are always

relative to the array’s own coordinates system (local array coordinate system). Figure 2.2 illustrates how both

antenna arrays were mounted on the respective measurement trolley. It can be seen that the Tx array is aligned

Tx Rx

Figure 2.2: Orientation of the Tx/Rx-Arrays on the trolleys.

with the handle bar of the trolley which means that 0◦ azimuth of the Tx array is equivalent to the driving direction

of the Tx trolley. The Rx array on the other hand is tilted by −135◦. In other words the driving direction of the

Rx trolley corresponds to −135◦ degrees in azimuth. Consider the case where both trolleys are located on the left

lane of the street in Figure 2.1, lets say the transmitter is at L7 and the receiver at L3. If both trolleys are oriented

such that the handle bar faces L1 the line-of-sight direction would ba at 0◦ azimuth at the transmitter and +45◦ at

the receiver as the receiver is in front of the transmitter. The azimuth angles are defined in mathematical positive

order that means counter-clockwise in the view presented in Figure 2.2.

2.2 LSP Analysis

In this section the results of the LSP analysis of the ”T1: Wide Grid” measurement data is presented. The LSPs

describe the statistical behaviour of the geometrical parameters of the channel and are in principle done in accor-

dance with the definitions that can be found in WINNER [14, 15]. From the large number of possible LSPs in

WINNER the results in this section are limited to the ”Delay-Spread” and the ”Angular-Spread” in both azimuth

and elevation at both transmitter and receiver.

The spread is calculated using the following equation, where it is not important whether the delay or any of the

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RESCUE D4.3, v1.0

angular spreads are calculated.

σ2x =

∫ xmaxxmin

(x− x)2 · p(x)dx∫ xmaxxmin

p(x)dx(2.3)

The variable x denotes the mean value defined as follows.

x =

∫ xmaxxmin

x · p(x)dx∫ xmaxxmin

p(x)dx(2.4)

In case of the delay spread the function p(x) would denote the Power-Delay-Profile (PDP) p(τ). Since RIMAX

results are used, which are discrete in the respective domain, the function p(x) is not a continuous function but

rather a series of dirac-deltas. The value of the power for a given deterministic component is obtained by calculating

the sum of the magnitude squares of the path weights.

p(xi) =∣∣γϕϕ,i

∣∣2 + ∣∣γϕϑ ,i∣∣2 + ∣∣γϑϕ,i

∣∣2 + ∣∣γϑϑ ,i∣∣2 (2.5)

Using this approach it is possible to obtain an estimate of the respective spreads without any influence of the

antenna characteristics used during measurements (antenna independent characterisation of the channel due to high

resolution parameter estimation). While this is widely done for the angular spreads in the scientific community,

it is not so common for deriving the delay spread. For the delay spread the calculation based on the bandlimited

complex channel impulse responses is commonly used.

N (−6.91,0.25)

−8.5 −8 −7.5 −7 −6.50

1

2

3

4

5

DS [log10(s)]

hist

ogra

m[%

]

Figure 2.3: Normalized histogram of time delay spread (all files).

For the calculation of the angular spreads equation 2.3 is not directly applicable. Due to the periodicity of the

angular domain it can happen that the term (x− x) exceeds +π or falls below −π . If the angles are not confined

within ±π equation 2.3 might overestimate the actual angular spread. In order to avoid this the values of (x− x)are first remapped to ±π before performing the calculations in equation 2.3.

The spreads are calculated for each measurement snapshot separately and their statistics in terms of mean and

standard deviation are determined. The large amount of snapshots is hereby subdivided into different categories

in order to analyse the LSP e.g. for a certain position of the transmitter or receiver respectively. As described

in section 5.1 within the WINNER channel model the spreads are modelled as random process and for a specific

channel simulation a certain set of LSPs is generated randomly. The statistical distribution is hereby modelled as a

log-normal distribution with scenario dependent values for mean and standard deviation. A log-normal distributed

process is defined as a random process where the logarithm of the variable is normal distributed. Therefore, the

following tables detail the linear mean and standard deviation (for illustrative purposes) as well as the log-mean and

the log-standard-deviation which are the important parameters for WINNER-like channel modelling. The analysis

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presented in this document is restricted to the area where Line-of-Sight (LOS) conditions can be found. Hence,

the Tx locations L12/R12 and above are skipped and also those snapshots from the measurement files where the

Rx is beyond this point. In other words the results presented in this document are valid for the street section of the

measurement route only.

An example is given in Figure 2.3. The plot shows the histogram in percent of the logarithm of the delay spreads

as they are obtained from all files/snapshots of the ”Wide Grid” measurement task. In addition the corresponding

fit of a normal distribution and its associated mean and standard deviation are shown. The same plots are shown

N (1.76,0.09)

1.4 1.6 1.8 2 2.20

1

2

3

4

ASA [log10(deg)]

hist

ogra

m[%

]

(a) azimuth spread

N (1.30,0.09)

0.6 0.8 1 1.2 1.4 1.60

1

2

3

4

5

ESA [log10(deg)]

hist

ogra

m[%

]

(b) elevation spread

Figure 2.4: Normalized histogram of azimuth and elevation spread of arrival (all files).

N (1.71,0.13)

1 1.2 1.4 1.6 1.8 2 2.20

1

2

3

4

5

ASD [log10(deg)]

hist

ogra

m[%

]

(a) azimuth spread

N (1.24,0.13)

0.6 0.8 1 1.2 1.4 1.60

1

2

3

4

ESD [log10(deg)]

hist

ogra

m[%

]

(b) elevation spread

Figure 2.5: Normalized histogram of azimuth and elevation spread of departure (all files)

in Figures 2.4 and 2.5 for the angular spreads at the receiver and transmitter respectively. It can be seen in these

results that the values at both sides are quite similar to each other. This is in contrast to the results usually obtained

in e.g. urban macro-cell scenarios where the spread at the transmitter (which is usually the base-station) is much

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smaller then the spreads at the receiver (mobile station). This result is somehow expected since in case of a V2V

scenario both nodes are at similar heights and in similar propagation conditions in terms of surrounding buildings

etc.

In V2V scenarios the most obvious difference to e.g. urban macro-cell scenarios is that both nodes (transmitter

and receiver) are mobile. Therefore, it is interesting to analyse the impact that e.g. the transmitter location has on

the different LSPs. In Table 2.1 the statistical values of the distribution of the delay spread are shown. This table

contains one row per measurement file and therefore each row denotes one Tx location (as depicted in Figure 2.1)

while the Rx is moved along the opposing side of the street. The name of the files can be read in the following

fashion: the first part denotes the Tx location and the second part the route of the receiver. So ”R10 L14-L1”

means that the Tx was on location ”R10” and the Rx was moving from ”L14” to ”L1”. Please note again that

only the data up to L/R11 was used but out of convenience the labelling of the files (up to L/R14) was kept for the

tables. It can be observed that the delay spread is fairly constant for all files ranging from −7.14 to −6.75 which

corresponds to ≈ 72ns and ≈ 177ns. However, it is noticeable that there seems to be a slight decrease of the delay

spread towards the L11 and R11 Tx locations. In this direction the transmitter is moving closer towards the open

area at the end of the street section.

The measurement campaign was designed such that the same route of the receiver was measured for different

transmitter locations. This creates the unique opportunity to virtually move the Tx along a trajectory while the Rx

is stationary. In order to do this we can take those snapshots from all measurement files where the Rx was at a

specific position and thus placing the Tx at any of the 28 Tx locations. However, this would result in only up to 28

snapshots if the Rx is kept at one unique location. To increase the number of snapshots the receiver was allowed to

be within a range ±0.5m around a specific transmitter location. The results for this approach are shown in Table

2.2 for the delay spread. The first row for example shows the results where the Rx was located in the vicinity of

Tx location R1 and the Tx was at any arbitrary location on the left side of the street. Compared to the ”moving

Rx” results the number of snapshots available was only about one tenth on average. Therefore, the results are not

absolutely comparable. The number of snapshots used is denoted in the sixth column labelled Nsurvey[%]. It can

be observed from Table 2.2 that the same behaviour as in Table 2.1 occurs. As the receiver is moving towards the

open area of the street the delay spread decreases, however, it is not as pronounced. The Tables 2.3,2.4,2.5 and

2.6 depict the LSP results for the azimuth and elevation spread of arrival i.e. the angular spread at the receiver

side. Again results are shown for both cases where either the transmitter or receiver is stationary. What can be

observed from this results is that again there seems to be a trend of change towards the open area of the street.

However, it is only noticeable when the Rx is kept at a fixed location and the Tx is virtually moved (Table 2.4).

The azimuth spread at the receiver appears to be smallest when the Rx is in the middle of the street and increases

towards the end of the street section. If the transmitter is fixed and the receiver is moving (Table 2.3) the spread is

not really constant but it seems there is no real correlation between Tx location and spread. Tables 2.7,2.8,2.9 and

2.10 present the results for the angular spreads at the transmitter side. It can be seen that overall (averaged over all

files) the angular spreads of the receiver and transmitter side are comparable. This outcome can be expected since

in a V2V scenario both nodes (receiver and transmitter) experience similar environments (in terms of surrounding

buildings etc.) and are placed at similar heights. Furthermore, the angular spreads at the transmitter show the same

behaviour as the spreads at the receiver in terms of dependency on the Tx location. If the Tx is kept fixed and

the Rx is moving the azimuthal spread (Table 2.7) increases towards the open area. It seems that this feature can

only be noticed where the receiver/transmitter is fixed and the LSPs are analysed at different locations. In other

words, the azimuthal spread at the receiver shows this behaviour in Table 2.4 where the receiver is virtually fixed

at certain locations and the azimuthal spread shows this in Table 2.7 where the transmitter is fixed. An explanation

for this observation could be that there is a dependency on the location of both the angular spreads at the receiver

and transmitter side. However, due to the averaging over multiple receiver or transmitter locations in either the

case of fixed transmitter or receiver locations the dependency is not visible any more. It is only revealed when

the respective receiver or transmitter location is fixed. Furthermore, it can be concluded that similar values of the

statistics of the LSPs can be found for the receiver and transmitter side. In addition to that the results show that the

receiver and transmitter locations can be switched (Rx placed at Tx locations and Tx is ”virtually” moved and vice

versa) and similar results are obtained. This supports the, already known assumption of reciprocity of the radio

channel.

(60)

RESCUE D4.3, v1.0

set μDS[s] σDS[s] μDS[log10(s)] σDS[log10(s)] Nsurvey[%]

L10 R1-R14 1 ·10−7 5.17 ·10−8 −7.09 0.31 4.42

L11 R1-R14 7.77 ·10−8 2.96 ·10−8 −7.14 0.18 4.42

L1 R1-R14 1.94 ·10−7 7.97 ·10−8 −6.75 0.2 4.42

L2 R1-R14 1.85 ·10−7 6.28 ·10−8 −6.76 0.17 4.42

L3 R1-R14 1.45 ·10−7 5.25 ·10−8 −6.87 0.15 4.42

L4 R1-R14 1.37 ·10−7 5 ·10−8 −6.89 0.16 4.42

L5 R1-R14 1.14 ·10−7 4.09 ·10−8 −6.97 0.16 4.42

L6 R1-R14 1.16 ·10−7 5.18 ·10−8 −6.98 0.2 4.43

L7 R1-R14 1.31 ·10−7 7.19 ·10−8 −6.97 0.32 4.43

L8 R1-R14 1.25 ·10−7 6.34 ·10−8 −6.97 0.26 4.42

L9 R1-R14 1.08 ·10−7 6.03 ·10−8 −7.06 0.31 4.42

R10 L14-L1 1.39 ·10−7 6.05 ·10−8 −6.92 0.26 4.68

R11 L14-L1 1.12 ·10−7 5.5 ·10−8 −7.05 0.34 4.66

R1 L14-L1 1.47 ·10−7 2.89 ·10−8 −6.84 9.18 ·10−2 4.66

R2 L14-L1 1.48 ·10−7 3.65 ·10−8 −6.84 0.12 4.65

R3 L14-L1 1.71 ·10−7 7.22 ·10−8 −6.81 0.2 4.67

R4 L14-L1 1.72 ·10−7 4.78 ·10−8 −6.78 0.13 4.67

R5 L14-L1 1.54 ·10−7 6.2 ·10−8 −6.85 0.17 4.67

R6 L14-L1 1.56 ·10−7 7.24 ·10−8 −6.87 0.26 4.67

R7 L14-L1 1.69 ·10−7 7.78 ·10−8 −6.82 0.2 4.68

R8 L14-L1 1.54 ·10−7 7.78 ·10−8 −6.87 0.23 4.67

R9 L14-L1 1.51 ·10−7 7.43 ·10−8 −6.9 0.28 4.67

all files 1.42 ·10−7 6.63 ·10−8 −6.91 0.25 100

Table 2.1: time delay spread statistics (fixed Tx ⇒ Rx track)

set μDS[s] σDS[s] μDS[log10(s)] σDS[log10(s)] Nsurvey[%]

Rx@TxPos:R1 1.29 ·10−7 6.04 ·10−8 −6.94 0.22 0.21

Rx@TxPos:R2 1.34 ·10−7 4.15 ·10−8 −6.9 0.15 0.48

Rx@TxPos:L2 2.14 ·10−7 7.26 ·10−8 −6.7 0.16 0.48

Rx@TxPos:R3 1.61 ·10−7 7.39 ·10−8 −6.84 0.2 0.48

Rx@TxPos:L3 1.8 ·10−7 5.38 ·10−8 −6.77 0.14 0.48

Rx@TxPos:R4 1.58 ·10−7 7.21 ·10−8 −6.84 0.18 0.48

Rx@TxPos:L4 1.71 ·10−7 4.68 ·10−8 −6.78 0.12 0.48

Rx@TxPos:R5 1.36 ·10−7 6.57 ·10−8 −6.91 0.2 0.47

Rx@TxPos:L5 1.53 ·10−7 5.54 ·10−8 −6.84 0.17 0.48

Rx@TxPos:R6 1.32 ·10−7 4.9 ·10−8 −6.91 0.17 0.48

Rx@TxPos:L6 1.58 ·10−7 5.56 ·10−8 −6.84 0.22 0.48

Rx@TxPos:R7 1.38 ·10−7 6.69 ·10−8 −6.92 0.23 0.48

Rx@TxPos:L7 1.61 ·10−7 5.87 ·10−8 −6.82 0.15 0.48

Rx@TxPos:R8 1.39 ·10−7 6.71 ·10−8 −6.91 0.22 0.48

Rx@TxPos:L8 1.53 ·10−7 3.71 ·10−8 −6.83 0.11 0.47

Rx@TxPos:R9 1.3 ·10−7 7.47 ·10−8 −6.98 0.29 0.48

Rx@TxPos:L9 1.36 ·10−7 5.76 ·10−8 −6.92 0.23 0.48

Rx@TxPos:R10 1.14 ·10−7 6.15 ·10−8 −7.04 0.33 0.48

Rx@TxPos:L10 1.21 ·10−7 6 ·10−8 −7 0.3 0.48

Rx@allTxPos 1.49 ·10−7 6.46 ·10−8 −6.88 0.22 8.83

Table 2.2: time delay spread statistics (”fixed” Rx ⇒ Tx ”track”)

(60)

RESCUE D4.3, v1.0

set μASA[deg] σASA[deg] μASA[log10(deg)] σASA[log10(deg)] Nsurvey[%]

L10 R1-R14 55.61 8.3 1.74 6.57 ·10−2 4.42

L11 R1-R14 54.62 7.54 1.73 6.5 ·10−2 4.42

L1 R1-R14 63.59 11.02 1.8 7.56 ·10−2 4.42

L2 R1-R14 65.07 16.94 1.8 0.12 4.42

L3 R1-R14 59.77 11.81 1.77 8.4 ·10−2 4.42

L4 R1-R14 63.98 14.38 1.8 9.33 ·10−2 4.42

L5 R1-R14 58.32 9.35 1.76 6.56 ·10−2 4.42

L6 R1-R14 60.36 9.69 1.78 6.58 ·10−2 4.43

L7 R1-R14 60.02 11.79 1.77 8.54 ·10−2 4.43

L8 R1-R14 55.06 10.54 1.73 7.92 ·10−2 4.42

L9 R1-R14 55.1 8.45 1.74 6.96 ·10−2 4.42

R10 L14-L1 56.59 8.33 1.75 6.36 ·10−2 4.68

R11 L14-L1 58.5 8.24 1.76 6.2 ·10−2 4.66

R1 L14-L1 60.76 9.11 1.78 6.64 ·10−2 4.66

R2 L14-L1 57.94 9.75 1.76 7.98 ·10−2 4.65

R3 L14-L1 69.72 15.09 1.83 9.56 ·10−2 4.67

R4 L14-L1 64.07 14.83 1.79 0.1 4.67

R5 L14-L1 59.62 20.45 1.75 0.15 4.67

R6 L14-L1 62.65 16.09 1.78 0.1 4.67

R7 L14-L1 54.15 14.13 1.72 0.12 4.68

R8 L14-L1 54.21 9.14 1.73 7.64 ·10−2 4.67

R9 L14-L1 53.7 9.06 1.72 7.21 ·10−2 4.67

all files 59.25 12.77 1.76 9.19 ·10−2 100

Table 2.3: azimuth spread of arrival statistics (fixed Tx ⇒ Rx track)

set μASA[deg] σASA[deg] μASA[log10(deg)] σASA[log10(deg)] Nsurvey[%]

Rx@TxPos:R1 61.02 12.86 1.77 0.11 0.21

Rx@TxPos:R2 56.22 6.64 1.75 5.19 ·10−2 0.48

Rx@TxPos:L2 45.45 7.91 1.65 7.84 ·10−2 0.48

Rx@TxPos:R3 56.87 8.53 1.75 6.46 ·10−2 0.48

Rx@TxPos:L3 48.12 8.49 1.67 8.19 ·10−2 0.48

Rx@TxPos:R4 54.47 9.36 1.73 7.29 ·10−2 0.48

Rx@TxPos:L4 52.35 11.39 1.71 9.62 ·10−2 0.48

Rx@TxPos:R5 52.77 7.19 1.72 5.84 ·10−2 0.47

Rx@TxPos:L5 54.71 9.4 1.73 7.68 ·10−2 0.48

Rx@TxPos:R6 55.48 7.92 1.74 6.16 ·10−2 0.48

Rx@TxPos:L6 59.34 12.59 1.76 9.79 ·10−2 0.48

Rx@TxPos:R7 56.65 12.82 1.74 8.96 ·10−2 0.48

Rx@TxPos:L7 60.34 13.64 1.77 8.49 ·10−2 0.48

Rx@TxPos:R8 62.47 12.26 1.79 8.27 ·10−2 0.48

Rx@TxPos:L8 60.11 8.11 1.78 5.59 ·10−2 0.47

Rx@TxPos:R9 64.84 11.87 1.8 7.84 ·10−2 0.48

Rx@TxPos:L9 66.23 11.95 1.81 7.85 ·10−2 0.48

Rx@TxPos:R10 66.42 13.04 1.81 8.5 ·10−2 0.48

Rx@TxPos:L10 70.72 17.59 1.84 0.11 0.48

Rx@allTxPos 58.06 12.67 1.75 9.25 ·10−2 8.83

Table 2.4: azimuth spread of arrival statistics (”fixed” Rx ⇒ Tx ”track”)

(60)

RESCUE D4.3, v1.0

set μESA[deg] σESA[deg] μESA[log10(deg)] σESA[log10(deg)] Nsurvey[%]

L10 R1-R14 19.98 3.24 1.29 7.12 ·10−2 4.42

L11 R1-R14 19.92 3.29 1.29 6.96 ·10−2 4.42

L1 R1-R14 23.04 5.95 1.35 0.12 4.42

L2 R1-R14 18.5 3.5 1.26 8.85 ·10−2 4.42

L3 R1-R14 18.36 3.34 1.26 7.75 ·10−2 4.42

L4 R1-R14 20.59 5.22 1.3 0.11 4.42

L5 R1-R14 19.18 3.76 1.27 8.49 ·10−2 4.42

L6 R1-R14 19.94 3.38 1.29 7.6 ·10−2 4.43

L7 R1-R14 20.4 4.24 1.3 9.31 ·10−2 4.43

L8 R1-R14 21.18 3.83 1.32 7.77 ·10−2 4.42

L9 R1-R14 21.61 3.58 1.33 7.12 ·10−2 4.42

R10 L14-L1 21.57 3.52 1.33 7.08 ·10−2 4.68

R11 L14-L1 22.29 2.71 1.34 5.33 ·10−2 4.66

R1 L14-L1 21.31 4.05 1.32 8.87 ·10−2 4.66

R2 L14-L1 22.39 4.5 1.34 9.31 ·10−2 4.65

R3 L14-L1 19.96 3.16 1.29 6.87 ·10−2 4.67

R4 L14-L1 19.49 4.03 1.28 0.11 4.67

R5 L14-L1 19.91 4.56 1.29 0.1 4.67

R6 L14-L1 19.94 3.59 1.29 7.88 ·10−2 4.67

R7 L14-L1 18.97 3.74 1.27 8.74 ·10−2 4.68

R8 L14-L1 20.22 4.41 1.3 9.51 ·10−2 4.67

R9 L14-L1 21.07 3.09 1.32 6.35 ·10−2 4.67

all files 20.45 4.1 1.3 8.91 ·10−2 100

Table 2.5: elevation spread of arrival statistics (fixed Tx ⇒ Rx track)

set μESA[deg] σESA[deg] μESA[log10(deg)] σESA[log10(deg)] Nsurvey[%]

Rx@TxPos:R1 22.34 4.78 1.34 0.11 0.21

Rx@TxPos:R2 18.7 2.6 1.27 6.38 ·10−2 0.48

Rx@TxPos:L2 20.49 4.14 1.3 8.02 ·10−2 0.48

Rx@TxPos:R3 20.46 2.75 1.31 5.98 ·10−2 0.48

Rx@TxPos:L3 19.83 4.18 1.29 9.52 ·10−2 0.48

Rx@TxPos:R4 21.13 4.42 1.32 8.73 ·10−2 0.48

Rx@TxPos:L4 20.87 4.27 1.31 9.8 ·10−2 0.48

Rx@TxPos:R5 19.9 4.5 1.29 0.1 0.47

Rx@TxPos:L5 20.07 4.19 1.29 9.25 ·10−2 0.48

Rx@TxPos:R6 21.32 4.13 1.32 7.94 ·10−2 0.48

Rx@TxPos:L6 21.39 5.03 1.32 0.1 0.48

Rx@TxPos:R7 20.08 5.6 1.28 0.13 0.48

Rx@TxPos:L7 21.67 3.83 1.33 7.6 ·10−2 0.48

Rx@TxPos:R8 22.16 7.1 1.32 0.13 0.48

Rx@TxPos:L8 19.59 2.91 1.29 6.39 ·10−2 0.47

Rx@TxPos:R9 20.28 4.22 1.3 8.73 ·10−2 0.48

Rx@TxPos:L9 21.45 3.6 1.33 7.14 ·10−2 0.48

Rx@TxPos:R10 18.93 3.59 1.27 8.66 ·10−2 0.48

Rx@TxPos:L10 20.15 3.21 1.3 6.66 ·10−2 0.48

Rx@allTxPos 20.52 4.36 1.3 9.23 ·10−2 8.83

Table 2.6: elevation spread of arrival statistics (”fixed” Rx ⇒ Tx ”track”)

(60)

RESCUE D4.3, v1.0

set μASD[deg] σASD[deg] μASD[log10(deg)] σASD[log10(deg)] Nsurvey[%]

L10 R1-R14 64.41 11.51 1.8 7.52 ·10−2 4.42

L11 R1-R14 56.71 8.1 1.75 5.93 ·10−2 4.42

L1 R1-R14 42.81 8.27 1.62 8.56 ·10−2 4.42

L2 R1-R14 36.24 13.71 1.53 0.16 4.42

L3 R1-R14 44.05 9.87 1.63 0.1 4.42

L4 R1-R14 46.76 11.47 1.66 0.11 4.42

L5 R1-R14 52.46 8.49 1.71 7.3 ·10−2 4.42

L6 R1-R14 51.76 8.33 1.71 7.03 ·10−2 4.43

L7 R1-R14 54.53 15.19 1.72 0.12 4.43

L8 R1-R14 58.4 11.21 1.76 8.92 ·10−2 4.42

L9 R1-R14 61.11 10.38 1.78 7.41 ·10−2 4.42

R10 L14-L1 70.7 13.61 1.84 8.92 ·10−2 4.68

R11 L14-L1 72.66 11.49 1.86 6.72 ·10−2 4.66

R1 L14-L1 40.6 7.2 1.6 7.83 ·10−2 4.66

R2 L14-L1 42.24 5.65 1.62 5.92 ·10−2 4.65

R3 L14-L1 48.03 8.93 1.67 7.92 ·10−2 4.67

R4 L14-L1 45.19 11.89 1.64 0.12 4.67

R5 L14-L1 49.92 15.76 1.68 0.14 4.67

R6 L14-L1 53.48 11.53 1.72 0.11 4.67

R7 L14-L1 60.06 16.69 1.76 0.11 4.68

R8 L14-L1 63.76 16.42 1.79 0.11 4.67

R9 L14-L1 68.05 15.52 1.82 0.1 4.67

all files 53.87 15.52 1.71 0.13 100

Table 2.7: azimuth spread of departure statistics (fixed Tx ⇒ Rx track)

set μASD[deg] σASD[deg] μASD[log10(deg)] σASD[log10(deg)] Nsurvey[%]

Rx@TxPos:R1 54.21 11.96 1.72 0.12 0.21

Rx@TxPos:R2 55.28 11.18 1.73 9.02 ·10−2 0.48

Rx@TxPos:L2 66.84 18.08 1.81 0.13 0.48

Rx@TxPos:R3 59.8 16.25 1.76 0.13 0.48

Rx@TxPos:L3 64.61 16.53 1.8 0.11 0.48

Rx@TxPos:R4 51.2 13.13 1.69 0.13 0.48

Rx@TxPos:L4 61.27 16.09 1.77 0.11 0.48

Rx@TxPos:R5 50.04 15.93 1.68 0.14 0.47

Rx@TxPos:L5 54.71 11.91 1.73 9.23 ·10−2 0.48

Rx@TxPos:R6 53.02 17.76 1.7 0.16 0.48

Rx@TxPos:L6 57.37 17.59 1.74 0.14 0.48

Rx@TxPos:R7 48.64 10.85 1.68 0.1 0.48

Rx@TxPos:L7 52.29 16.29 1.7 0.13 0.48

Rx@TxPos:R8 46.63 11.91 1.65 0.12 0.48

Rx@TxPos:L8 47.37 12.22 1.66 0.1 0.47

Rx@TxPos:R9 45.34 12.44 1.64 0.13 0.48

Rx@TxPos:L9 47.25 15.07 1.65 0.15 0.48

Rx@TxPos:R10 47.81 10.58 1.67 0.11 0.48

Rx@TxPos:L10 47.9 11.48 1.67 0.11 0.48

Rx@allTxPos 53.21 15.63 1.71 0.13 8.83

Table 2.8: azimuth spread of departure statistics (”fixed” Rx ⇒ Tx ”track”)

(60)

RESCUE D4.3, v1.0

set μESD[deg] σESD[deg] μESD[log10(deg)] σESD[log10(deg)] Nsurvey[%]

L10 R1-R14 17.15 5.04 1.22 0.13 4.42

L11 R1-R14 16.52 4.62 1.2 0.13 4.42

L1 R1-R14 21.5 4.19 1.32 8.93 ·10−2 4.42

L2 R1-R14 16.24 3.87 1.2 0.11 4.42

L3 R1-R14 21.73 5.9 1.32 0.12 4.42

L4 R1-R14 18.6 3.48 1.26 8.42 ·10−2 4.42

L5 R1-R14 17.9 3.89 1.24 9.59 ·10−2 4.42

L6 R1-R14 16.65 4.93 1.2 0.14 4.43

L7 R1-R14 17.75 4.76 1.23 0.12 4.43

L8 R1-R14 17.05 4.8 1.21 0.13 4.42

L9 R1-R14 17.16 4.45 1.22 0.12 4.42

R10 L14-L1 18.7 3.39 1.26 8.19 ·10−2 4.68

R11 L14-L1 22.73 4.08 1.35 7.99 ·10−2 4.66

R1 L14-L1 17.58 5.99 1.21 0.18 4.66

R2 L14-L1 18.93 4.77 1.26 0.11 4.65

R3 L14-L1 15.18 5.58 1.16 0.15 4.67

R4 L14-L1 14.14 3.95 1.13 0.13 4.67

R5 L14-L1 17.42 5.8 1.21 0.16 4.67

R6 L14-L1 17.29 4.7 1.22 0.13 4.67

R7 L14-L1 16.7 4.09 1.21 0.11 4.68

R8 L14-L1 18.99 4.5 1.27 0.11 4.67

R9 L14-L1 18.5 3.19 1.26 7.68 ·10−2 4.67

all files 17.93 5.02 1.24 0.13 100

Table 2.9: elevation spread of departure statistics (fixed Tx ⇒ Rx track)

set μESD[deg] σESD[deg] μESD[log10(deg)] σESD[log10(deg)] Nsurvey[%]

Rx@TxPos:R1 14.83 4.78 1.14 0.16 0.21

Rx@TxPos:R2 16.39 6.37 1.19 0.15 0.48

Rx@TxPos:L2 20.5 4.36 1.3 9.91 ·10−2 0.48

Rx@TxPos:R3 17.4 4.95 1.22 0.14 0.48

Rx@TxPos:L3 18.63 4.23 1.26 9.81 ·10−2 0.48

Rx@TxPos:R4 17.14 4.59 1.22 0.12 0.48

Rx@TxPos:L4 20.28 3.76 1.3 8.16 ·10−2 0.48

Rx@TxPos:R5 15.74 4.36 1.18 0.12 0.47

Rx@TxPos:L5 17.18 4.34 1.22 0.11 0.48

Rx@TxPos:R6 15.5 4.44 1.17 0.13 0.48

Rx@TxPos:L6 16.41 5.56 1.18 0.18 0.48

Rx@TxPos:R7 19.15 3.93 1.27 8.7 ·10−2 0.48

Rx@TxPos:L7 17.01 4.63 1.21 0.12 0.48

Rx@TxPos:R8 18.02 5.49 1.23 0.14 0.48

Rx@TxPos:L8 16.39 4.99 1.19 0.14 0.47

Rx@TxPos:R9 20.68 3.71 1.31 8.73 ·10−2 0.48

Rx@TxPos:L9 18.29 5.21 1.24 0.13 0.48

Rx@TxPos:R10 20.99 4.9 1.31 0.1 0.48

Rx@TxPos:L10 16.29 4.41 1.2 0.12 0.48

Rx@allTxPos 17.82 5.04 1.23 0.13 8.83

Table 2.10: elevation spread of departure statistics (”fixed” Rx ⇒ Tx ”track”)

(60)

RESCUE D4.3, v1.0

2.3 Impact of moving cars

In the previous section the analysis of the LSPs for the ”Wide Grid” measurement task were shown. In this

task there were no mobile scatterers (additional vehicles other than receiver and transmitter) as the purpose was

to analyse the impact of dual node mobility on an otherwise static/stationary channel. In the ”Moving Cars”

task both nodes were fixed at various locations and two different type of cars (sedan and pick-up truck) moved

along the street. The aim of this task is to give insight to the impact of additional vehicles on the channel. The

measurement data was processed using the RIMAX estimation framework and exemplary results are presented in

the following. A complete list of available measurement setups is given in [12]. In this section the results for the

”L7 L3 PickupTruck forward” case are shown. In this case the transmitter was located at position L7 while the

receiver was at L3 (see Figure 2.1). The transmitter was oriented such that its handle bar faces towards the ”L11”

position while the receiver handle bar faces L1. Therefore, it can be expected that the LOS direction is at about

+45◦ azimuth at the receiver and about −180◦ azimuth at the transmitter side. The pick-up truck was moving

”forward” which means from R1 towards R11. The other movement option of the cars was ”backward” which is

from L11 towards L1. During the ”backward” direction the truck is moving on the same lane where receiver and

transmitter are situated. Therefore, there will be a section where the truck is in between receiver and transmitter

with the potential of blocking part of the link between the two. In case of the ”forward” movement this however

SnapshotNo0 500 1000 1500 2000 2500

pow

er [d

B]

-54.5

-54

-53.5

-53

-52.5

-52

-51.5

-51

(a) L7 L3 PickupTruck ”forward”

SnapshotNo0 500 1000 1500 2000 2500

pow

er [d

B]

-54.5

-54

-53.5

-53

-52.5

-52

-51.5

-51

(b) L7 L3 PickupTruck ”backward”

Figure 2.6: Total power of measurement data for ”forward” and ”backward” driving direction of the pick-up truck. In case of ”backward” it can be seen how the power drops while the truck is movingin between receiver and transmitter.

will not be the case as the truck is basically just passing by the two nodes. There on the other hand the truck

might act as an additional scatterer. Figure 2.6 shows the total power (of the measurement data) for the two cases

of ”forward” and ”backward” movement of the truck while Rx and Tx are located at L3 and L7 respectively. It

can be observed that in case of ”backward” movement the total power stays constant until approximately snapshot

1000. This is where the truck is close to the transmitter and starts overtaking it. Until snapshot 2000 the truck is

between the two nodes. It can be seen that the power drops during that time, however, not too strong which might

be caused by incomplete blockage of the LOS since the truck is not really in between the two. Another reason

could be that the power of the LOS is diffracted by the truck but still the energy is transferred from transmitter

to receiver. Another interesting aspect is the slight oscillation of the power the occurs right before the power

drop. This might be caused by interference of the multipath created by the approaching truck with the rest of the

multipath components. The other part of Figure 2.6 shows the total power when the truck is moving ”forward”.

Here the power stays relatively constant over time with a slight increase while the truck is passing by the nodes.

This might be an indication of the truck acting as an additional scatterer that causes more energy to be transferred

from transmitter to receiver.

Figure 2.7 shows the RIMAX results for the ”forward” movement direction of the truck. Each coloured dot

(60)

RESCUE D4.3, v1.0

depicts an estimated discrete component while the colour itself denotes the power of the component. The data has

been filtered in delay and angle in such a way that only those parts where the contribution of the truck appears

are displayed. In the delay domain it can be seen that most parts are static as they represent the surrounding

Del

ay

0.05

0.1

0.15

0.2

SnapshotNo0 500 1000 1500 2000 2500

L7_L3_PickupTruck_forward

Figure 2.7: L7 L3 PickupTruck Forward Delay Time

environment which stays fixed since neither receiver nor transmitter is moving. At around snapshot 1200 however

a path (or cluster of paths) can be observed that moves further away over time (snapshot number). In the same

manner a path is approaching in the beginning of the file, however, this is not as clear since it is masked by the

static paths. This moving path/cluster is most likely the truck which indeed represents a mobile scatterer in this

scenario. Figure 2.8 depicts the estimation result for the azimuth angle at the receiver over time/snapshot. Starting

from snapshot 400 it can be seen that paths are moving from ≈−100◦ azimuth towards ≈+45◦. The truck starts

from position R1 which would correspond to an azimuth angle of arrival of about −100◦. As the truck is passing by

the receiver the corresponding angle would decrease an eventually approach an angle close to the LOS direction

which is at +45◦ azimuth. It is therefore reasonable to assume that the moving path in Figure 2.8 shows the

pick-up truck. Finally the angular results for the transmitter side are showin in Figure 2.9. In a similar manner

moving paths can be observed. This time however much later after snapshot 1000. This is supposed to be the time

instance where the truck is passing by the transmitter and is approaching the R11 position. The angles are again

in accordance with geometrical considerations. The starting point of the truck is somewhere close to the LOS

direction as seen from the transmitter which is at ±180◦ azimuth. As the truck moves it can be expected that its

azimuth angle at the transmitter changes towards 0◦ which is the direction of L11/R11. This is shown is Figure 2.9.

Another interesting feature is the disappearing of paths at ≈ −50◦ azimuth at around snapshot 800. This seems

to be happening when the truck is more or less in between receiver and transmitter. This might indicate that the

truck is shadowing another scatterer and after the truck passed the transmitter the scatterer is visible again. This

supports the idea that a mobile scatterer such as the truck is not only acting as an additional source of multipath

but also shadows multipath coming from the surrounding environment.

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RxA

zim

uth

-150

-100

-50

0

50

100

SnapshotNo0 500 1000 1500 2000 2500


Figure 2.8: L7 L3 PickupTruck Forward RxAzimuth Time

TxA

zim

uth

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

SnapshotNo0 500 1000 1500 2000 2500


Figure 2.9: L7 L3 PickupTruck Forward TxAzimuth Time

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RESCUE D4.3, v1.0

3. Quasi-Stationarity analysis of TUIL measurement campaign

This chapter investigates the staionarity of the measured channel based on the recorded complex channel impulse

responses. In general it is important for the performance of a wireless transmission system how fast (in terms of

e.g. distance) a channel changes or how similar the channel is at adjacent positions. The analysis is done using the

Generalized-Local-Scattering-Function (GLSF) and the Correlation-Matrix-Distance (CMD).

3.1 Analysis based on the Generalized Local Scattering Function

From [16], the LSF is defined for non-stationary channels in continuous case as

CH(t, f ;τ,ν) =∫ ∞

−∞

∫ ∞

−∞Rh(t,τ;Δt,Δτ)

×e− j2π(νΔt+ f Δτ)dΔtdΔτ(3.1)

with

Rh(t,τ;Δt,Δτ) = E[h(t,τ +Δτ)h∗(t −Δt,τ)] (3.2)

is the 4-dimension covariance function and E{.} represents mathematical expectation operator.

The DU channel as defined in [16], introduces dispersion and correlation underspread property of non-WSSUS

channel. Channel is said to be dispersion underspread if maximum delay-doppler product τmaxνmax � 1. For

correlation underspread channel, ΔτmaxΔνmaxτmaxνmax

� 1 where Δτmax and Δνmax is maximum correlation in delay and

doppler domain.

The LSF defined above suffers from some drawback, e.g, it may not be always positive. To overcome this

shortcomings of LSF, generalized local scattering functions (GLSFs) is defined, which is smooth version of LSF

[16] given as [∗n refers to n dimension convolution]

C(φ)H (t, f ;τ,ν)� (CH ∗4 Φ)(t, f ;ν ,τ) (3.3)

where Φk described as

Φ(t, f ;ν ,τ) =K

∑k=1

γk

∫ ∞

−∞

∫ ∞

−∞L∗

Gk(−t,− f +� f )

×LGk(−t −�t,− f )e− j2π(ν�t−τ� f )d�td� f

(3.4)

with LGk(t, f ) is the transfer function in time-frequency domain,γk being normalizing constants defined as

∑Kk=1 γk = 1 and K being the number of used windows.

The GLSF thus obtained is non-negative and real-valued, and is interpreted an expected multi window spectrogram

[16]. We use discrete representation of GLSF, as the channel sounding is done in discrete time-frequency instants.

Thus GLSF for discrete case is represented as [17]:

C(Φ)H [m,q; p,n] =

K−1

∑k=0

γkE{|H(Gk)[m,q; p,n]|2} (3.5)

where

H(Gk)[m,q; p,n] =√

TsFs

Nt/2−1

∑m′=−�Nt/2

Nf /2−1

∑q′=�Nf /2

L∗Gk[m′,q′]

×LH [m+m′,q+q′]e− j2π

(pm′Nt

− nq′Nf

) (3.6)

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LH [m,q], LGk [m,q], m, p, q and n representation of continuous to discrete case, Nt and Nf are time and frequency

windows length, Ts and Fs are difference in successive samples in time and frequency. We omit frequency depen-

dency on LSF and considered only time dependence in our estimation.

For evaluating the staionarity time, first collinearity of GLSF is estimated between different time instants. It is

based upon inner product in Euclidean space. The value of collinearity is bounded between α[m,m′] ∈ [0,1],with 1 meaning maximum correlation between different LSF instances and 0 corresponding to no correlation.

Collinearity of GLSF is computed as [17]:

α[m,m′] =tr{

C(Φ)H

H [m,q]C(Φ)H [m′,q]

}

||C(φ)H [m,q]||F ||C(φ)

H [m′,q]||F(3.7)

where tr stand for trace operation and ||.||F refers to frobenius norm.

Then we define threshold for collinearity using Indicator function as [17]:

Mcol [m]� {m′ |α[m,m′]> colth}. (3.8)

with colth is the collinearity threshold. And the corresponding stationarity time is obtained as [17]:

Tstat, j[m]� |Cj[m]|Ts (3.9)

with |Cj[m]| is the connected subset of Mk with maximum cardinality and Ts is the spacing in the time samples.

Before estimating GLSF and its correlation function we perform validation of DU assumption which is given as

ΔτmaxΔνmax � τmaxνmax � 1. We perform a rough estimate of stationarity region for wide grid static measure-

ment scenario [18]. In similar lines estimation of stationarity time for moving cars measurement scenario can be

calculated. Maximum doppler shift is fmax = vmax. fc/c ≈ 10.12 Hz, where vmax ≈ 1.2 m/s is the speed of Rx

measurement trolley, fc is the central frequency and c is the speed of light in vacuum. Correspondingly minimal

coherence time Tc � 1/ fmax = 98 ms. With maximum delay of τmax ≈ 3.2μs we obtain minimal coherence band-

width of Bc � 1/τmax = 312.5 kHz. Thus we obtain τmax fmax ≈ 3.24 ·10−5 � 1, fulfilling dispersion underspread

property of a channel.

For estimating minimum stationarity time we first calculate minimum stationarity length Lmin, taking λ to be 4 we

get Lmin ≈ 4λ = 4c/ fc = 0.47 m which close enough to distance of 0.38 m for 10 snapshots. The corresponding

rough estimate of minimal stationarity time is Tmin � 1/Δνmax = Lmin/vmax ≈ 0.40 s. From [16] minimal station-

arity in frequency Fmin can be estimated as Fmin � 1/Δτmax ≈ c/hmax ≈ 15 MHz where hmax is the maximum size

of an object ≈ 20 m, assuming scattering to be from same physical object. Thus ΔτmaxΔνmax ≈ 1.67 ·10−7, which

satisfies correlation underspread property of the channel. Thus DU assumption is fulfilled.

For estimation of GLSF we use separation of time and frequency windows. The windows are created using discrete

prolate spheroidal sequence (DPSS) [19]. The window length of 10 and 128 is used in time and frequency domain

respectively. Number of times window is repeated is limited to 2 in either domain, i.e, I = J = 2. We used sliding

window mechanism for estimation of GLSF. The window was shifted by 1 snapshot after processing segment of

dimensions 128 (frequency bins) x 10 (snapshots). Thus in total 3117 segments were obtained for wide grid static

measurement and 2160 segments for moving cars measurement scenario

For calculation of collinearity, we stacked all delay x doppler elements obtained from GLSF estimation in vector

form and then we calculated the correlation between them for different time instants[20]. For estimating station-

arity time we used threshold of 0.9 for collinearity of GLSF colth. Stationarity time was calculated by taking

summation over all time instants for which the indicator function was 1, i.e, time instants where the collinearity

exceeded the defined threshold.

In this analysis, out of 32x32 multiple input multiple output (MIMO) antenna configuration we considered only

4x4 MIMO setup for upper ring at Tx and Rx in both the scenarios. The selected directional antenna elements

were radiating in front, back, left and right with respect to driving direction for both Tx and Rx measurement

trolley. For the estimation of GLSF and stationarity time we considered only single antenna link with main lobes

facing each other in both scenarios out of available 16 antenna links.

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RESCUE D4.3, v1.0

Results and discussion

LOS propagation direction in Wide Grid and Moving Car scenario

The estimated LSF for the wide grid static measurement scenario is shown in Fig 3.1 for four different time in-

stances: (a) t = 0s, receiver trolley at the beginning of track, (b) t = 78s, receiver trolley approaching the transmitter

trolley, (c) t = 80s, receiver trolley passing transmitter trolley, (d) t = 95s receiver trolley moving away from the

transmitter trolley after taking a right turn. From the figure it is evident, line of sight (LOS) moves from right to

center as the receiver trolley is moved slowly towards the transmitter trolley and shifts from center to left as the

receiver trolley passes the transmitter trolley and moves away further after taking right turn. The delay decreases

from 0.375 μs at time t = 0s to 0.075 μs at t = 78s, as the Rx measurement trolley approaches the Tx trolley, i.e,

distance between the trolleys decreases. As the Rx measurement trolley crosses the Tx trolley and moves away,

delay increases to 0.1 μs for t = 80s and at t = 95s delay of 0.125 μs is observed, due to the fact the distance

between the trolleys is increasing. In Fig 3.1d negative doppler frequency of -11 Hz confirms that the receiver

trolley is moving away from the transmitter trolley.

Time variance of LSF indicates that the channel is non WSS, as along with LOS path smaller path with variant

delay and doppler are observed.

For computing stationarity time we first calculated collinearity of GLSF for the two scenarios. Fig 3.2 depicts

correlation of GLSF for the two considered scenarios at different time instants. Note that the scale of colormap is

different for the two scenarios.

In Fig 3.2a strong decrease in collinearity is observed away from the main diagonal, implying that GLSF at differ-

ent time instants are correlated only for small amount of time. The time variant structure of collinearity measure is

due to rich scattering environment. From time instant of 80s collinearity reduces thereafter, i.e, when the receiver

measurement trolley passes the transmitter trolley and move further away after taking turn. This attributed due to

the change in line of sight, the antenna element at the receiver which was in LOS of transmitter after taking turn is

now pointing in back while the element pointing in the back before taking turn, now is in LOS path with respect to

driving direction.

For Fig 3.2b very strong correlation is observed over longer time instants, meaning that even with LOS being

obstructed with mobile scatterer the GLSF for different time instants is mostly same and is not changing. For

beginning of the track, i.e, time instant 1− 4s, low collinearity is due to the mobile scatterer which is overtaking

the transmitter measurement trolley. Also during the time instant 60− 69s low correlation of GLSF is observed

due to the movement of mobile scatterer away from the receiver measurement trolley. The collinearity measure for

this scenario has a time invariant structure, which is mostly uniform throughout the track, as strong reflection are

observed from the high buildings located nearby to the measurement trolleys.

Recalling that for wide grid static measurement scenario the transmitter trolley is stationary and receiver measure-

ment trolley is moved slowly without any mobile scatterer, while for moving cars scenario both transmitter and

receiver are stationary and mobile scatterer is position between them.

After estimation of collinearity of GLSF we calculated stationarity time by taking sum over all the time instant

for which indicator function was 1, for the two considered scenarios. Stationarity time with mean of 3.86s was

observed for wide grid static measurement scenario while for moving car measurement scenario very high station-

arity time with mean 73.64s was observed. Fig 3.3 depicts time-variant stationarity time for the two scenarios. In

Scenario 1 large variation in stationarity time is observed due to the small window size used in time for estimating

LSF. Stationarity time is relatively higher for the beginning of the track. From time instance of 80s decrease in

stationarity time is observed, due to receiver trolley overtaking the transmitter trolley and moving away further.

Lower values of stationarity time in street crossing-section is due to the change in line of sight with respect to

driving direction.

For 2 scenario with the same window size smoother curve is obtained due to invariant structure of collinearity

measure. Very high stationarity time is observed, even when mobile scatterer is positioned between the stationary

transmitter and receiver, blocking the LOS path. Lower values of stationarity time during the beginning and at the

end of track is due to the mobile scatterer overtaking the transmitter and receiver trolley respectively.

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00.5

11.5

22.5

3 −3−2

−10

12

−220

−200

−180

−160

−140

−120

−100

−80

−60

Doppler frequency [Hz]Delay [µ s]

Pow

er [d

B]

(a)

00.5

11.5

22.5

3 −10−5

05

10

−180

−160

−140

−120

−100

−80

−60

−40


Pow

er [d

B]

(b)

00.5

11.5

22.5

3 −10−5

05

10

−200

−150

−100

−50


Pow

er [d

B]

(c)

00.5

11.5

22.5

3 −10−5

05

10

−200

−180

−160

−140

−120

−100


Pow

er [d

B]

(d)

Figure 3.1: GLSF estimation at different time instants (a) t = 0s, (b) t = 78s, (c) t = 80s and (d) t = 89s

Different view directions in Wide Grid and Moving Car scenario

In contrast to the above analysis four different view directions (front, back, left, right) wrt. the driving direction

has been considered. The Fig 3.4 depicts correlation of the GLSF for wide grid static measurement scenario

for these four different orientations of the antenna elements. It is apparent that for all the orientation strong

decrease in collinearity is observed away from the main diagonal, implying that GLSF at different time instants are

correlated only for small amount of time. The time variant structure of collinearity measure is due to rich scattering

environment. From Fig 3.4a and 3.4b it is evident that front and left orientation have similar structure, with left

orientation being in proximity of the line of sight (LOS) path. The structure of back and right orientation is also

observed to be similar as depicted in Fig 3.4c and 3.4d, with right orientation being mostly hindered from LOS

path for the entire track. As the Rx measurement trolley passes the Tx trolley and moves away after taking turn,

i.e from time instant of 80s, collinearity reduces thereafter for front and left orientation due to the change in LOS

path. The front antenna element at the receiver which was in LOS of transmitter after taking turn is now pointing

in back. While the element pointing in the back before taking turn, now is in LOS path with respect to driving

direction resulting in increased collinearity for back orientation.

For moving car measurement scenario collinearity of GLSF is shown in Fig 3.5 for the different orientations of

the antenna element with respect to driving direction. Note that the scale of colormap is different for the two

scenarios. From Fig 3.5a and 3.5b we observe front and right orientation have homogeneous and time invariant

structure throughout the measurement scenario. Decrease in collinearity during beginning and end of the track

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(a) (b)

Figure 3.2: Collinearity of GLSF for (a) wide grid static measurement and (b) moving cars measurementscenario

0 20 40 60 80 100

1

2

3

4

5

6

7

8

Time[s]

Sta

tiona

rity

Tim

e[s]

(a)

0 10 20 30 40 50 60 70

10

20

30

40

50

60

70

Time[s]

Sta

tiona

rity

time[

s]

(b)

Figure 3.3: Stationarity time (a) wide grid static measurement and (b) moving cars measurement scenario

for right orientation due to movement of the mobile scatterer overtaking Tx and Rx trolley respectively. High

correlation observed for the two orientation is due to strong reflection from the high buildings located nearby to

the measurement trolleys. While the structure for back and left orientation is also observed to be similar as shown

in Fig 3.5c and Fig 3.5d, with left orientation being in the hindered path from the Tx trolley and having large

variations in the collinearity matrix.

Again after estimation of collinearity of GLSF we calculated the stationarity time. Fig 3.6 shows stationarity

time for four orientations of the antenna element for wide grid static measurement scenario. Large variation in

stationarity time is observed due to the small window size used in time for estimating LSF. The front and left

orientations with homogeneous collinearity structure also have similar structure for stationarity time as shown in

Fig 3.6a and 3.6d. Relatively higher stationarity time at the beginning of the track for the two orientations is due

to LOS component. From time instance of 80s decrease in stationarity time is observed, due to receiver trolley

overtaking the transmitter trolley and moving further away. As the result the LOS path changes, with front antenna

element now pointing at the back while the antenna element pointing at the back is now in LOS path with respect

to driving direction. Which accounts for higher stationarity for back and right orientations after Rx trolley takes

turn as depicted in 3.6c and 3.6b.

Table 3.1 summarizes the mean stationarity time for the two scenarios with respect to each orientations. The mean

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(a) front (b) left

(c) back (d) right

Figure 3.4: Collinearity of GLSF for wide grid static measurement scenario with respect to driving direction

Table 3.1: Mean stationarity time [s] for different orientation for wide grid and moving cars scenarioScenario Front Left Back Right

Wide grid 3.8608s 4.1040s 3.1322s 1.9138sMoving Cars 73.6468s 10.5234s 15.7227s 34.5865s

stationarity time for left orientation is higher than the front orientation for wide grid scenario due to reflections

from the building located close to street crossing section.

Stationarity time for moving cars scenario for each orientation is shown in Fig 3.7. High stationarity time of

73.6468s and 10.5234s for front and right orientation is due to strong constant reflections from the nearby located

buildings, even with LOS path being completely blocked as shown in Fig 3.7a and 3.7b. Decrease in stationarity

time for right orientation is due to the mobile scatterer movement at beginning and end of track. While the

stationarity time for back and left orientations is depicted in Fig 3.7c and 3.7d, with left orientation mostly being

in the hindered path having low mean stationarity time of 10.5234s.

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(a) front (b) right

(c) back (d) left

Figure 3.5: Collinearity of GLSF for moving cars measurement scenario with respect to driving direction

3.2 Analysis based on the Correlation Matrix Distance (CMD)

In [21], Correlation matrix distance (CMD) is introduced for measuring non-stationarity in MIMO channels based

upon the spatial correlation matrices. It is used to measure (dis)similarity between two matrices using inner prod-

uct. The inner product between two matrices R1 and R2 is given as

< R1,R2 >= ∑i

∑j

r(1),i jr(2),i j = tr{R1R2} (3.10)

Then from Cauchy-Schwarz inequality we have

tr{R1R2} ≤ ||R1||F ||R2||F (3.11)

where ||.||F represents Frobenius norm.

Thus from above, CMD for two matrices R1 and R2 is defined as

CMD(R1,R2) = 1− tr{R1R2}||R1||F ||R2||F (3.12)

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1

2

3

4

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9

Time [s]

Sta

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Tim

e [s

]

(a) front

0 20 40 60 80 1000

1

2

3

4

5

6

7

8

9

Time [s]

Sta

tiona

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Tim

e [s

]

(b) right

0 20 40 60 80 1000

1

2

3

4

5

6

7

8

9

Time [s]

Sta

tiona

rity

Tim

e [s

]

(c) back

0 20 40 60 80 1000

1

2

3

4

5

6

7

8

9

Time [s]

Sta

tiona

rity

Tim

e [s

]

(d) left

Figure 3.6: Stationarity time for wide grid static measurement scenario with respect to driving direction

From 3.12 CMD in time for characterizing similarity in spatial domain can be expressed as [17]

CMDi(m,m′) = 1− tr{Ri[m,q],Ri[m

′,q]}

||Ri[m,q]||F ||Ri[m′,q]||F

(3.13)

where m and q are discrete time and frequency values, Ri represents full, Tx and Rx correlation matrix given as [17]

R f ull [m,q] = E{

vec{LH [m,q]}vec{LH [m,q]H}}RT X [m,q] = E

{LT

H [m,q]L∗H [m,q]

}RRX [m,q] = E

{LH [m,q]LH

H [m,q]} (3.14)

with LH [m,q] being the channel transfer function in time and frequency.

The value of CMD in the above expression ranging from zero to one, i.e, CMDi(m,m′) ∈ [0,1]. With zero

indicating maximum correlation between different time instances and 1 corresponding to no correlation. In terms

of eigen value decomposition(EVD), the product of two matrices R1 =U1Λ1UH1 and R2 =U2Λ2UH

2 is given as [22]

R1R2 =U1Λ1UH1 U2Λ2UH

2 =U1DUH2 (3.15)

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0 20 40 600

10

20

30

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50

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80

Time [s]

Sta

tiona

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Tim

e [s

]

(a) front

0 20 40 600

10

20

30

40

50

60

70

80

Time [s]

Sta

tiona

rity

Tim

e [s

]

(b) right

0 20 40 600

10

20

30

40

50

60

70

80

Time [s]

Sta

tiona

rity

Tim

e [s

]

(c) back

0 20 40 600

10

20

30

40

50

60

70

80

Time [s]

Sta

tiona

rity

Tim

e [s

]

(d) left

Figure 3.7: Stationarity time for moving cars measurement scenario with respect to driving direction

where (Di j) = uH(1),iu(2), jλ(1),iλ(2), j

Here λ(1),i and λ(2), j are eigen values with u(1),i and u(2), j being corresponding ith and jth eigen vectors of R1 and

R2. Thus D will be zero if u(1),i and u(2), j are orthogonal or either λ(1),i and λ(2), j is zero. So, if D is zero then in

(3.12) tr{R1R2} will be zero and the CMD while have the maximum value of one.

For evaluating the stationarity time, we define threshold for CMD using indicator function as [17]:

Mcmd [m]� {m′ |CMDi(m,m

′)<CMDth} (3.16)

with CMDth is the CMD threshold. And the corresponding stationarity time is obtained as [17]:

Tstat, j[m]� |Cj[m]|Ts (3.17)

with |Cj[m]| is the connected subset of Mk with maximum cardinality and Ts is the spacing in the time samples.

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Stationarity distance and vehicle influence in Moving Car scenario

For a deeper analysis on the influence of a moving car onto the wireless link between two vehicles the CMD of

a 4x4 MIMO matrix has been studied. The four antenna elements at both ends of the link have been selected in

the same manner, as follows: horizontal polarized antenna ports at elements from the upper ring of each SPUCA,

whereby the inter-element distance was defined by selecting each second element at the antenna ring (so basically

the 4 elements are viewing again front, back, left and right). Furthermore 4 different measurement runs from the

moving car measurement scenario the ”L7-R2” have been chosen: textitpickup truck forward as well as backward

and textitvan forward as well as backward. The 3.8 and 3.9 detail the results. In 3.8 the stationarity distances

derived on the different CMD results for the Tx, Rx and full correlation matrix are highlighted. Basically all

CMDs follow the same trend, whereby the CMD for Tx and Rx are very similar and the full CMD is - as expected

- usually higher. Very interesting is the region around the distance 15-25 m for the forward driving direction of

the interacting car and the region at 60-80 m for the corresponding backward case. At his point both, the van

and the pickup truck, influence clearly the link between the non-moving Tx and Rx trolley. Hence the stationarity

distance is very low. This effect can be very impressive highlighted in 3.9 where for the four combinations the

CMD is plotted. The influencing region can be clearly identified as difference from the blue background color. A

subsequent analysis based on identifying the start and end point of this influencing region leads us to the results

characterizing size of this area 3.2. For comparison also the values derived from the GLSF are shown. Interestingly

it was found that the size for the GLSF is appr. 6-9m with a trend to 9 and for the CMD is appr. 8-12m with a trend

to 11m. For the GLSF it can be seen that the region is larger for the pickup truck compared to the van and for the

CMD it is similar but the difference is not that strong. As a rule of thumb the influencing region is close to the Tx

or Rx vehicle and the region is appr. double of the size (length) of the interacting car/vehicle.

Table 3.2: Interacting vehicle influence on stationarity

TrackGLSF CMD

Start [m] End [m] Distance [m] Start [m] End [m] Distance [m]

L7-R2 truck forward 17.25 26.3 9.05 15.56 27.32 11.76

L7-R2 truck backward 63.28 72.26 8.98 63.28 73.25 9.97

L7-R2 Van forward 11.48 17.74 6.26 11.71 19.37 7.66

L7-R2 Van backward 63.05 71.48 8.43 60.83 71.34 10.51

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]

RX CMDTX CMDFULL CMD

(a) pickup truck forward

0 20 40 60 80 1000

10

20

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80

Distance [m]

Sta

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Dis

tanc

e [m

]


(b) pickup truck backward

0 20 40 60 80 100 1200

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Distance [m]

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Dis

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e [m

]


(c) van forward

0 20 40 60 80 1000

20

40

60

80

100

120

Distance [m]

Sta

tiona

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Dis

tanc

e [m

]


(d) van backward

Figure 3.8: Stationarity distance for moving cars measurement scenario with different interacting vehiclesand driving directions

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(a) pickup truck forward (b) pickup truck backward

(c) van forward (d) van backward

Figure 3.9: CMD analysis for moving cars measurement scenario with different interacting vehicles anddriving directions

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4. Cluster based analysis of Uoulu measurement campaign

In this chapter results are presented from a V2V channel sounding campaign conducted by Oulu university. Al-

though, the campaign was performed outside the RESCUE project its results can give important feedback for the

channel modelling work performed in WP4 of the RESCUE project. The analysis focusses on directional results

obtained using high resolution parameter estimation. Furthermore, the obtained estimation results were processed

with a multipath clustering algorithm in order to provide insight to per-cluster parameters as they are necessary for

the proposed V2V channel model.

The radio channel in vehicle-to-vehicle (V2V) communication is significantly different in comparison to the tra-

ditional base station to mobile station (BS-MS) propagation scenario since the both link ends are moving and the

antennas are close to the ground level [3]. Traditional geometry based stochastic models (GSCM) for BS-MS com-

munication, e.g., WINNER model, are based on cluster concept. This means that the cluster statistic obtained from

the measurements is given as an input for the model. Recently, there has been discussion to extend these models

to support V2V communication. To overcome this problem the updates for the model framework should be done,

e.g. for modeling dual mobility. Also, the adjustment for the parameters should be done since the propagation

environment is more dynamic. A numerous measurement campaigns have been carried out to study V2V channel

characteristics. Previous studies have been mainly focused on the basic propagation parameters such as path loss

and delay spread. Only few studies have been focused on the directional analysis of V2V propagation channels

and none of those, to best of our knowledge, have been focused on statistical analysis of cluster properties. This

motivated us to carry out V2V radio channel measurements and analyzing the cluster parameters. In this section

we present the measurement based cluster parameters from the vehicle-to-vehicle (V2V) channel measurements in

an urban environment. To be more specific, we present the statistical properties of the clusters usable in a GSCM

model. The overall (’non-cluster’) parameters have been given in [11] and [9]. The cluster results here are based

on data from the same locations as [11], [9] and are thus suitable to complete the model parameters.

4.1 Parameter description

The main overall (’non-cluster’) large scale (LS) parameters are:

• Shadow fading standard deviation [dB], delay spread, K-factor.

• Angle spreads of departure (azimuth and elevation) and Angle spreads of arrival (azimuth and elevation)

• Necessary proportionality parameters [23] are given also.

In our model the large scale (LS) parameters are specified by the logarithmic (or decibel when appropriate) means

and standard deviations. In addition the large scale parameters are correlated with each other. Results for the

LS-parameters with auto and cross-correlations have been given in [11] and [9] for 2.3 and also for 5.25 GHz.

Small scale parameters are the instantaneous cluster powers, delays, AoAs, EoAs, AoDs and EoDs. The distribu-

tions of these are determined by the corresponding LS parameters.

The cluster parameters are:

• Number of clusters.

• Number of rays per cluster.

• Cluster ASD, ESD, ASA and ESA. (CASD, CESD, CASA and CESA, respectively).

• Per cluster shadowing standard deviation.

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Table 4.1: Data post-processing settings.Max num of iterations 5

Num of full search iterations 3

Doppler-block size 4 snapshots

Max number of paths 50

AoA search range 180 deg – +180 deg

EoA search range -70 deg – +90 deg

In this chapter, we present the above mentioned results for V2V connections in LOS and NLOS environments.

4.2 Parameter Estimation Tools

The measured data was post-processed by initialization and search improved SAGE algorithm [24]. The algorithm

gives as an output the estimates of propagation paths meaning that the effect of antennas is removed in the data-

processing. Each propagation path estimate contains the information of path delay, direction (AoA and EoA) and

the amplitudes of each polarizations component. The main post-processing settings are presented in Table 4.1. The

results presented in this paper are for vertical polarization only.

From the post-processed results (in UOULU case SAGE) the clusters have been extracted and the desired char-

acteristics like Cluster-wise ASA (CASA), CESA, etc., calculated. The clusters are composed of rays that have

closely related spatial parameters. Cluster results of UOULU have been calculated using the KPowerMeans clus-

tering algorithm, described in [25] and [26], that is an extension of KMeans algorithm. Delays, angles (azimuth and

elevation in both link ends) and powers of the propagation paths are given as an input to the clustering algorithm.

We have used the 3D clustering for the azimuth-of-arrival (AoA), elevation-of-arrival (EoA), delay and power of

the rays..Due to the fact that we used SIMO antenna configuration, we could extract angular estimates directly only

for the receiver end. The clustering algorithm consists of two phases; initial guess and KPowerMeans algorithm. In

the initial guess phase, the algorithm sets the cluster centroids as described in [27]. Multipath component distance

(MCD) presented in [26] is used as a metric for calculating the multidimensional distances between propagation

paths and all centroids. Propagation rays are set to the clusters based on the shortest MCD. In the initial guess

phase, the optimum number of clusters is selected based on power limit, say 0.01. This means that if there are Nc

extracted clusters and the weakest cluster power has less than 0.01 of the total received power, it is discarded and

the previous combination of clusters, Nc-1 clusters are given as an input to the KPowerMeans algorithm. If the

number of clusters in the initial guess and KPowerMeans algorithm does not match, a new initial guess is made

and the algorithm is repeated.

In our simulations we have used power limits 0.01. In addition we used the clustering time interval corresponding

6 snapshots. This means that six snapshots were handled at one time.

4.3 V2V SIMO Channel Sounding Campaign at Oulu

The measurements were conducted in Oulu downtown with EB PropsoundCSTM channel sounder [28] at the

center frequencies of 2.3 and 5.25 GHz. The measurement devices were mounted in two vehicles shown in Figure

4.1.

The measurement device uses direct sequence spread spectrum (DSSS) technique for channel sounding. The im-

pulse responses (IRs) of the channel samples are obtained by correlating the received signal with the spreading

code used in transmission. Sounding in spatial domain is employed by switching through all Tx - Rx antenna

pairs in time domain. The antenna elements should be switched through so fast that the channel response remains

practically constant within the antenna switching period. To keep the number of switches reasonable, the mea-

surements were performed for single-input multiple-output (SIMO) antenna configuration. A vertically polarized

dipole antenna and an omnidirectional antenna array (ODA) were used as Tx antenna and Rx antenna, respectively.

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Figure 4.1: Measurement vehicles. The blue one contains the transmitter

Table 4.2: Measurement settings at 2.3 GHz and 5.25 GHz center frequency.Parameter 2.3 GHz 5.25 GHz

Bandwidth [MHz] 100 200

Transmit power [dBm] 23 23

Antenna configuration 1 x 56 1 x 50

Tx antenna gain [dBi] 1 1

Rx antenna gain [dBi] 6 6

Code length [chips] 255 511

TX and Rx relative speed [km/h] 0-20 0-20

Max. Doppler shift [Hz] 170 292

The ODA consists of 28 (56 feeds) dual polarized elements in 2.3 GHz. The antennas were on the roof of cars

with the heights of 1.6 m and 2.5 m on the Tx side and the Rx side, respectively, as shown in Figure 4.1. The

information of GPS positions was recorded in order to calculate the link distance between the Tx and the Rx. The

main measurement settings are presented in 4.2.

The measurements of UOULU were performed at the city center of Oulu, Finland. The measurement scenarios

were urban LOS and NLOS scenarios at 2.3 and 5.25 GHz. In the LOS measurements there were two subscenarios:

Cars driving in the same direction (SD) and in the opposite direction (OD).

The SD measurements were performed in the low traffic (LT) density conditions and the OD measurements were

performed in the high traffic (HT) density conditions. The measurement environment consists of one lane streets

per direction and four to six store buildings by the streets. The measurement routes are presented in Figure 4.2.

The total distance in the 2.3 and 5.25 GHz with V2V LOS and NLOS measurements in the urban environment is

about 5 - 10 km.

The measurements for the NLOS environment were performed so that the vehicles drove in same direction sepa-

rated approximately by 15 to 50m, except that the the vehicles turned in the crossing one after another. After the

turning of the first vehicle to the crossing street and the second vehicle drove on the original street the buildings

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caused shadowing conditions until the second vehicle arrived on the crossing street. There were 15 corners mea-

sured at 2.3 GHz NLOS and 18 at 5.25 GHz. However, many of them could not be used, because no real NLOS

condition was present. The corners that were analyzed were selected so that the NLOS condition was present.

Figure 4.2: Example routes for the V2V 2 an 5 GHz measurements.

4.4 LS Results

In the analysis we are aiming at obtaining the parameter sets for the frequencies 2.3 and 5.25 GHz in LOS and

NLOS scenarios. The focus will be in the clustered results, but we will propose also the over-all (non-clustered)

parameters. Firstly, we will present non-clustered results followed by clustering results starting from Section 4.5.

The most interesting results are depicted in Figures, however the numerical values for all the parameters are given

in tables.

The over-all results have been calculated using in principle the same data as used in METIS project [9]. Now the

values are calculated for all over-all parameters, except for some correlation values that cannot be calculated from

the measurement data due to the measurement setup (SIMO). Instead the parameter values should be taken from

literature. The results for V2V NLOS measurements are missing from METIS results and are calculated now first

time from the V2V NLOS measurement data.

The over-all parameter values, sc. large scale parameters, extracted from the measurements are the following

[23]:

• RMS-delay spread (DS)

• RMS-azimuth spread in arrival (ASA)

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• RMS-azimuth spread in departure (ASD)*

• RMS-elevation spread in arrival (ESA)

• RMS-elevation spread in departure (ESD)*

• Ricean K-factor (KF)

• Shadow fading (SF)

• Cross-polarization ratio (XPR)

The seven first parameters are LS parameters. The XPR is used slightly differently. In the deep NLOS conditions

the XPR is very small and can be neglected. ”*” means that the ASD and ESD parameter values cannot be extracted

from the measurement data due to the SISO measurement setup. However, they can be obtained due to reciprocity.

In the model the parameters are used as logarithmic (log10) form represented by the logarithmic mean and standard

deviation. than the actual LS parameters. The KF, XPR and SF are used as dB values. In addition scaling factors

are given for the DS and delay distribution, ASA and azimuth angle distribution as well as ESA and elevation

angle distributions, see [23]. Besides the parameter values, the autocorrelation functions for the LS parameters

and cross-correlation values for all pairs of the LS parameters have been extracted from the measurements. The

analysis results for the over-all parameters are shown partly in the Figures 4.3, 4.4 and 4.5, and all of them are

given in the tables: The logarithmic/decibel valued LS parameters and their correlation distances are given in Table

4.3. The cross correlations are given in Table 4.4.

In addition to the LS parameters there are the Small Scale parameters that use the LS parameter values to control

the values of the Small Scale parameters, i.e. the instantaneous AoA, EoA, AoD and EoD angles and e.g. the

instantaneous shadow fading, delay spread, K-factor and XPR.

Pathloss

Pathloss figures for 2.3 and 5.25 GHz LOS can be found in [9]. From the V2V measurements considered in this

chapter, we recognized that pathloss for the NLOS case (actually ”around the corner case) is the same as for the

LOS case, when both vehicles are on the same street, i.e. in the beginning and in the end of the measurement.

When the vehicles are on the different streets having the corner between them, the corner causes an additional loss.

It seems to be about 10 to 15 dB in the measured cases.

Azimuth and elevation spreads in arrival

The PDF of the azimuth and elevation spreads are shown in Figure 4.3. They behave roughly similarly. The 50

percent value for azimuth spread is slightly below 40 degrees and the corresponding value for the elevation spread

is round 20 degrees. The figures for AoA and AoE have been shown in Figure 4.3.

The distribution is quite peaked to be Wrapped Gaussian. Actually the distribution could be combined of two

Laplacian distributions, peaks towards Tx and opposite direction to the Tx. In addition an even distribution could

be added, if the level of the distribution would otherwise be too low between the spikes. For NLOS the AoA can be

described with a Wrapped Gaussian distribution, assuming that the spikes and elevated level at direction towards

Tx can be explained with LoS conditions in the beginning and/or end of the measurement sessions. The EoA is

surprisingly similar for 2.3 and 5.25 GHz and also for LOS and NLOS. We can assume that the spike in the -70

degrees is some kind of artefact. Otherwise Laplacian distribution plus an even distribution could be fitted to the

measured distribution.

Logarithmic values of the LS parameters and their linear correlation distances for the frequencies 2.3 and 5.25

GHz and propagation conditions LOS and NLOS are shown in Table 4.3, where the superscript * means that the

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Figure 4.3: Cdf-curves for azimuth and elevation for 2.3 GHz LOS (a), 5.25 GHz LOS (b), 2.3 GHz NLOS(c) and 5.25 GHz NLOS (d)

results are theoretically obtained from the results in opposite transmitting direction (due to missing results). It was

found that correlation distances of the K-factor seem to be unexpectedly small.

In V2V 5GHz OD/LOS case the measured correlation distances are considerably different: Correlation distances

for DS, SF, ASA and ESA are 12, 11, 15 and 22 m, respectively. At the same time the correlation distance for the

Ricean K-factor is only 0.4 m. Intuitively the parameters should be the same for the movements in the same and

the opposite directions.

The cross-correlations of the LS parameters needed for the model are given in Table 4.4. These have been shown

for 2.3 and 5.25 GHz center frequencies and LOS and NLOS propagation conditions. The superscript * in Table

4.4 denotes the case where the results are theoretically obtained from the results in opposite transmitting direction

(due to missing results). Cross-correlations ASD vs ASA, ESD vs ASA, ASD vs ESA and ESD vs ESA could not

be extracted from our measurements. These values should be taken from other sources, like [23] and [29].

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Table 4.3: LS parameters and their correlation distances for the frequencies 2.3 and 5.25 GHz.Scenarios 2.3 GHz 5.25 GHz 2.3 GHz 5.25 GHz

LOS NLOS LOS NLOS

DS log10([s]) -7.7 -7.5 -7.7 -7.5

Std 0.36 0.27 0.37 0.36

ASD log10([ ]) 1.6 1.5 1.6 1.6

Std 0.13 0.22 0.16 0.11

ASA log10([ ]) 1.6 1.5 1.6 1.6

Std 0.13 0.22 0.16 0.11

ESD log10([ ]) 1.3 1.1 1.4 1.4

Std 0.14* 0.23* 0.11* 0.10*

ESA log10([ ]) 1.3 1.1 1.4 1.4

Std 0.14 0.23 0.11 0.1

K-factor (K) [dB] 8.2 5.9 10.2 6.9

Std 5.6 5.3 8.4 5.6

XPR [dB] 15.3 14.7 15.4 15.7

Std 6.7 6.4 7.7 7

Shadow fading (SF) [dB] 4 7 4 5.1

Delay distribution Exp

Delay scaling parameter r 0.19 0.44 0.35 0.33

Correlation distance [m]

DS 8 3 7 2

ASD* 4 4 2 3

ASA 4 4 2 3

ESD* 6 4 2 5

ESA 6 4 2 5

SF 7 4 2 2

Table 4.4: Cross correlations of the LS parameters at 2.3 GHz and 5 GHz center frequency.Scenarios 2.3 GHz 5.25 GHz 2.3 GHz 5.25 GHz

LOS NLOS LOS NLOS

Cross-Correlations ASD vs DS* 0.3 0.1 0.4 0.5

ASA vs DS 0.3 0.1 0.4 0.5

ESD vs DS* 0 -0.2 -0.3 0

ESA vs DS 0 -0.2 -0.3 0

ASD vs SF* 0’ 0.2 0.5 0.4

ASA vs SF 0 0.2 0.5 0.4

ESD vs SF* -0.1 -0.4 -0.4 0

ESA vs SF -0.1 -0.4 -0.4 0

DS vs SF -0.1 0.3 0.6 0.8

ESA vs ASA 0.5 0.3 -0.2 -0.4

ASD vs ESD* 0.5 0.3 -0.2 -0.4

ASD vs K* 0 0 0.1 0

ASA vs K 0 0 0.1 0

ESD vs K* 0 0 -0.2 0

ESA vs K 0 0 -0.2 0

DS vs K 0 0 0 0

SF vs K -0.1 0 0.1 0

Delay distribution Exp

Delay scaling parameter r 0.19 0.44 0.35 0.33

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Figure 4.4: Cdf-curves for azimuth and elevation for 2.3 GHz LOS (a), 5.25 GHz LOS (b), 2.3 GHz NLOS(c) and 5.25 GHz NLOS (d)

The effect of obstacles

In the case of V2V an interesting matter is the effect of different objects. The buildings, hills, rocks and comparable

large objects are considered to be part of the landscape and their effect should be included in the model, as the

corner effect in our case.

Beyond these objects remain pieces of infrastructure like traffic lights, light poles, power line pylons etc. stable

objects. In addition we encounter moving objects like vehicles, human beings etc. For V2V the greatest influence

have the other moving vehicles. It would be possible to try to extract effects of moving and stable objects from the

measured data. Although this would be extremely important, the time for this kind of analysis is too short. Another

alternative is to try to model these obstacles by ray-tracing type of tools

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Figure 4.5: PDFs of the azimuth (a) and elevation (b) spreads for 2.3 GHz NLOS connections. PDFs of theazimuth (c) and elevation (d) spreads for 5.25 GHz NLOS connections

4.5 V2V cluster results

The cluster results will be presented for 5GHz and 2 GHz and for LOS (same street) and NLOS (driving around

corner). The mean of the linear values (and also the decibel values when needed) for the cluster parameters are

given in the Table 4.5. In the following, we will graphically illustrate and explain the results shown in the table.

During the analysis it became evident that LOS SD and LOS OD can be presented with the same model. In the

analysis we have used the power limit 0.01 and calculated over 6 adjacent snapshots at a time.

The following cluster parameters are presented in Table 4.5 for 2.3 and 5.25 GHz LOS and NLOS connections:

• Number of clusters.

• Number of rays in a cluster.

• Cluster ASA (CASA).

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• Cluster ESA (CESA).

• Per-cluster shadowing standard deviation.

• Cluster delay spread.

• Cluster life-times.

Throughout our analysis we assume that for the reverse case (Rx to Tx) the results can be generalized due to

reciprocity of the radio vawe propagation. Therefore we present the cluster ASD (CASD) and cluster ESD (CEST)

identical with the CASA and CESA results.

The LOS condition is separated in two sub-cases, same direction (SD) and opposite direction (OD). As a matter of

fact, these should be identical due to the symmetry of the cases. Only the locations in the lanes are different: In the

SD sub-case the vehicles are mostly on the same lane, in the OD case the vehicles are predominantly on different

lanes. It is assumed that the difference can be ignored.

The NLOS case means here the case when the vehicles are on crossing streets. Only the case when the vehicles

drive the route in same direction, separated by some tens of meters, is analyzed. In the measurements the vehicles

are relatively long time on same street both when starting and when ending the measurement. NLOS happens near

the street crossing. Results have been selected from cases where clear entering and exiting of the shadowed zone

can be recognized.

Number of clusters and number of rays in a cluster

The cumulative distribution functions (CDFs) for the number of clusters and for the number of rays in a cluster

can be seen in Figures 4.6, 4.7, 4.8, and 4.9. In these figures, the measured median number of the clusters and the

number of rays in a cluster are given. The median values for the number of rays are small in comparison to the

values reported in WINNER II. However, it is obvious that the number of rays is small since the possible maximum

number of rays in single snapshot is 50. Similar values have been reported in the literature, e.g. in [30]

Cluster ASA and ESA

The CDFs of the cluster-wise azimuth and elevation spreads in the receiver side (CASA and CESA, respectively)

are shown in Figures 4.10, 4.11, 4.12 and 4.13. In these figures, the measured CASA and CESA values are given.

Although we can’t get the parameters (ASD and ESD) from the measurements directly we can use the values ASA

and ESA representing also the ASD and ESD in the V2V channel model. The reason is that the antenna heights

are reasonably equal and due to the reciprocity the connections would work equally in the opposite direction. It

should be noted that the same is true also for the overall ASD and ESD.

Cluster RMS-delay spread

Cluster RMS-delay spread is defined equally as the overall RMS-delay spread, except that it is calculated only for

the radio waves included in the cluster. Cluster delay spread is therefore smaller than overall one. Actually the

cluster RMS-delay spread is so small that it can be set to zero in most cases, like in the WINNER model [23]. The

cumulative distribution functions (CDF) of cluster delay spreads are shown in Figures 4.10, 4.11, 4.12 and 4.13.

The values in the different cases can be found in 4.5.

Per-cluster shadowing

In general, the total power of different clusters is different. The shadowing standard deviation per cluster describes

deviation of powers between clusters. According to our measurements its median is 2 - 6 dB and its values for the

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Table 4.5: Mean values of the cluster parameters for 2.3 and 5.25 GHz.Scenarios 2,3 GHz 5,25 GHz

LOS SD NLOS LOS SD NLOS

Number of clusters 7.7 8.2 8.0 5

Number of rays per cluster 4.0 3.4 5.8 8.7

Cluster DS (ns) 1.4 1.0 ’1.9 2.6

Cluster ASD (degrees)* 14.7 11.6 16.0 16.9

Cluster ASA (degrees) 14.7 11.6 16.0 16.9

Cluster ESD (degrees)* 8.8 6.9 9.8 11.4

Cluster ESA (degrees) 8.7877 6.8759 9.7569 11.3608

Per cluster shadowing std [dB] 5.0 4.5 4.9 4.0

Cluster lifetime (ms) 182 127 341 273

different cases can be found in Table 4.5.

Cluster lifetime

Cluster lifetime is given in Table 4.5. It seems that the analyzed lifetime depends strongly on the strategy for

analysis: Analysing several snapshots at one time, six in our case, gives extremely longer lifetimes than analyzing

only one snapshot at a time. The difference is about 100 fold, as can be seen in 4.5. In addition the values of cluster

lifetimes are spread over a very wide time interval. The two moving vehicles have in principle a LoS connection.

In addition some paths reflected from walls can be quite stable in spite of the fact that the vehicles are moving.

This is especially true for movements in same direction. Scattering from different objects in the neighborhood will

certainly have shorter durations even if the scatterers are stable, because the vehicles are moving. The phenomenon

is more severe for scatterers moving in the opposite direction. Again the cluster lifetime may be longer, if the

scatterers are vehicles moving in the same direction with nearly the same speed.

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5. Extension of WINNER channel model (WIM) for V2V

5.1 Overview of WIM Implementation

The WINNER channel model (WIM) is of the class of GBSCMs. It basically models a Channel-Impulse-Response

(CIR) by sum-of-rays where each ray is parametrized using DoA, DoD, ToA and a complex polarimetric path-

weight. This resembles the ”geometric” part of GBSCM and is similar to what can be found e.g. in ray-tracers. The

generation of the ray-parameters is stochastically driven by certain well-defined random processes (characterised

by their respective probability density functions (pdfs)). The moments (first and second) of the random processes

are itself randomly generated thus introducing the LSPs. In other words the LSPs are the moments of the random

processes that are used to finally create the ray parameters. Another crucial part of WIM is the concept of clusters.

A cluster is basically a collection of rays that share equal or at least very similar parameters. In WIM a cluster is

composed of a fixed number ob sub-rays (e.g. 20). This principle is depicted in Fig. 5.1.

The generation of channel coefficients in the WINNER model is divided into the generation of LSPs and small

scale parameters. The overall principle is depicted in Figure 5.2 which is taken from [14] where further details

about the whole channel model can be found as well. The geometric parameters (delay, angles etc.) of each ray are

generated using random processes according to the respective LSPs and pdfs. Once the LSPs are determined the

cluster delays are generated randomly and the cluster power is determined according to the delay-spread. In order

to calculate the angles a mapping between PDP and Power-Angular-Spectrum (PAS) is performed as depicted in

Fig. 5.3. In addition to this deterministic mapping of the angles a small random variation is performed on top of

the mapped angles. This procedure determines the mean angle of each cluster while the angular distribution of

sub-rays within a cluster is done in a deterministic manner.

The complex amplitude of each ray is generated by taking the aforementioned power from the PDP while the phase

is chosen randomly which is creating the fast fading that is observed in radio channels.

Figure 5.1: Principle of GBSCM (WIM)

Furthermore, WIM supports multiple links. That means multiple base-stations and multiple mobile-stations can

be defined and the pairing between stations (which MS is connected with which BS) can be freely defined. The

positions of each station can be defined in a certain coordinate system which eventually defines the network layout

(see Fig. 5.4). The distance between BS and MS has an impact on certain LSPs such as shadowing and pathloss.

Another key concept of WIM is ”drops”. A drop is a collection of channel realizations that share the same ray or

cluster parameter. The motivation of introducing drops is to simulate a (very) small movement of a mobilestation

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Figure 5.2: Principle of WIM channel generation. Image taken from [14] Figure 4-1.

where the geometric parameters of rays are unchanged. However, the phase of each ray will change according

to doppler shifts depending of the angles of the rays and the (assumed) velocity and moving direction of the

mobilestation. This doppler-induced phase change of the rays will create fast fading over the different channel

realizations within a drop.

While this model works very well for cellular networks it lacks some feature that are necessary for V2V channels.

Spatial consistency

The problem of spatial consistency is a very current research topic in the scientific community of channel mod-

elling. It is discussed in many publications regarding e.g. 5G channel models or in general V2V channel. By

spatial consistency we mean the feature of a channel model to produce ”similar” channels for ”similar” locations

in space. If two mobile stations are linked to the same basestation and they are very close to each other they should

observe highly correlated channels. In terms of GBSCMs this means that the geometrical structure of the channel

doesn’t change rapidly in space thus the channel generated by the model at nearby locations should be practically

the same. The need for having spatial consistent channel models arise both in multi-user models, where multiple

stations can be placed arbitrarily in space, as well as in models where the stations are mobile and the trajectory

of a station can be freely chosen. As stated above the WINNER model supports a network layout thus allowing

multiple nodes. The mobility of each node is however only virtual in the sense that within a drop a certain mobility

is simulated by introducing doppler-shifts to the rays.

The problem of spatial consistency is addressed in WINNER, however, only on the level of channel statistics

or LSPs. The basic assumption is that the radio channels of closely spaced stations should possess similar or

correlated LSPs. Therefore, the LSPs are not completely randomly created any more but a certain correlation

based on distance between stations is induced.

Unfortunately, this approach is not sufficient to generate spatial consistent channels. Not only the LSPs of neigh-

bouring channels should be similar or correlated but also the parameters of the clusters or the individual rays.

Consider the example shown in Fig. 5.5. Here the PDP of two links is shown where the location of both mobile

stations is the same. Since both stations are at the same location their links have the same LSPs. However, as you

can see the cluster delays are completely different. This is due to the random generation of cluster delays in WIM

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Figure 5.3: Derivation of cluster-angles based on randomly generated cluster-delays (image taken from [31])

Cells area, X[m]0 10 20 30 40 50 60 70

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Figure 5.4: examplary network layout of WIM with two mobile-stations and one base-station

independent from the stations location or any LSP correlation. In a spatial consistent channel model one could

expect, however, that the same channel coefficients are generated for multiple stations that are placed at the same

point in space.

Dual node mobility

With node-mobility we mean that not only the mobile-station moves but also the ”basestation” (in terms of V2V

it would be better to call it transmitter and receiver respectively). As mentioned above WINNER considers node

mobility only for the mobile stations and models its impact by introducing doppler shift within a drop. However,

for V2V it is often desired to define arbitrary trajectories along which both stations of a link (receiver/transmitter)

move. Furthermore, the extend of the trajectory is expected to be much longer then the extend of a drop in WIN-

NER and also follow more complicated paths then just straight lines (what is implicitly assumed in the WINNER

node mobility concept). Solving the problem of ”dual node mobility” is highly related to guarantee ”spatial con-

sistency” and time-evolution of the channel model.

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Mobile scatterers

In V2V scenarios it is necessary to correctly model the influence neighbouring vehicles have on the channel.

In WINNER, being a channel model that is also designed for urban areas where traffic occurs, the impact of

other vehicles is integrated into the overall probabilistic nature of the channel. In other words the randomness of

amplitude and phase of the clusters can be caused by vehicles. However, the overall movement of the vehicles

is assumed to be random with respect to the individual stations and therefore create randomness in the channel.

In V2V however the stations themselves are vehicles and they usually don’t move randomly with respect to other

vehicles but e.g. they drive in convoys or are overtaken/passed by in a rather deterministic fashion. Therefore, is

seems to be necessary to model the impact of other (nearby) vehicles in a specific way.

delay [s] ×10-70 1 2 3 4 5 6 7 8

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Figure 5.5: PDP of two links (MS) at the same location. PDP is averaged over 1000 realisations. Due toLSP-correlation the LSPs are equal for both links since they are at the same location. However,cluster delays differ for both links.

5.2 Extension for node mobility/spatial consistency

As mentioned in the previous section spatial consistency is defined as the property of a channel model to produce

”similar” or correlated channels for stations or links that are closely separated in space. Two links can be close in

space either by placing two different station at different locations or by a single station moving through a scenario

over time.

In WINNER the spatial consistency is only guaranteed for the LSPs of the channel but not the actual delays and

angles of the clusters or rays. The reason for that lies in the random generation of cluster delays for each drop and

link independently of each other. Since the cluster angles are semi-deterministic (deterministic mapping based on

cluster power and PAS plus additional randomness of the angles) determined from the cluster delays they are also

random. Therefore, it is not possible to use WINNER if a station is supposed to move along a trajectory that is

longer then the duration of a drop. Leaving the drop would theoretically require the WINNER channel model to

generate a new drop where the cluster parameters are randomly drawn, thus causing spatial inconsistency.

It can be concluded that in order to have a spatial consistent channel it is necessary to modify the random generation

of cluster delays. Delays should somehow be correlated with the delays at neighbouring locations, quite similar to

how the LSPs are already correlated. One approach would be then to introduce correlation in the generation of the

small scale parameters.

However, we have decided to take a different method and use interpolation using a predefined grid. The general

idea is that the channels generated by WIM are realistic representations of a certain propagation scenario. The

problem of spatial inconsistency arises from the transition between different drops, which is also known as the

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problem of time-evolution. Consider the example given in Figure 5.6. It shows the cluster delays of two drops that

are generated for two different locations of stations. If one station is now moving from its location towards the

other station the cluster delays should also move towards the delays of the other station. This is illustrated by the

arrows in Figure 5.6. This will create a spatial consistent channel along the trajectory of the moving station.

Figure 5.6: PDPs of two links at different locations (with correlated LSPs).

In order to implement the change of the cluster delays a suitable interpolation method will be chosen. The location

of a station is determined by two coordinates namely the x- and y-coordinate. Furthermore, both stations of a

link should be able to move (dual-node mobility). So the location of a single link or drop is denoted by a four-

dimensional vector

s = [xRx,yRx,xT x,yT x] (5.1)

Therefore, the interpolation method has to be able to work in 4D-space. For a proof of concept the MATLAB-

function ”interpn”1 will be used for the interpolation.

Another important issue is the pairing of clusters in different drops. Since multiple clusters are usually generated

an association which cluster in one drop belongs to another cluster in another drop is mandatory. For the time

being, the pairing is implemented by simply assigning clusters based on their delay. In other words the first delay

in one drop will be associated with first delay in the other drop and so on.

The general idea of establishing spatial consistency is to use the WINNER channel model to generate drops at

defined locations in the 4D-coordinate system. The spatial distance between grid points can in theory be arbitrarily

chosen, however, it should be much larger then the usual extent of a WINNER drop. The channel coefficients for

an arbitrary location in the 4D-space will then be determined by interpolation using the adjacent grid points. This

principle is illustrated in Figure 5.7. Using the interpolation approach means that relevant random process required

in the traditional WIM (see Figure 5.2) will be replaced by interpolation. Once the parameters are calculated the

WIM code can be reused to finally generate the channel coefficients. The overall scheme is depicted in Figure 5.8.

Besides the interpolation of the cluster delays also the cluster angles (azimuth/elevation of departure/arrival) are

interpolated. The initial phases of the rays will still be generated randomly in order to still allow for a random

fluctuation of the channel. The cluster powers are determined by interpolating the delay-spread and use of the

interpolated cluster delays as it is done in WIM (just with the exception that delays and spread are not drawn ran-

domly). Furthermore, from the LSPs the angular spread is not interpolated, actually it is not explicitly determined

any more. This is due to the fact that the angular spread will be determined solely by the interpolated cluster angles

and powers.

1https://de.mathworks.com/help/matlab/ref/interpn.html

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Cells area, X[m]0 10 20 30 40 50 60 70

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Figure 5.7: examplary network layout of WIM with one mobile-stations and associated interpolation grid-points

Generate WINNER-Drops at grid-points

Create 4D xy-Grid with certain sampling distance

For given coordinates of Rx/Tx find closest grid

points

Interpolate necessary LSPs, cluster delays and

cluster angles

Use WIM to generate cluster coefficients based

on interpolated parameters

Figure 5.8: Scheme of generating spatial consistent channels.

5.3 Extension for mobile scatterers

In the previous section a procedure was describe to incorporate dual node mobility and spatial consistency into

the WINNER channel model in order to enable it for V2V channel simulations. Another important aspect of V2V

channels is the impact of mobile scatterers. The aforementioned extension, however, deals only with the modelling

of a channel with respect to surrounding buildings or the general environment. Mobile scatterers are not covered

by this approach. In order to incorporate them the following method is proposed. The positions and velocities

of additional cars is assumed to be feed to the channel model from a traffic simulator/emulator. That means the

V2V channel model is not itself responsible of generating realistic traffic scenarios but it is supplied from an

external component. Once the trajectories of the other vehicles are known their impact is overlayed on the general

channel.

Figure 5.9 shows an illustration of how the channel between two vehicles can be viewed. There is a single LOS

component and several clusters which might be single- or multi-bounce clusters. As the cars are moving along a

RxTx

Figure 5.9: Illustration of ray angles without additional scatterer

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RxTx

Figure 5.10: Illustration of ray angles without additional scatterer. One Cluster is shadowed (depicted bygray colour) and one addtional dynamic cluster appeared (depicted by red colour).

defined trajectory the parameters of the clusters are altered in order to provided a dynamically changing but spatial

consistent channel (according to the extension proposed in section 5.2). If another vehicle is approaching the two

nodes several things can happen. First of all if the vehicle is close it might shadow a cluster when it is moving

in the respective angle of arrival of angle of departure. Furthermore, it might create a dynamic cluster whose

temporal and angular properties are defined by the relative position of the vehicle to the other two nodes. This

principle is depicted in Figure 5.10. The shadowing part can be seen as a spatial filter that is applied to the PAS of

the transmitter and receiver respectively. Therefore, it is necessary to define the angular extent of the vehicle with

respect to the nodes. This can be done by defining a certain physical dimension. The closer the vehicle is to one

of the node the larger it will appear in the angular domain and the more clusters it might shadow. Since the car

(ans the two nodes) are still moving through the scenario this spatial filter has to be adapted over time according

to new positions of the vehicles. Similarly the spatial filtering property of the additional vehicle will only be in

effect if the vehicle is close enough to either the transmitter or receiver. In other words its angular extent has to be

large enough to effectively shadow a cluster. The parameters of the dynamic cluster can be calculated purely on

the geometrical relation between receiver, transmitter and the vehicle. However, the reflection coefficient has to be

carefully defined. In other words a model of the Radar-cross-section (RCS) of the vehicle has to be defined, that

takes into account the shape and the material. For simplicity and as a proof of concept the shape might initially be

approximated by a cuboid. Once this additional cluster is defined it will be dynamic in the sense that it changes its

properties even if receiver and transmitter are not moving, this should not happen with the ”environment” or static

clusters. Therefore, the final channel need not be spatial consistent in the sense that at same transmitter/receiver

location the same clusters should be visible.

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6. Use of Channelsounder Measurement Data for System-LevelSimulations

In the previous chapters it was detailed how measurement data from channel sounding campaigns can be used to

investigate the geometrical propagation conditions of V2V radio channels. The data is processed using sophisti-

cated high resolution parameter estimation techniques and subsequent statistical data analysis provides input for

deriving advanced channel models. However, the data acquired during the campaigns can also be directly used

for link- and more importantly system-level performance evaluations and/or simulations. Although, the choice

of antennas is usually mainly motivated by their applicability to parameter estimation, they are not so different

from realistic application antennas. The transmit signal during channel sounding is chosen in such a way that it is

possible to derive the channels complex impulse response or transfer function (in frequency domain). Therefore,

the data allows e.g. for (more or less) arbitrary modulation schemes.

Using channel sounding data for system-level simulations instead e.g. data from channel models gives the highest

degree of realism possible. Basically, no assumptions other then choice of antennas and signal bandwidth are

made and no simplifications typically found in any channel model are applied. However, they are extremely site

specific. Channel sounding has been successfully used in the past to evaluate the performance on a system-level.

In [32] sounding data is used to analyse Turbo-MIMO equalization in various propagation conditions and with

multiple users. Within the RESCUE project it is possible to use the same approach to validate and demonstrate

how effective the links-on-the-fly concept is. In similar manner in [33], sounding data is used.

System-level simulations commonly require multiple links being processed/available e.g. in multi-user or multi-

basestation concepts. During the V2V channelsounding campaign in Ilmenau (see chapter 2 and [12]), however,

only a single link, in the sense of single transmitter- and receiver-array was measured at a time. Nevertheless, the

data can be used if coherent links are not required. In order to do that different snapshots, at different receiver

or transmitter locations can be selected to represent different links or different users. Furthermore, the individual

antenna array elements can be used to form different links, however, with the drawback of being rather closely

spaced (in the array) but with the benefit of being coherent.

Within the RESCUE project different toy-scenarios are defined to represent various system layout where the links-

on-the-fly concept is assumed to be of benefit. Consider Figure 6.1a that depicts a sketch of ”toy-scenario 2”. In

(a) Toy-Scenario 2 (b) Wide-Grid locations

Figure 6.1: Example for taking Toy-Scenario 2 from measurement data

”toy-scenario 2” there is one source (”S”), one destination (”D”) and two relay nodes (”R1” and ”R2”). Source

and destination are not connected (no link between them) but the only way for them to communicate is through the

relays. One example of this scenario can be obtained from the measurement data by considering the Tx location

R14 as the source. Now any receiver position or more precise any snapshot corresponding to a receiver location

in the respective measurement file (R14 L14 L1) can be one of the relays. In order to determine the link from the

relays to the destination it is necessary to find a transmitter location different from the source location but where

in the corresponding measurement files the relay positions are included. An example is given in Figure 6.1b. For

easier readability a specific transmitter location (L5 and L11) was chosen to depict the relay position, however, it

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could be any position along the right lane of the street. The locations of source and destination are in this example

however limited to the transmitter location. The measurements where conducted in such a fashion that all links

are ”across the street”. That means if the transmitter is anywhere on the left lane, the receiver always has to be on

the right lane. That’s why the relay positions in Figure 6.1b are both on the left lane. Another degree of freedom

lies in the choice of antenna array elements at the transmitter and receiver. Even in geometrical LOS conditions a

”bad” link can be established by choosing elements that do not face each other.

Recreating ”toy-scenario 2” from the measurement data is the easiest one since it requires only a single link

between nodes. A sketch of ”toy-scenario 1” is depicted in Figure 6.2a. Here only a single relay is present but the



source is connected to both the relay and the destination. It is not possible to directly map this to the measurement

data. If the source is chosen to be a location on the right lane then both relay and destination have to be on the

left lane, since these are the only links measured. However, now it is not possible to have the link from relay to

destination since this would require a link where both nodes are on the left lane. The solution to this problem is to

treat positions at both the left and the right lane to be the same. This is illustrated in Figure 6.2b. The link from

source to destination will be from R7 to L14, the link from source to relay is R7 to L11. The link from relay to

destination is then from L11 to ”R13.5”. The position ”R13.5” is supposed to be at L14 but on the other side of

the street.

Finally, the ”toy-scenario 3” and an example of how to obtain it from the measurement data is shown in Figure 6.3a

and 6.3b. In this case the same method as for ”toy-scenario 2” of sharing positions on opposing sides of the street

is used. An alternative method of dealing with the problem of multi-link nodes could be to have the relays use



shared positions. In that sense the link from source to relays could from R14 to L5 and L11 as in 6.3b. However,

the link from relay to destination can be from ”R5.5” and R11 to the destination at L1. Doing so would at least

guarantee that the links from both relays terminate at the exact same location. The relay can then be thought of as

an ”extended” relay with receiving antenna on the left lane and transmitting antenna on the right lane.

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7. Summary & Conclusion

Within this deliverable detailed channel analysis, parameter derivation and channel model extensions for V2V

wireless links have been provided. Two independent V2V channel sounding campaigns have been used: a Single-

Input Multiple-Output (SIMO) campaign from UOULU and a MIMO one from TUIL. Both data sets have been first

analysed by means of high resolution multipath parameter estimation. Especially to allow for detailed directional

propagation characterization. The TUIL data set has been considered for the statistical analysis of the WINNER

like LSPs, whereby for the first time a double directional analysis has been performed. Furthermore with the TUIL

data sets the local non-stationarity regions have been studied, whereby special focus has been spent to identify the

region by interacting vehicles (e.g. causing shadowing and scattering). The UOULU data sets has been analysed

to derive new inside into the statistical parameters of the multipath clusters.

Important work has been spend for the extension of a geometry based stochastic channel model, the WINNER

model. The goal is to evolve the model to be applicable for VANETs performance evaluation. Two main features

have been detailed: the spatial consistency and moving clusters. A prerequisite is to allow for a mixture of random

and deterministic cluster generation. Within the classical WINNER approach the spatial consistency for closely co-

located users/nodes has been handled at the large scale parameter level and not at the cluster/multipath parameter

level. Since in the V2V case many nodes are closely co-located the spatial consistency must be modelled in a

realistic way: by interpolation of cluster parameters. Furthermore mobile scatterers/clusters (as vehicles) play an

important role in particular for the V2V links, hence this has been introduced to the WINNER model by shadowing

of clusters and creation of new dynamic clusters.

The results of this deliverable are an essential requirement for the subsequent practical work within WP4 and for

system level performances studies at WP2 and WP3 within the RESCUE project. Considering computer simula-

tions the required V2V channel data can be either directly taken from the V2V MIMO channel sounding campaign

or from the extended WINNER channel model. However both can/will also be used for the practical performance

validations inside the Over-The-Air test facility at TUIL. Here a SDR testbed will be used to validate the PHY and

MAC, developed within RESCUE, under realistic conditions within a virtual electromagnetic environment.

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8. References

[1] R.K. Sharma, C. Schneider, W. Kotterman, G. Sommerkorn, P. Große, F. Wollenschlager, G. Del Galdo, M.A.

Hein, and R.S. Thoma. Virtual electromagnetic environment for over-the-air testing of car-to-car and car-to-

infrastructure communication. In General Assembly and Scientific Symposium (URSI GASS), 2014 XXXIthURSI, pages 1–4, Aug 2014.

[2] M.A. Hein, C. Bornkessel, W. Kotterman, C. Schneider, R.K. Sharma, F. Wollenschlager, R.S. Thoma,

G. Del Galdo, and M. Landmann. Emulation of virtual radio environments for realistic end-to-end testing for

intelligent traffic systems. In Microwaves for Intelligent Mobility (ICMIM), 2015 IEEE MTT-S InternationalConference on, pages 1–4, April 2015.

[3] A. F. Molisch, F. Tufvesson, J. Karedal, and C. F. Mecklenbrauker. A survey on vehicle-to-vehicle propaga-

tion channels. Wireless Communications, IEEE Journal on, 16(6):12–22, 2009.

[4] http://projects.celtic-initiative.org/winner+/.

[5] A.F. Molisch, F. Tufvesson, J. Karedal, and C.F. Mecklenbrauker. A survey on vehicle-to-vehicle propagation

channels. Wireless Communications, IEEE, 16(6):12–22, 2009.

[6] H. Boeglen, B. Hilt, P. Lorenz, J. Ledy, A.-M. Poussard, and R. Vauzelle. A survey of v2v channel mod-

eling for vanet simulations. In Wireless On-Demand Network Systems and Services (WONS), 2011 EighthInternational Conference on, pages 117–123, 2011.

[7] Cheng-Xiang Wang, Xiang Cheng, and D.I. Laurenson. Vehicle-to-vehicle channel modeling and measure-

ments: recent advances and future challenges. Communications Magazine, IEEE, 47(11):96–103, 2009.

[8] David W. Matolak. Modeling the vehicle-to-vehicle propagation channel: A review. Radio Science,

49(9):721–736, 2014.

[9] L. Raschkowski, et al. Deliverable D1.4, METIS Channel Models V1.0, ICT-317669-Mobile

and wireless communications Enablers for the Twenty-twenty Information Society (METIS).

(https://www.metis2020.com/wp-content/uploads/deliverables/METIS D1.4 v2.pdf), March 2015.

[10] P. Große, C. Schneider, G. Sommerkorn, and R. Thoma. A hybrid channel model based on winner for

vehicle-to-x application. In COST IC1004 TD(13)07040, Ilmenau, Germany, May 28-31 2013.

[11] A. Roivainen, P. Jayasinghe, J. Meinila, V. Hovinen, and M. Latva-aho. Vehicle-to-vehicle radio channel

characterization in urban environment at 2.3 ghz and 5.25 ghz. In pimrc, pages 37–41, Washington DC,

USA, September 2014.

[12] Report on V2V Channel Measurement Campaign. Technical Report ICT-619555 RESCUE D4.2.

[13] A. Richter. On the Estimation of Radio Channel Parameters: Models and Algorithms (RIMAX), Doctoral

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