ETSI TR 103 257-1 V1.1.1 (2019-05)

Intelligent Transport Systems (ITS); Access Layer; Part 1: Channel Models for the 5,9 GHz frequency band

DTR/ITS-00437-1

General Information

• Status: Published
• Publication Date: 28-May-2019
• Technical Committee: ITS WG4 - Media and Medium related
• Current Stage: 12 - Completion
• Due Date: 13-May-2019
• Completion Date: 29-May-2019

Standards Content (Sample)

TECHNICAL REPORT
Intelligent Transport Systems (ITS);
Access Layer;
Part 1: Channel Models for the 5,9 GHz frequency band

2 ETSI TR 103 257-1 V1.1.1 (2019-05)

Reference
DTR/ITS-00437-1
Keywords
ITS, radio, V2X
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3 ETSI TR 103 257-1 V1.1.1 (2019-05)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 8
3.1 Terms . 8
3.2 Symbols . 8
3.3 Abbreviations . 8
4 Introduction . 10
4.1 Wave propagation. 10
4.2 Common channel models . 11
4.3 Usage of channel models . 13
4.4 The 5,9 GHz frequency band and V2X communication . 13
4.5 Scenarios . 13
4.5.1 Introduction. 13
4.5.2 Urban . 14
4.5.3 Rural . 15
4.5.4 Highway . 15
4.5.5 Tunnels . 15
4.5.6 LOS probability . 16
4.6 Summary . 16
5 Channel models . 16
5.1 Introduction . 16
5.2 Path loss models . 16
5.2.1 Free space path loss model . 16
5.2.2 Two-way ground reflection model . 17
5.2.3 Log-distance path loss model. 18
5.3 Tapped delay line model . 18
5.4 Geometry-based stochastic channel model . 19
5.4.1 Introduction. 19
5.4.2 General parameters . 19
5.4.2.1 Introduction . 19
5.4.2.2 Step 1 - Set scenario . 20
5.4.2.3 Step 2 - LOS/NLOS/NLOSv . 20
5.4.2.4 Step 3 - Path loss and shadowing . 21
5.4.2.4.1 Path loss models and vehicle blockage loss. 21
5.4.2.4.2 Shadow fading . 22
5.4.2.5 Step 4 - Large scale correlated scatterers . 23
5.4.3 Small scale parameters . 25
5.4.3.1 Introduction . 25
5.4.3.2 Step 5 - Generate delays . 25
5.4.3.3 Step 6 - Generate cluster powers . 26
5.4.3.4 Step 7 - Generate arrival and departure angles . 26
5.4.3.5 Step 8 - Perform random coupling of rays . 28
5.4.3.6 Step 9 - Generate XPRs . 28
5.4.4 Coefficient generation . 28
5.4.4.1 Introduction . 28
5.4.4.2 Step 10 - Draw initial random phases . 29
5.4.4.3 Step 11 - Generate channel coefficients . 29
5.4.4.4 Step 12 - Apply path loss and shadowing . 31
5.5 Channel Models for Link Level Simulations. 31
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4 ETSI TR 103 257-1 V1.1.1 (2019-05)
5.5.1 Cluster Delay Line Models . 31
5.5.1.1 Introduction . 31
5.5.1.2 CDL parameters for LLS . 32
5.5.2 Map-based hybrid channel model (Alternative channel model methodology) . 34
5.5.2.1 Overview . 34
5.5.2.2 Coordinate system . 34
5.5.2.3 Scenarios . 34
5.5.2.4 Antenna modelling . 34
5.5.2.5 Channel generation. 35
5.5.2.5.1 Introduction . 35
5.5.2.5.2 Step 1: Set environment and import digitized map . 35
5.5.2.5.3 Step 2: Set network layout and antenna array parameters . 35
5.5.2.5.4 Step 3: Apply ray-tracing to each pair . 36
5.5.2.5.5 Step 4: Generate large scale parameters . 37
5.5.2.5.6 Step 5: Generate delays for random clusters . 37
5.5.2.5.7 Step 6: Generate powers for random clusters . 38
5.5.2.5.8 Step 7: Generate arrival angles and departure angles . 38
5.5.2.5.9 Step 8: Merge deterministic clusters and random clusters . 39
5.5.2.5.10 Step 9: Generate ray delays and ray angle offsets . 40
5.5.2.5.11 Step 10: Generate power of rays in each cluster . 41
5.5.2.5.12 Step 11: Generate XPRs . 41
5.5.2.5.13 Step 12: Draw initial random phases . 41
5.5.2.5.14 Step 13: Generate channel coefficients . 41
Annex A: Mahler model for tracking multipath components . 44
Annex B: LOS probability and transition probability curves . 45
Annex C: Coordinate system . 48
C.1 Definition . 48
C.2 Local and global coordinate systems . 48
C.3 Transformation from a LCS to a GCS . 48
C.4 Transformation from an LCS to a GCS for downtilt angle only . 52
Annex D: Ensuring Spatial consistency in GBSCM models . 54
Annex E: Bibliography . 57
History . 59

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5 ETSI TR 103 257-1 V1.1.1 (2019-05)
Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The information
pertaining to these essential IPRs, if any, is publicly available for ETSI members and non-members, and can be found
in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to ETSI in
respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the ETSI Web
server (https://ipr.etsi.org/).
Pursuant to the ETSI IPR Policy, no investigation, including IPR searches, has been carried out by ETSI. No guarantee
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Foreword
This Technical Report (TR) has been produced by ETSI Technical Committee Intelligent Transport Systems (ITS).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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6 ETSI TR 103 257-1 V1.1.1 (2019-05)
1 Scope
The present document provides a set of channel models describing how signals in the 5,9 GHz frequency band are
perturbed by the mobile radio environment in different use cases.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] I. Tan, W. Tang, K. Labertaux and A. Bahai: "Measurement and analysis of wireless channel
impairments in DSRC vehicular communications", in Proc. Of International Conference on
Communications (ICC '08), Beijing, China, May 2008, pp. 4882-4888. .
[i.2] P. Alexander, D. Haley, and A. Grant: "Cooperative intelligent transport systems: 5.9 GHz field
trials", in Proceedings of the IEEE, vol. 99, no. 7, pp. 1213-1235, July 2011. .
[i.3] L. Bernado, T. Zemen, F. Tufvesson, A. F. Molisch and C. F. Mecklenbräuker: "Delay and
Doppler spreads of non-stationary vehicular channels for safety relevant scenarios", in IEEE
Transactions on Vehicular Technology, vol. 63, no. 1, pp. 82-93, January 2014. .
[i.4] T. S. Rappaport, Wireless Communications: "Principles and Practice", Prentice Hall, 1996.
[i.5] M. Boban, J. Barros and O. Tonguz: "Geometry-based vehicle-to-vehicle channel modeling for
large-scale simulation", in IEEE Transactions on Vehicular Technology, vol. 63. No. 9,
pp. 4146-4164, November 2014.
[i.6] M. Boban, T. T. V. Vinhoza, M. Ferreira, J. Barros and O. K. Tonguz: "Impact of vehicles as
obstacles in vehicular ad hoc networks", in IEEE Journal on Selected Areas in Communications,
vol. 29, no. 1, pp. 15-28, January 2011.
[i.7] K. Mahler, W. Keusgen, F. Tufvesson, T. Zemen and G. Caire: "Measurement-Based Wideband
Analysis of Dynamic Multipath Propagation in Vehicular Communication Scenarios", in IEEE
Transactions on Vehicular Technology, October 2016.
[i.8] M. Boban, W. Viriyasitavat and O.K. Tonguz: "Modeling vehicle-to-vehicle line of sight channels
and its impact on application-layer performance", in Proceeding of the 10th ACM international
workshop on Vehicular inter-networking, systems, and applications (VANET 13), Taipei, Taiwan,
June 2013, pp. 91-94. .
[i.9] J. Karedal, N. Czink, A. Paier, F. Tufvesson and A. Molisch: "Path loss modelling for vehicle-to-
vehicle communications", in IEEE Transactions on Vehicular Technology, vol. 60, no. 1,
pp. 323-328, January 2011.
[i.10] Recommendation ITU-R P.526: "Propagation by diffraction", International Telecommunication
Union Radiocommunication Sector, Geneva, November 2013. .
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7 ETSI TR 103 257-1 V1.1.1 (2019-05)
[i.11] M. Boban, X. Gong and W. Xu: "Modeling the evolution of line-of-sight blockage for V2V
channels", in Proceedings of the 2016 IEEE 84th Vehicular Technology Conference
(VTC2016-Fall), Montréal, Canada, September 2016, pp. 1-6.
[i.12] ETSI TR 102 638 (V1.1.1) (2009-06): "Intelligent Transport Systems (ITS); Vehicular
Communications; Basic Set of Applications; Definitions".
[i.13] ETSI TR 138 901 (V14.0.0) (2017-03): "5G; Study on channel model for frequencies from 0.5 to
100 GHz (3GPP TR 38.901 version 14.0.0 Release 14)".
[i.14] B. Aygun, M. Boban, J.P. Vilela and A.M. Wyglinski: "Geometry-Based Propagation Modeling
and Simulation of Vehicle-to-Infrastructure Links", in Proceedings of the 2017 IEEE 83th
Vehicular Technology Conference (VTC2016-Spring), Nanjing, China, May 2016, pp. 1-5.
[i.15] T. Mangel, O. Klemp and H. Hartenstein: "5.9 GHz inter-vehicle communication at intersections:
a validated non-line-of-sight path-loss and fading model", in EURASIP Journal on Wireless
Communications and Networking 2011, 2011:182 DOI: 10.1186/1687-1499-2011-182.
[i.16] WINNER Project Board: "D5.4 v1.4 - Final Report on Link Level and System Level Channel
Models", 18 November 2005.
NOTE: Available at https://www.researchgate.net/publication/229031750_IST-2003-
507581_WINNER_D5_4_v_14_Final_Report_on_Link_Level_and_System_Level_Channel_Models.
[i.17] WINNER Project Board: "D1.1.2 V1.2 - WINNER II Channel Models", 30 09 2007.
NOTE: Available at https://www.cept.org/files/8339/winner2%20-%20final%20report.pdf.
[i.18] WINNER Project Board: "D5.3 - WINNER+ Final Channel Models", 30 06 2010.
NOTE: Available at https://www.researchgate.net/publication/261467821_CP5-
026_WINNER_D53_v10_WINNER_Final_Channel_Models.
[i.19] 3GPP TR 37.885 (2018-05): "Study on evaluation methodology of new Vehicle-to-Everything
V2X use cases for LTE and NR".
[i.20] 3GPP TR 36.885: "Study on LTE-based V2X services".
[i.21] L. Liu, C. Oestges, J. Poutanen, K. Haneda, P. Vainikainen, F. Quitin and P. De Doncker: "The
COST 2100 MIMO channel model", in IEEE Wireless Communications, vol. 19, no. 6, pp. 92-99,
December 2012.
[i.22] RESCUE project deliverable D4.3: "Report on channel analysis and modeling".
NOTE: Available at https://cordis.europa.eu/docs/projects/cnect/5/619555/080/deliverables/001-
D43v10FINAL.pdf.
[i.23] M. Walter, D. Shutin and U.-C. Fiebig: "Delay-Dependent Doppler Probability Density Functions
for Vehicle-to-Vehicle Scatter Channels", in IEEE Transactions on Antennas and Propagation,
vol. 62, no. 4, pp. 2238-2249, April 2014.
[i.24] M. Walter, T. Zemen and D. Shutin: "Empirical relationship between local scattering function and
joint probability density function", 2015 IEEE 26th Annual International Symposium on Personal,
Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, 2015, pp. 542-546.
[i.25] H. Friis: "A note on a simple transmission formula", in Proceedings of the IRE, vol. 34, no. 5,
pp. 254-256, May 1946.
[i.26] Glassner, A S: "An introduction to ray tracing", Elsevier, 1989.
[i.27] R.G. Kouyoumjian and P.H. Pathak: "A uniform geometrical theory of diffraction for an edge in a
perfectly conducting surface", in Proceedings of IEEE, vol. 62, no. 11, pp. 1448-1461,
November 1974.
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[i.28] P. H. Pathak, W. Burnside and R. Marhefka: "A Uniform GTD Analysis of the Diffraction of
Electromagnetic Waves by a Smooth Convex Surface", in IEEE Transactions on Antennas and
Propagation, vol. 28, no. 5, pp. 631-642, 1980.
[i.29] J. W. McKown, R. L. Hamilton: "Ray tracing as a design tool for radio networks", in IEEE
Network, vol. 5, no. 6, pp. 27-30, November 1991.
[i.30] ICT-317669-METIS/D1.4: "METIS channel model", METIS 2020, February 2015.
[i.31] W. Tomasi: "Electronic Communication Systems - Fundamentals Through Advanced", Pearson.
pp. 1023.
[i.32] J. Deygout: "Multiple knife-edge diffraction of microwaves", in IEEE Transactions on Antennas
and Propagation, vol. 14, no. 4, 1966, pp .480-489.
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
For the purposes of the present document, the following symbols apply:
G Antenna Gain Receiver
RX
G Antenna Gain Transmitter
TX
K Ricean K factor
R
L parameter denoting the number of random clusters
RC
L parameter denoting the number of deterministic clusters
RT
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AOA Angle of Arrival
AOD Angle of Departure
B2R Base station to road side unit
C-ITS Cooperative ITS
DUT Device Under Test
ITS Intelligent Transport Systems
LOS Line-Of-Sight
LSP Large-Scale-Parameters
MAC Medium Access Control
MD Mobile Discrete
MPC MultiPath Component
NGSM Non-Geometry Stochastic Model
NLOS Non-LOS caused by objects other than vehicles
NLOSv Non-LOS caused by vehicles
P2B Pedestrian to base station
P2P Pedestrian to pedestrian
PAS Power angular spread
PDF Probability Density Function
PL Path Loss
R2R Road side unit to road side unit
RMS Root Mean Squared
RSU Road side unit
RX Receiver
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9 ETSI TR 103 257-1 V1.1.1 (2019-05)
SD Static Discrete
SF Shadow Fading
TDL Tapped Delay Line
TX Transmitter
US Uncorrelated Scatterers
V2B Vehicle to base station
V2I Vehicle-to-Infrastructure
V2P Vehicle to pedestrian
V2V Vehicle-to-Vehicle
V2R Vehicle to road side unit
V2X Vehicle-to-X
VANET Vehicular ad hoc networks
WSS Wide Sense Stationary
XPR Cross Polarization power Ratios
ZOA Zenith angles Of Arrival
ZOD Zenith angles Of Departure
ZSD Zenith angle Spreads at Departure
GBDM Geometry-based deterministic model
NS Network Simulator
PHY Physical Layer
FSPL Free Space Path Loss
GBSCM Geometry-based stochastic channel model
SLS System Level Simulations
LLS Link Level Simulations
DS Delay Spread
ASA Azimuth angle Spread of Arrival
ASD Azimuth angle Spread of Departure
ZSA Zenith angle Spread of Arrival
FIR Finite Impuls Response
BS-UT Base Station-User Terminal
GCS Global Coordinate System
CDL Cluster Delay Line
SCM Stochastic Channel Model
NR New Radio
UTD Uniform Theory of Diffraction
TOA Time Of Arrival
BS Base Station
UT User Terminal
LCS Local Coordinate System
PDP Probability Density Plot
WIM WINNER Channel Model
WIM-SC WIM-Spatial Consistency
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10 ETSI TR 103 257-1 V1.1.1 (2019-05)
4 Introduction
4.1 Wave propagation
Channel models, also called propagation models, are an important part when designing and evaluating wireless systems
from the reception at the antenna all way up to the end user application. Channel models aim at mimic the perturbation
signals undergo when travelling between transmitter (TX) and receiver (RX). The different effects that can be seen in a
wireless channel are attenuation, reflection, transmission, diffraction, scattering, and wave guiding. The signal strength
is decaying as the distance increases between TX and RX, i.e. the signal gets attenuated. Wave guiding is an effect that
actually preserves the signal strength due to the fact that the signal is restricted in its expansion. It can occur for
example in urban canyons and tunnels. In Figure 1, reflection, transmission, scattering, and diffraction, are illustrated.
Reflection occurs on smooth surfaces, whereas transmission is when the signal penetrates the object. Scattering spreads
the signal in several directions, which occurs on rough surfaces, and diffraction is when the signal is bending around a
sharp edge. Smooth, rough, large, and small, are all relative to the wavelength in question. Increased carrier frequency
implies smaller wavelength (e.g. 5,9 GHz is equal to a wavelength of 5 cm), more optical propagation, smaller
antennas, and higher attenuation (the signal strength is decaying faster with distance).
Reflection
Scattering
Diffraction
Transmission
Figure 1: Different effects on the signal: transmission, reflection, scattering and diffraction
In wireless channels several replicas of the same signal can reach RX, which have bounced off different objects during
propagation; and if TX and/or RX are moving there will be Doppler effects. This is relative movement of the TX/RX
that shifts the frequency of the signal and makes it different at the receiver from the one that was originally transmitted.
Figure 2 provides an example where RX receives one line-of-sight (LOS) component and two replicas of the signal that
have bounced off objects (a multipath scenario).
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11 ETSI TR 103 257-1 V1.1.1 (2019-05)
Building Building
MPCs
TX RX
Line-of-sight
MPC = Multipath component
Figure 2: Multipath scenario, where several replicas
of the signal besides the LOS component reach RX
The multipath components (MPC) will travel longer distances and will therefore arrive later than the LOS component.
These delayed copies of the signal give rise to self-interference at RX, which could be constructive or destructive, see
Figure 3. The worst case of destructive interference is when two equally strong signals are shifted 180 degrees (see
Figure 3(b)).
+ = + =
(a) Constructive interference (b) Destructive interference

Figure 3: Constructive (a) and destructive self-interference (b)
4.2 Common channel models
There is a diverse set of channel models, which increase in complexity when more details about the propagation
environment are added. The simplest channel models are deterministic path loss models, where the attenuation of the
signal is based on a predetermined formula using the carrier frequency and distance between TX and RX as input. In
other words, this kind of models will always result in the same result when the frequency and the distance is the same.
Two well-known path loss models are the free-space path loss model and the two-ray ground reflection model.
Free-space only assumes a LOS component, whereas the two-ray ground reflection model is consisting of one LOS
component and one ground reflection (one MPC). There exist more advanced path loss models where parameters are
derived from real-world channel measurements for the LOS as well as for the situation when the LOS is blocked by
another vehicle or building.
A path loss model is always present regardless of how complex the channel model is, since this deterministically
decides the signal strength based on TX-RX separation, carrier frequency, and possible obstruction of LOS component.
Path loss models suitable for V2X communication are further detailed in clause 5.2.
Statistical models add a fading component to the path loss model. Fading is the fluctuation of the signal strength and it
is often modelled as a random process. Fading could either be due to multipath propagation (a.k.a. small-scale fading)
or shadowing from obstacles affecting the propagation (a.k.a. large-scale fading). Small-scale fading is due to multipath
propagation effect as mentioned earlier (see Figure 2) and gives rise to a certain amount of either constructive or
destructive self-interference (see Figure 3). If there is a LOS component, this is usually very dominant since this
contains the most energy compared to other copies of the signal (MPCs). Large-scale fading captures fluctuations on a
larger scale above 10 wavelengths as opposed to small-scale fading, which is within a wavelength.
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12 ETSI TR 103 257-1 V1.1.1 (2019-05)
Well-known statistical models for small-scale fading are Rayleigh, Rician, and Nakagami. In short, Rician distribution
is used when the communication contains a LOS component and Rayleigh in absence of LOS. Nakagami captures both
when there is a LOS and when this is absent. Nakagami is often used for protocol simulations of vehicular networks.
Large-scale fading (shadowing) is very often represented by a Gaussian process.
In tapped delay line (TDL) models, individual MPCs are treated separately. Each MPC ("tap") will have its own fading
statistics (e.g. Rician, Rayleigh) and phase shift, to cover phase differences between MPCs. Each tap may feature an
individual Doppler spectrum. TDLs add better accuracy to the channel model by treating arriving MPC individually
compared to when only using for example a Rician fading model (which could be regarded as only one signal hitting
the RX). However, TDLs do not address the specific environment surrounding the vehicle such as buildings or objects
that appear in different scenarios (however, a TDL could be tailored to a specific scenario).
TDLs and statistical models belong to the group of channel models that is called non-geometry based stochastic channel
models (NGSM), which describe the paths between TX and RX by only statistical parameters without reference to the
geometry.
Geometry-based stochastic channel models (GBSCM), on the other hand, also account for the environment such as
buildings and vehicles, which are denoted scatterers. The geometry of the propagation environment is randomly
generated according to specified statistical distributions. Dedicated vehicular radio channel measurements at 5 GHz
show that the main contributions to the signal reception are LOS, deterministic scattering, and diffuse scattering
components. The LOS component has high gain as long as there is a direct path from TX to RX. The LOS component's
gain decreases whenever an interacting object obstructs the direct path (shadowing). The diffuse scattering contribution,
stemming from surrounding buildings, other structures along the road, or foliage, forms a fairly large fraction of the
overall channel gain.
Geometry-based deterministic model (GBDM) uses pure ray tracing or ray launching to determine the channel's
characteristics. It needs 2.5D or 3D building data to search for all possible paths from TX to RX to find transmissions,
reflections, diffraction, and scattering objects. Its result is deterministic. Searching for propagation paths is complex and
it is computationally expensive. The complexity increases dramatically with the order of transmission and reflection, i.e.
the number of possible interactions with objects. With increasing frequency band (decreasing wave length) the accuracy
and hence reliability of ray tracing or launching based models decrease since impacts from material parameterization
and small object detail modelling becomes more pronounced. Therefore, it is not a good choice to use it for a general
channel model. Anyway, its deterministic approach can be used to create/parameterize new channel models as an
alternative to time consuming real-world measurement campaigns. However, ray tracing allows for investigation of
critical situations in which a statistical approach is not sufficient.
Table 1 summarizes the mentioned channel models and what aspects of the channel impairments each class of models
try to address.
Table 1: Summary and description of different channel models
Channel model Path loss Fading Doppler Environment Description
Path loss models are integral in all channel
models describing the deterministic signal
Path loss X
attenuation based on TX-RX distance and
carrier frequency.
Adds a fading component (both small-scale
Statistical models X X  and large-scale) to the path loss. Models only
one received signal component.
Models several MPCs individually using
TDL X X X statistics but can also add Doppler effects due
to speed differences between TX and RX.
Addresses also the environment by modelling
potential scatterers according to statistical
Geometry-based
distributions which affect MPCs. Further, it
stochastic X X X X
addresses also the temporal evolution of the
models
channel and thus considers correlations in time
and space.
Addresses the whole propagation environment
in a deterministic way by generating each MPC
Geometry-based
and its interaction with the environment
deterministic X X X X
(including for example material of buildings,
models
street signs, foliage, etc.). Very scenario
specific and computational expensive.

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4.3 Usage of channel models
A channel model is selected based on what part of the communication system that is going to be studied. For network
level simulations, where communication protocols including medium access control (MAC) are studied, a statistical
model (e.g. Rician, Rayleigh, and Nakagami) is the predominant channel model type to keep computational time down.
These simulations usually consist of many vehicles to stress the network and protocols and to find weaknesses of the
system as a whole. Well-known network simulators for vehicular ad hoc networks (VANET) are NS-2, NS-3, Veins,
OMNET++, and OPNET. Statistical channel models found in the literature for VANETs are parameterized for specific
scenarios such as urban and highway.
For physical (PHY) layer simulations more details about the channel is necessary to understand how a certain PHY is
affected by for example delay and Doppler spreads due to multipath propagation. More details about the scenario itself
needs to be present in PHY layer simulations. TDLs and geometry-based stochastic and deterministic channel models
are for obvious reasons the preferred channel models for this kind of simulations.
4.4 The 5,9 GHz frequency band and V2X communication
The 5,9 GHz band is a challenging frequency band for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)
communications, collectively known as V2X communication, due to the high carrier frequency resulting in a
wavelength of 5 cm. This frequency band provides a rich multipath environment especially in urban areas (many MPCs
will arrive at RX). The LOS will often be blocked by other vehicles or buildings since the antennas are approximately
on the same height especially in the V2V case. This results in many scatterers (both static and mobile) affecting the
wave propagation especially in urban scenarios. Further, in highway scenarios high relative speeds can be achieved
resulting in high Doppler. For communication with smart infrastructure (V2I), one node might be stationary and the
antenna might be elevated resulting in a slightly better reception environment for moving vehicles but this is totally
dependent on what kind of smart infrastructure that has been V2X enabled. The propagation channel for V2X is
difficult to resemble due to the rich multipath environment.
4.5 Scenarios
4.5.1 Introduction
The selected V2X scenario has a major impact on the wave propagation and thus the channel model. There are three
major scenarios: urban, rural, and highway, with the special case of tunnels. As the vehicle density increases in the
different scenarios, the probability for the blockage of the LOS component increases and then strong MPCs needs to
contribute to a successful reception of a transmission. Good reflectors are street signs and scatterers that are made of
metallic structures with a smooth surface. However, good reflectors that are too far away can also cause a large delay
spread resulting in inter-symbol interference and decoding problems when LOS is blocked. Delay spread is the delay
between the first signal component arriving at the receiver and the last for a given symbol that is transmitted. Higher
vehicle speeds can result in Doppler effect. In Figure 4, the scenarios detailed in subsequent clauses are illustrated.

Figure 4: V2X scenarios
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4.5.2 Urban
The urban scenario is defined as single-lane or multi-lane city streets used either for one-way or for two-way traffic in
densely populated areas. There can be road signs, streetlights, traffic signs and traffic signals, single- to multi-story
buildings situated along the roadsides and a multitude of traffic. The propagation channel in such a scenario is
considered to have a rich scattering environment since there are many objects (both static as well as mobile) affecting
the wave propagation and the antennas are situated at almost the same height close to the ground. The urban scenario
will have a higher probability for blockage of the LOS component. Thus the successful reception of transmissions is
based on strong MPCs. The Doppler effect will be small or zero since vehicles move with modest speeds in this
scenario.
Intersections in urban environments are challenging and these are important from a safety point of view to avoid
collisions between vehicles coming from perpendicular streets. In Figure 5, an urban crossing is depicted and the LOS
component is missing due to the building, therefore, the communication has to rely on strong reflections (MPCs).

Figure 5: Urban crossing scenario with buildings
An urban approaching scenario is depicted in Figure 6. The signal is reflected by buildings and other objects such as
vehicles but a LOS component exists.

Figure 6: Urban approaching scenario with possible scatterers
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4.5.3 Rural
A rural scenario is characterized by a country road with open surroundings, i.e. with little to no objects along the
roadside, usually made up of single lanes with two-way traffic. Due to a lower density of the scattering objects, such as
other vehicles, buildings and large fences in the surroundings, the experienced delay from MPCs (delay spread) are
typically lower than that in the other scenarios. Depending on the speed difference of TX and RX, a Doppler effect
might kick-in. The lack of scatterers and when foliage is present close to the road can cause problem with successful
reception when the LOS component is missing. The rural scenario is depicted in Figure 7.

Figure 7: Rural scenario representing a very open environment with few scatterers
4.5.4 Highway
A highway scenario is characterized by a road with two - or more - lanes reserved usually for one-way traffic.
Moreover, the maximum allowed driving speeds can vary between 120 km/h to 140 km/h (and in some cases there is no
limit at all). For that reason, the Doppler spreads experienced in highway scenario can be very high. Typical scattering
objects in the surrounding are metallic guardrails, sound-berms (material properties can vary), overhead road signs and
bridges, and constructions situated usually a few hundred meters away from the roadside. The density of metallic
scatterers is higher than that in the rural scenario. In extreme cases, the delay spread can be large due to the presence of
metallic road signs above the road situated further away. Figure 8 depicts a highway LOS scenario with possible
scatterers.
Figure 8: Highway scenario with possible scatterers
Entering the highway or merging several lanes coming from separate directions might block the LOS component due to
foliage, slope, orientation of terrain, or the presence of barriers and buildings. Due to the blockage and presence of few
good reflectors can make this particular instance of the highway scenario challenging. Figure 9 depicts a highway
scenario where trucks are obstructing the LOS component of the signal.

Figure 9: Highway scenario where LOS is obstructed
4.5.5 Tunnels
Tunnel is a scenario characterized by a road within a tunnel with two or more lanes that can be allocated for one-way
traffic only. The propagation channel in a tunnel is considered to have very rich scattering from the ground, walls, roof
and metallic structure for the ventilation. In certain situations, depending on the interior material of the tunnel, wave
guiding can be experienced.
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4.5.6 LOS probability
The blockage of the LOS component can be modelled statistically over time and space for V2V. When a blockage of
LOS occurs between a specific TX-RX pair, this blockage might be persistent caus
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