BRNO UNIVERSITY OF TECHNOLOGY
VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
FACULTY OF ELECTRICAL ENGINEERING AND
COMMUNICATION
FAKULTA ELEKTROTECHNIKY
A KOMUNIKAČNÍCH TECHNOLOGIÍ
DEPARTMENT OF RADIO ELECTRONICS
ÚSTAV RADIOELEKTRONIKY
INTRA- AND OUT-OF-VEHICLE CHANNEL
MEASUREMENTS AND MODELING
MĚŘENÍ A MODELOVÁNÍ KANÁLŮ UVNITŘ A VNĚ VOZIDEL
DOCTORAL THESIS
DIZERTAČNÍ PRÁCE
AUTHOR
Pavel Kukolev
AUTOR PRÁCE
SUPERVISOR
ŠKOLITEL
BRNO 2016
prof. Ing. Aleš Prokeš, Ph.D.
ABSTRACT
The dissertation is focused on channel measurements and modeling for vehicle-to-X
communication and on localization. In order to realize an integrated intelligent transportation system (ITS), it is important to estimate channel features for intra-vehicle and
out-of-vehicle scenarios. For this propose the following activities are carried out: simulation of the 802.11p PHY; comparison with 802.11a; channel measurements for different
scenarios based on the 802.11p and ultra-wideband (UWB); creating channel models for
802.11p and UWB; UWB measurements to assess performance of localization.
• The vehicle-to-X communication is supposed on the IEEE 802.11p standard. The
dissertation presents the differences between IEEE 802.11a and IEEE 802.11p
physical layer standards through the simulation results of the transmission over
a HIPERPLAN/2 channel. Further, the simulation of the 802.11p signal transmission over ITU-R M.1225 channel, which includes pedestrian and vehicle models
with different relative delays and average power, is presented. The influence of the
channel on the signal is analyzed using MATLAB simulation in terms of bit error
rate (BER).
• The dissertation reports vehicular channel measurements in the frequency band of
5.8 GHz for IEEE 802.11p standard and for UWB (3-11 GHz). Experiments for
both intra-vehicle and out-of-vehicle environments are carried out. It was observed
that the large-scale variations (LSVs) of the power delay profiles (PDPs) can be
best approximated through a two-term exponential decay model for the 802.11p
protocol, in contrast to the Saleh-Valenzuela (S-V) model which is suitable for
UWB systems.
• For each measurement, the LSV trend was used to construct the respective channel
impulse response (CIR). Next, the CIR is used in 802.11p simulation to evaluate
the BER performance, following a Rician model. The results of the BER simulation shows the suitability of the protocol for in-car as well as out-of-car wireless
applications. The simulation for out-of-car parameters indicate that the error performances do not vary much and it is possible to determine an average BER curve
for the whole set of data.
• The randomness in UWB channel for small positional variations around a car,
parked in an underground garage, is reported. The path loss (PL) is found to be
monotonically increasing with distance but varies randomly with angle and height
and thereby renders signal strength based ranging inaccurate for such scenarios.
On the other hand, arrival time of the first ray can be used for reliable estimation of
distance, independent on transmitter angle or height. The number of clusters in the
PDP is reduced with distance but the nature of the profile remains fairly consistent
with angle. The S-V model parameters also vary with distance and height but their
average values are close to the IEEE 802.15.3 recommended channel model.
• For localization applications the distance between the antennas is calculated exploiting the linear dependence of distance on delay from PDP. The coordinates of
a transmitting antenna are found with the help of two receiving antennas following a two-dimensional (2-D) time-of-arrival (TOA) based localization technique.
A comparison of the calculated coordinates with the original ones exhibits an error of less than 6% which supports the suitability of the proposed approach for
localization of the cars.
KEYWORDS
Intra-vehicle channel; Out-of-vehicle channel; IEEE 802.11p; Channel measurement;
Ultra-wideband; Power delay profile; Bit error rate; Saleh-Valenzuela model; Localization; Time of arrival.
ABSTRAKT
Disertační práce je zaměřena na měření a modelování kanálu uvnitř a vně vozidla pro
komunikaci a lokalizaci. Pro účely vytvoření integrovaného inteligentního dopravního
systému ITS (Intelligent transportation system) je důležitý odhad vlastnosti kanálů pro
vnitřní a venkovní scénáře. Za tímto účelem je vhodné provést řadu činností, které jsou
obsahem disertační práce: Simulace fyzické vrstvy 802.11p, její srovnávání s 802.11a,
měření kanálu pro různé scénáře pro 802.11p a pro širokopásmový systém (UWB),
vytvoření modelů kanálů pro 802.11p a UWB a výzkum vlastností lokalizace založené na
měření v pásmu UWB.
• Výzkum komunikace vozidla s okolím založená na IEEE 802.11p standardu. Jedním z cílů disertační práce je ukázat rozdíly mezi standardy fyzické vrstvy IEEE
802.11a a IEEE 802.11p prostřednictvím simulace s použitím modelu kanálu
HIPERPLAN/2. V práci je uvedena simulace přenosu signálu 802.11p kanálem
ITU-R M.1225 s odlišným zpožděním a středním výkonem (pro chodce a vozidla).
Vliv kanálu na signál je analyzován za použití simulace v prostředí MATLABu
pomocí vyhodnocení chybovosti.
• Určení vlastností kanálů v kmitočtovém pásmu 5,8 GHz pro standard IEEE 802.11p
a UWB. Experimenty byly prováděny pro vnitřní a vnější prostředí vozidla. Bylo
zjištěno, že pro protokol 802.11p může být trend (dlouhodobý vývoj) profilu PDP
(power delay profile) nejlépe aproximován pomocí modelu obsahujícího dvě klesající
exponenciální funkce, na rozdíl od Saleh-Valenzuelova (S-V) modelu, který je více
vhodný pro UWB systémy pracující v pásmu 3 až 11 GHz.
• Vytvoření odpovídající impulzní odezvy (CIR) s využitím trendu PDP. Informace
o CIR byla použita pro simulaci 802.11p za účelem vyhodnocení chybovosti při
použití Ricianova modelu. Výsledky odhadu BER ukazují vhodnost protokolu pro
vnitřní a vnější prostředí bezdrátových aplikací. Výsledky simulací dále ukazují, že
se chybovost zásadně nemění a proto je možné určit střední křivku BER pro celou
sadu změřených dat.
• Určení vlivu malé změny polohy antény na vlastnosti kanálu. Práce ukazuje náhodnost parametrů UWB kanálu pro malé změny polohy antény okolo vozidla, zaparkovaného v podzemní garáži. Ztráty šířením jsou monotónně rostoucí se vzdáleností,
avšak náhodně se mění v závislosti na úhlu a výšce antén, a proto je vyhodnocení vzdálenosti pomocí síly signálu pro tyto scénáře nevhodné. Na druhé straně
může být pro spolehlivé určení vzdálenosti bez ohledu na úhel nebo výšku antény
použita doba příchodu prvního svazku. Ověření vlivu změn konfigurace kanálu na
parametry S-V modelu. Práce demonstruje závislost parametrů Saleh-Valenzuela
modelu v na vzdálenosti a výšce antén, avšak ukazuje, že jejich průměrné hodnoty
jsou blízké IEEE 802.15.3 standardu.
• Ověření možnosti lokalizace pomocí metody TOA (time of arrival). Vzdálenost
mezi anténami byla určena z profilu PDP s využitím lineární závislosti vzdálenosti
na zpoždění. Souřadnice vysílací antény byly nalezeny pomocí dvou přijímacích
antén pomocí 2-D lokalizační techniky TOA. Porovnání vypočtených souřadnic
s původními vykazuje chybu menší než 6%, což ukazuje vhodnost navrženého
přístupu pro lokalizaci vozidel.
KLÍČOVÁ SLOVA
Kanal uvnitř vozidel; Kanal ve vozidel; IEEE 802.11p; Měření kanalu; UWB; PDP; BER;
Saleh-Valenzuelova model; Localizace; TOA.
KUKOLEV, Pavel Intra- and out-of-vehicle channel measurements and modeling: doctoral thesis. BRNO: Brno University of Technology, Faculty of Electrical Engineering
and Communication, Department of Radio Electronics, 2016. 80 p. Supervised by prof.
Ing. Aleš Prokeš, Ph.D.
DECLARATION
I declare that I have written my doctoral thesis on the theme of “Intra- and out-of-vehicle
channel measurements and modeling” independently, under the guidance of the doctoral
thesis supervisor and using the technical literature and other sources of information which
are all quoted in the thesis and detailed in the list of literature at the end of the thesis.
As the author of the doctoral thesis I furthermore declare that, as regards the creation
of this doctoral thesis, I have not infringed any copyright. In particular, I have not
unlawfully encroached on anyone’s personal and/or ownership rights and I am fully aware
of the consequences in the case of breaking Regulation S 11 and the following of the
Copyright Act No 121/2000 Sb., and of the rights related to intellectual property right
and changes in some Acts (Intellectual Property Act) and formulated in later regulations,
inclusive of the possible consequences resulting from the provisions of Criminal Act
No 40/2009 Sb., Section 2, Head VI, Part 4.
BRNO
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author’s signature
ACKNOWLEDGEMENT
I would like to express great thanks to my supervisor Prof. Ing. Aleš Prokeš, Ph.D. for
his patient guidance on my study, for his precious advice, and for all his devoted time,
that made this research feasible.
The research is not the work of one person. I need to express my gratitude to Dr.
Aniruddha Chandra, Ph.D. and Dr. Ing. Christoph Mecklenbräuker (Vienna University
of Technology, Institute of Telecommunications, Austria).
I thank my parents and my friends for their emotional support.
This work was supported by the Czech Science Foundation, Project No. 13-38735S
"Research into wireless channels for intra-vehicle communication and positioning", and
by National Sustainability Program under grant LO1401. These supports are gratefully
acknowledged.
BRNO
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author’s signature
Faculty of Electrical Engineering
and Communication
Brno University of Technology
Purkynova 118, CZ-61200 Brno
Czech Republic
http://www.six.feec.vutbr.cz
ACKNOWLEDGEMENT
Research described in this doctoral thesis has been implemented in the laboratories
supported byt the SIX project; reg. no. CZ.1.05/2.1.00/03.0072, operational program
Výzkum a vývoj pro inovace.
BRNO
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author’s signature
CONTENTS
1 Introduction
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2 State of Art
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2.1 Channel measurements and modeling . . . . . . . . . . . . . . . . . . 16
2.2 Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Objectives of the thesis
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4 Channel Measurements and Modeling
4.1 PHY simulation results of the IEEE 802.11p . . . . . . . . . . . . . .
4.1.1 Overview of PHY of the 802.11p . . . . . . . . . . . . . . . . .
4.1.2 Simulation model of 802.11p . . . . . . . . . . . . . . . . . . .
4.1.3 Comparison of the 802.11a and 802.11p . . . . . . . . . . . . .
4.1.4 BER performance of 802.11p standard over an ITU-R Multipath channel . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Intra-vehicle channel measurement based on the IEEE 802.11p . . . .
4.2.1 Measurement setup . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Intra-vehicle channel description based on the IEEE 802.11p . . . . .
4.3.1 Large-scale variations for Intra-Vehicle channel . . . . . . . . .
4.3.2 Small-scale variations for Intra-Vehicle channel . . . . . . . . .
4.3.3 BER-performance of the 802.11p over Intra-Vehicle Channel .
4.3.4 Comparison with UWB . . . . . . . . . . . . . . . . . . . . . .
4.4 Around of vehicle channel measurement based on the IEEE 802.11p
and UWB systems . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.1 Out-of-vehicle channel measurement setup . . . . . . . . . . .
4.5 Out-of-vehicle channel description in 5.8 GHz band . . . . . . . . . .
4.5.1 BER-performance of the 802.11p over Out-of-vehicle Channel
4.6 Out-of-vehicle channel description in UWB . . . . . . . . . . . . . . .
4.6.1 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6.2 PDP Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Localization using out-of-vehicle measurements in
5.1 Distance estimation . . . . . . . . . . . . . . . . . .
5.1.1 Delay distance dependence . . . . . . . . . .
5.2 Two dimensional localization . . . . . . . . . . . . .
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6 Conclusion
UWB
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List of abbreviations
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References
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A List of publication
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LIST OF FIGURES
1.1
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
The DSRC band allocations in different regions and countries. . . . .
The 802.11p OFDM system model. . . . . . . . . . . . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over
the Rayleigh channel Model A . . . . . . . . . . . . . . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over
the Rayleigh channel Model B . . . . . . . . . . . . . . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over
the Rayleigh channel Model C . . . . . . . . . . . . . . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over
the Rayleigh channel Model E . . . . . . . . . . . . . . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11p with coding rate
1/2 over the Rayleigh channels with different Models . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11p with coding rate
3/4 over the Rayleigh channels with different Models . . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 1/2 over Pedestrian Channel A . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 3/4 over Pedestrian Channel A . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 1/2 over Vehicular Channel B . . . . . . . .
BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 3/4 over Vehicular Channel B . . . . . . . .
Simulation results of coded transmission with coding rate 1/2 (3
Mbps) and 3/4 (4.5 Mbps) over Pedestrian Channel A and Vehicular Channel B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The underground parking lot with the vehicle under test. The receive/transmit antennas inside the car. . . . . . . . . . . . . . . . . .
The antennas patterns for different bands. . . . . . . . . . . . . . . .
The schematic diagram of the intra-vehicle measurement setup. . . .
The transmitter and receiver antenna positions for the intra-vehicle
channel measurements. . . . . . . . . . . . . . . . . . . . . . . . . . .
The PDPs for in/out-side measurements for different Tx-Rx distances.
Normalized LSV (𝛾) of the PDP for LOS/ NLOS measurements carried out for different Tx-Rx separations for in/out-side measurements.
SSV (𝜉) of the PDP for LOS/ NLOS measurements carried out for
different Tx-Rx separations for in/out-side measurements. . . . . . . .
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4.20 Empirical CDF of SSV fitted with three statistical distributions (logistic, GEV, and normal) for different Tx-Rx separations, from left
to right, top: d = 0.53 m, d = 0.94 m, d = 1.28 m, bottom: d = 2.03
m, d = 3.38 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.21 BER performance of IEEE 802.11p standard over the measured channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.22 The UWB and the 802.11p normalized PDP vs normalized time samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.23 The vehicle under test and measuring equipments. . . . . . . . . . . .
4.24 Placement of the receiving antennas for around-vehicle measurements.
4.25 Placement of the transmitting antenna for around-vehicle measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.26 The PDPs for measurements points in 5.8 GHz band. . . . . . . . . .
4.27 LSV of PDP for measurements points in 5.8 GHz band for out-ofvehicle measurements. . . . . . . . . . . . . . . . . . . . . . . . . . .
4.28 BER results based on the 802.11p over measured channels. . . . . . .
4.29 PDPs for Tx-Rx separation (𝑑) = 3m. . . . . . . . . . . . . . . . . . .
4.30 PDPs for Tx-Rx separation (𝑑) = 9m. . . . . . . . . . . . . . . . . . .
5.1 Superposition of PDPs for Rx1 for all Tx positions. . . . . . . . . . .
5.2 Delay versus distance. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 The principle of 2D localization. . . . . . . . . . . . . . . . . . . . . .
5.4 Actual and estimated position of the Tx antenna. . . . . . . . . . . .
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LIST OF TABLES
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
5.1
5.2
5.3
5.4
5.5
The main differences between IEEE 802.11p and 802.11a . . . . . .
Modulation schemes and mappings . . . . . . . . . . . . . . . . . .
Simulation parameters to compare 802.11p and 802.11a standards .
Description of the HIPERLAN/2 . . . . . . . . . . . . . . . . . . .
The HIPERLAN/2 channel models . . . . . . . . . . . . . . . . . .
The ITU-R channel models for Pedestrian and Vehicular test environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simulation parameters over ITU-R channels with different speed . .
Detailed settings for intra-vehicle measurements . . . . . . . . . . .
Parameter values for LSV of in/out-side setups. . . . . . . . . . . .
Two sample K-S test 𝑝 values for fitting SSV with different continuous
distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Characteristics of the channel models for in/out-side measurements.
Distances between transmitting and receiving antennas . . . . . . .
Parameters of LSV for measurements points in 5.8 GHz band for
out-of vehicle measurements. . . . . . . . . . . . . . . . . . . . . . .
Parameters for Out-of-vehicle channel. . . . . . . . . . . . . . . . .
PL values (in dB) for all Tx locations. . . . . . . . . . . . . . . . .
S-V parameter values . . . . . . . . . . . . . . . . . . . . . . . . . .
Distance of Rx1 from Tx antenna positions . . . . . . . . . . . . . .
Distance of Rx2 from Tx antenna positions . . . . . . . . . . . . . .
Distance of Rx3 from Tx antenna positions . . . . . . . . . . . . . .
Parameters of polynomial fitting . . . . . . . . . . . . . . . . . . . .
Comparison between measured and calculated coordinates of Tx . .
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1
INTRODUCTION
Wireless communication has been developed and used in many areas of our life.
The level of wireless communications has been grown in the latest years. Initially
this growth was in the cellular mobile’s area from analog systems to 2G (as GSM,
CDMA), 3G (GPRS, WCDMA), 4G (LTE). On other side, progress in digital electronics has given cheaper smart devices and wireless sensors, which have become part
of our life. The IEEE standards for wireless area network (WLAN), which includes
machine-to-machine (M-2-M), vehicle-to-X (V-2-X) communications, together with
cloud systems have changed conception of communication networks. In our days
more wireless communication are introduced in real life. The applications of new
technologies related with wireless networks are being developed in transport area
too.
Modern vehicles include more and more digital and smart technologies: for navigation, location information, safety and intra-vehicle communication. This applications are directed on reducing of car accidents, traffic jams and help on the roads.
Vehicular Ad Hoc Network (VANET) is a application for V-2-X communication, where different technologies are applied. Well known the mobile technologies,
such as 2G and 3G, are out to be unsuitable to VANET, because latency and priority service are used, which contrary requirements for safety-critical applications.
The development of decentralized communication technology is chosen for V-2-X
communication. Wireless communication channels named Dedicated Short Range
Communications (DSRC) are designed for automotive use and content set of protocols and standards involving everything from physical layer (PHY) for VANET [1].
The American Society for Testing and Materials (ASTM) started standardization of
DSRC for vehicle roadside communications [2]. The standard is called 802.11p described in wireless access in vehicular environments (WAVE) [3, 4], and governed by
a non-profit organization, the car-2-car communication consortium (C2C-CC) [5].
The Federal Communications Commission (FCC) allowed a 75 MHz band from
5.850-5.925 GHz for DSRC in the USA. The DSRC spectrum in the EU is a 30 MHz
band from 5.875-5.905 GHz. Spectrum allocated to DSRC in other countries can
be with status "in use", "allocated" or "potential". The DSRC band allocations in
different regions and countries are illustrated in Figure 1.1.
Vehicle area networks (VAN) and VANETs generally refer to networks between
cars and infrastructure points located along the road side [6, 7]. These networks are
aimed to deliver information about the traffic, to ensure safety of the passengers,
and to provide driver assistance and passenger entertainment [8].
Presently there had been an upsurge in the interest of the automobile industries
regarding wider applications of intra-vehicular wireless transmission. The use of
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Fig. 1.1: The DSRC band allocations in different regions and countries.
wireless communication in the vehicle cuts down the wiring harness, which will
reduce the weight and cost of the car, eliminate the need of drilling to pass the cables;
and will simplify the design as well as production processes. The IEEE 802.11p
protocol was specified primarily for V-2-V/V-2-I scenarios where both transmitter
and receiver are placed outside the vehicle [9]. In order to realize fully integrated
intelligent transportation systems (ITS), it is important to evaluate the efficacy of
the protocol for wireless connections in in-car (both transmitter and receiver are
inside the vehicle) and out-of-car (transmitter is inside and receiver is outside, or
vice-versa) scenarios as well.
Another application of V-2-X communication is locating a personal vehicle, be
it safety-critical, e.g. railway crossing surveillance [10], or non safety-critical, e.g.
finding a vehicle in parking lot [11]. There exists different global navigation satellite
systems (GNSS) such as GPS in USA, GLONASS in Russia, and Galileo in Europe,
to determine the location of a car in outdoor scenarios. Car localization is also
possible to some extent with the help of terrestrial cellular mobile networks [12],
another vehicles and Roadside Units (RSUs) [13]. However, finding exact locations of
vehicles in an enclosed space represents a major challenge. The positioning systems
mentioned before cease to work in confined and underground areas and it is necessary
to find a suitable method for such scenarios.
A short introduction and overview were given above about the current progress
in C-2-C technology and applications of communications and localizations. The
second chapter briefly describes measurements provided for C-2-X communication
for different scenarios for channel modeling and calculating of channel parameters.
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This chapter also presents results of UWB localization in buildings. The definition
of the dissertation objectives follows in chapter 3.
Chapters 4 and 5 make the core of this thesis and describe simulations and the
experiments. Chapter 4 discusses PHY simulation model of the IEEE 802.11p and
comparison with 802.11a based on simulation results over fading channels. Chapter
5 demonstrates measurements for intra-vehicle and out-of-vehicle scenarios in UWB
and for 802.11p standard. This chapter also includes two-term exponential channel
modeling for 802.11p and Saleh-Valenzuela (S-V) model for UWB. Section 5.6 additionally represents localization of vehicle in the underground garage utilizing UWB.
Finally, the main results are summarized in conclusions.
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2
2.1
STATE OF ART
Channel measurements and modeling
Channel modeling in most cases is based on real-world measurements of the channel. The measurements are used to understand the nature of the channel, extract
model parameters and validate of existing channel models. Design and implementation of measurement campaigns should be carried out in a manner such that all
the major characteristics can be obtained. This task is difficult in reality, because
each measurement is limited in measuring equipment, measurement conditions and
scenarios.
Real-world measurements have been prepared and realized for vehicular communication systems in the past under different scenarios and measurement setups.
Generally, vehicular channel measurements fall under two broad categories: vehicle
to vehicle (V-2-V) and vehicle to infrastructure (V-2-I) channels.
Channel measurements and modeling based on 802.11p
A general review on advances and challenges in V-2-V channel measurements is
provided in [14]. For both V-2-V and V-2-I scenarios, the propagation conditions
can be grouped into highway, rural, urban and suburban, based on the classification
scheme of cellular channels which are well studied. This classification was used for
measurements in [15–19]. In [15], speed-separation diagrams were prepared to analyze channel propagation, channel Doppler spread and coherence time based on the
measurements of V-2-V propagation at 5.9 GHz channel in suburban scenario. The
measuring system for on-road experimental test to characterize V-2-C wireless communication channels are described in [16]. In [17], the power delay profiles (PDPs),
RMS delay spread, approximate amplitude fading distributions and frequency correlation function were provided using a spread-stepped correlator technique at 5
GHz in three areas: large cities, open highway areas, and small cities. The PDPs
were characterized using an exponential decay cluster model, path loss (PL) models.
Further, Doppler spread statistics were derived from channel measurements at 5.9
GHz for the car-to-car channel in rural, highway, and urban environments described
in [18]. Paper [19] presents measuring of the sensitivity to the position of the antennas based on radio reception performance the IEEE 802.11a in the 5 GHz band
for roof- and in-vehicle scenario.
There are some other measurements attempted to imitate the real-world scenario
in a better way but were more complex and involved more parameters. The scenarios
were designed based on the previous classification but includes novel parameters such
16
as traffic analysis and driving directions [20–23]. In [20], the paper presents results
for PL, PDPs, and delay-Doppler spectra at the 5.2 GHz band on the highway when
cars move in opposite directions on high speed. Results of the V-2-V measurement in
the 5 GHz band for delay spread, amplitude statistics, and correlations are presented
in [21]. In [22], time-variant channel impulse response (CIR) is extracted from
measured data by the multiple input multiple output (MIMO) channel sounder at
5.7 GHz in typical urban traffic scenarios. In [23], delay spread and propagation
aspects were discussed for various vehicular environment parameters, i.e. speed,
roads and building construction, vehicle population, and traffic statistics.
More measuring campaigns were directed to scenarios based on specific situations and locations. Measurements in tunnels, on bridges, slopes, intersections
and in direct line-of-sight (LOS) with buildings and other vehicles are presented
in [24–33]. Reference [24] presents the PDPs for collision avoidance application.
Parametrization of PL categorized in LOS and non line-of-sight (NLOS) paths for
V-2-V MIMO channel at 5.7 GHz are discussed in [25]. Authors of [27] analyze the
NLOS reception of the signals according to the IEEE 802.11p in a typical urban
intersection. Channel model using real-world measurements in a city scenario based
on IEEE 802.11p is shown in [28]. The relationship between types of vehicles and
the average power of the received signal and the signal distribution, as well as the
effects of vehicle shadowing on IEEE 802.11p based communication are investigated
in [29]. The LOS propagation results for road intersections are presented in [30],
in terms of PDPs, PL and delay spreads. Shadow fading channel models categorized into LOS and obstructed LOS, based on measurements in urban and highway
scenarios are presented in [34]. Signal propagation in urban area prediction technique for V-2-V situations are presented and compared with real measurements and
simulations in [32]. Various methods for channel modeling in tunnels are reviewed
in [33], including numerical methods solving Maxwell equations, waveguide or modal
approach, ray tracing based methods and two-slope PL modeling.
In [35–39] channel measurements for signal propagation modeling were considered for inter-vehicle scenarios. The inter-vehicle channel at 5.2 GHz were measured
in realistic road traffic scenario and statistical functions were derived [35]. In [36],
NLOS, PL and fading models were presented based on measurements at intersections for inter-vehicular 5.9 GHz band. Geometry-based stochastic channel models
are used for describing the fading of V-2-V radio propagation channel at 5.3 GHz on
campus, highway, suburban and urban areas in [37]. In [38], the propagation model
was obtained with the “Wiedemann-Model” tool to generate realistic scenarios for
individual driving actions. Channel measurement results based on the IEEE 802.11a
for road traffic scenario are shown in [39].
The articles [40,41] present the distribution of channel parameters based on non17
stationary vehicular channel measurements following a bimodal Gaussian mixture
distribution. In [40], the time-frequency-dependent local scattering function was
estimated from measurements. Moreover, the PDP, the Doppler power spectral
density, the RMS delay spread and the RMS Doppler spread are analyzed. Article
[41] shows that the small-scale fading of the envelope of the first delay bin of Rician
distribution varies with K-factor.
It must be noted here that every measuring campaign is unique and is different
from others, even the same scenarios or the same frequency band. The main differences are vehicle types, locations of the antennas and configuration of roads and
buildings. A roof-mounted antenna is popular in measurement configurations. This
configuration is presented in [17,18,42–46] for single input single output (SISO) and
in [20, 22, 24, 47] for MIMO. Places of antenna location inside the vehicle on the
roof and on the windshield are shown in [21]. The various positions on the roof
were used in [19, 27]. Authors in [48] describe the impact of antenna placement on
channel parameters based on channel measurements performed with four omnidirectional antennas mounted on the roof, bumper, windscreen, and left-side mirror
of the transmitter and receiver cars.
Initially, on the first step, measurements for V-2-V and V-2-I were carried out
in the cellular mobile frequency band of 900 MHZ used in road payment systems
[42, 43, 49, 50]. Vehicle channel measurements were performed at frequencies of 5
GHz [17, 19–21, 35, 47, 51], 5.9 GHz [15, 16, 18, 25–29, 52], and 2.4 GHz [44, 53].
The reference [54] describes measurement based narrow-band channel models at
frequency of 5.2 GHz. Reports from some inter-vehicular measurement campaigns
in the 5.9 GHz band are also available in [55]. A wide set of PDP measurements and
proposed tapped delay line models based on Markov chains were provided in [55].
The MIMO measurements for V-2-V at 5.2 GHz were performed in highway
and rural environments in [56]. Measurements at 5.6 GHz band performed with
MIMO for urban intersection scenario, LOS and NLOS situations are discussed
in [57]. In [58], V-2-V measurements are performed for single input multiple output
(SIMO) to explain PL, delay spread, maximum excess delay, the standard deviation
of slow fading and K-factor. Rician K-factor from time-frequency variability based
on vehicular channel measurements at 5.6 GHz are analyzed in [59]. The urban communication scenario at intersection in 6 GHz band is analyzed in [60]. Reference [61]
describes the geometry-based channel models for existing measurement campaigns,
the most important environments, and the delay spread and Doppler spreads. Simulation of V-2-V propagation model and comparison with cellular channels are presented in [62].
The characterization of the 5 GHz intra-vehicle communication channel is only
attempted recently in [63], where the authors present PDPs, delay spread, and
18
statistical channel models for a minivan and a bus.
Channel measurements and modeling based on UWB
As far as intra-vehicular channel measurements are concerned, most of the articles [64, 65] are focused on UWB as UWB is a short-range transmission technology.
Theoretical limits on UWB range estimation and practical algorithms in terms of
Cramer-Rao and Ziv-Zakai are discussed in [66]. The frequency domain measurements of the intra-vehicle UWB channel were prepared in [67]. Authors in [68]
present clustering channel models based on the channel measurement in frequency
domain from 2 to 16 GHz in different intra-vehicle scenarios and developed by modifying the IEEE 802.15.3a [69]. The performance of multi-band OFDM in intravehicle communication is studied in [70]. The authors in [71] report about a comparison of broadband channel sounding experiments over a different range of cars.
References [72, 73] describe a detailed UWB channel modeling campaign for intravehicular environments. Time-domain channel sounding and modeling the UWB
propagation channel with modified S-V PDP model are presented in [74]. The stationary regions based on the correlation between consecutive PDPs are extracted
from UWB channel measurements results in [75].
2.2
Localization
The UWB network may be integrated for intra-vehicle and around vehicles localization. Measurement studies of communication and positioning inside vehicle are
available in [76–78]. The authors of [76] describe the localization and tracking technology based on the UWB. In [77, 78], the CIR measured for the frequency range
3-11 GHz are presented via a two-part exponentially decaying envelope-delay profile
for the intra-vehicle localization system based on the time of arrival (TOA). The
UWB sensor networks were used for localization through coherent and non-coherent
approaches of data fusion for imaging and object localization in [79]. Vehicle penetration loss and PDPs depending on angle around of car were calculated from UWB
measurement in [80].
19
3
OBJECTIVES OF THE THESIS
The objectives of the dissertation thesis are formulated as follows:
• Design and implementation of simulation model based on the IEEE 802.11p.
Performance comparison of the IEEE 802.11p and the IEEE 802.11a over
fading channels. The BER performance of the IEEE 802.11p in dependence
with a vehicular speed.
• Measuring real-world stationary channels in frequency domain for the intravehicle scenario. Channel modeling based on the 802.11p measurement results.
Separation of large-scale variation (LSV) and small-scale variation (SSV) from
PDP, and extracting CIR for using it in simulation model. The BER performance of the IEEE 802.11p of intra-vehicle channels.
• Measuring the IEEE 802.11p (5.8 GHz band) and UWB (3-11 GHz band) channels in frequency domain for the out-of-vehicle scenario. The BER performance
of the IEEE 802.11p over the out-of-vehicle channels. Channel modeling for
UWB using S-V model.
• Analysis of a localization technique designed for UWB system from measurement results for out-of-vehicle scenario.
20
4
CHANNEL MEASUREMENTS AND MODELING
4.1
PHY simulation results of the IEEE 802.11p
This chapter provides a description of the simulation model recommended for the
IEEE 802.11p protocol. The main task of chapter is to determine the differences
between standards 802.11p and 802.11a. For comparison, bit error rate (BER) was
selected. For simulation model channels appropriate for both standards were used.
The IEEE 802.11p protocol inherits the principle of the 802.11a, but has other
options in connection with another bandwidth. Thus, the concept model for both
standards will be used.
Transmitting raw bits in the network medium is function of PHY, which is the
lowest layer of the Open Systems Interconnection (OSI). Wireless communication
requires effort on this layer, especially in channel modeling and high reliability [81].
4.1.1
Overview of PHY of the 802.11p
PHY of the V-2-X communication system is based on the IEEE 802.11p standard.
The IEEE 802.11p is modified version of the IEEE 802.11a standard [82]. Original
802.11a was optimized for work in local area networks with low mobility, it couldn’t
process information in system with high delays. Amendments defined in the IEEE
802.11p aims to ensure interoperability between wireless devices that interact in
rapidly changing communication environments and handle situations where transactions must be completed in a time-frame, much shorter than that of the IEEE
802.11a [83].
PHY of the IEEE 802.11p and the IEEE 802.11a are based on OFDM. The
main differences between the IEEE 802.11a and the IEEE 802.11p are presented in
Table 4.1 [84]. An OFDM with 64 sub-carriers is used for transmission, but only
52 sub-carriers are utilized: 48 – for the actual data, 4 – pilot sub-carriers. Pilot
sub-carriers transmit a fixed pattern for disregard frequency and phase offset at the
receiver side. IEEE 802.11a uses modulation schemes BPSK, QPSK, 16-QAM or
64-QAM in combination with different coding rates. A nominal data rate could be
from 6 to 54 Mbit/s for mode with 20 MHz bandwidth. The IEEE 802.11p utilizes
the half with 10 MHz bandwidth, it is used to make signal robust against fading,
resulting in corresponding data rate reduction.
The rate-dependent parameters of the IEEE 802.11p standard PHY, which utilizes BPSK, QPSK, 16-QAM, and 64-QAM as sub-carrier modulation schemes in
21
combination with rates 1/2, 2/3, and 3/4 convolutional codes to obtain a variable
data bit rate from 3 up to 27 Mbit/s which are listed in Table 4.2 [85, 86].
Tab. 4.1: The main differences between IEEE 802.11p and 802.11a
Parameters
IEEE 802.11p
IEEE 802.11a
3, 4.5, 6, 9
6, 9, 12, 18
12, 18, 24, 27
24, 36, 48, 54
Modulation
BPSK, QPSK, 16-QAM, 64-QAM
Coding rate
1/2, 2/3, 3/4
Number of sub-carriers
52
Symbol duration (𝜇s)
8
4
Guard time (𝜇s)
1.6
0.8
FFT period (𝜇s)
6.4
3.2
Preamble duration (𝜇s)
3.2
1.6
Sub-carrier spacing (MHz)
0.15625
0.3125
Changes
Bit rate (Mbit/s)
Half
No changes
No changes
No changes
Double
Double
Double
Double
Half
Tab. 4.2: Modulation schemes and mappings
Data
rate
(Mbit/s)
3
4.5
6
9
12
18
24
27
Modulation
Coding
rate,
𝑅
Coded bits
per
sub-carriers,
𝑁𝐵𝑃 𝑆𝐶
Coded bits
per OFDM
symbol,
𝑁𝐶𝐵𝑃 𝑆
Data bits
per OFDM
symbol,
𝑁𝐷𝐵𝑃 𝑆
BPSK
BPSK
QPSK
QPSK
16-QAM
16-QAM
64-QAM
64-QAM
1/2
3/4
1/2
3/4
1/2
3/4
2/3
3/4
1
1
2
2
4
4
6
6
48
48
96
96
192
192
288
288
24
36
48
72
96
144
192
216
22
4.1.2
Simulation model of 802.11p
The latest technologies, such as 802.11p, are based on OFDM PHY, because the
OFDM-based transmission systems can provide high-rate transmission and high
spectrum efficiency in channel environments.
Fig. 4.1: The 802.11p OFDM system model.
The configuration of the model prepared in accordance with the recommendations for IEEE 802.11a standard [87]. A simulation model was implemented using
MATLAB and SIMULINK. The structures of transmitters and receivers for the
802.11p and 802.11a correspond to blocks in Figure 4.1.
The simulation model includes following signal processing stages: transmitter,
channel, receiver.
The transmitter:
• Convolutional coding with a coding rate, 𝑅 = 1/2 and 𝑅 = 3/4;
• BPSK modulation scheme;
• Inverse fast Fourier transform (IFFT) for 64 samples;
• Adding cyclic prefix (CP) with length of one-fourth of the symbol duration;
The channel:
23
• Transmitting the signal with 8 𝜇s (for 802.11p) and 4 𝜇s (for 802.11a) duration
over the fading channel HIPERPLAN/2;
The receiver:
• Removing CP;
• Fast Fourier transform (FFT) for 64 sub-carriers;
• Least Squares (LS) channel estimation;
• BPSK demodulation;
• Decoding error codes by using the algorithm Viterbi hard decision.
For getting accurate results, ten thousand bits are transmitted. More parameters of
simulation are presented in Table 4.3.
4.1.3
Comparison of the 802.11a and 802.11p
To determine the differences between BER performances of the standards 802.11a
and 802.11p channel models HIPERLAN/2 are used as fading channel models.
Channel characteristics of HIPERLAN/2 channels are used for a both standards
because they have the same structure published in [88]. Table 4.4 presents description of the channel. Parameters of channel are described in Table 4.5.
Tab. 4.3: Simulation parameters to compare 802.11p and 802.11a standards
Parameters
Bandwidth (MHz)
Mode 1
802.11p 802.11a
20
10
Inner modulation
Coding rate
Mode 2
802.11p 802.11a
20
10
BPSK
1/2
3/4
Location of pilots (№)
7 21 35 49
Transmission
channel models
HIPERLAN/2:
A, B, C, E
The transmission of data was simulated for varying 𝐸𝑏 /𝑁0 over fading Rayleigh
channels: Model A, Model B, Model C, and Model E that are shown in Figures 4.2,
4.3, 4.4 and 4.5, two BER curves were obtained: for standards IEEE 802.11a and
IEEE 802.11p.
For low values of average 𝐸𝑏 /𝑁0 , the errors are same. The average 𝐸𝑏 /𝑁0 required
for BER of 10−4 is found at 2 dB for mode 1 and 3 dB for mode 2 over Channel
A, for BER of 10−4 – 3 dB for mode 1 and 0 dB for mode 2 over Channel B, and
for BER of 10−4 – 3 dB for mode 1 and 5 dB for mode 2 over Channel C. Figure
24
Tab. 4.4: Description of the HIPERLAN/2
Environment
Description
A
Typical office environment for NLOS
condition with 50 ns RMS delay spread
B
Typical large open space and office environment for NLOS
condition and 100 ns RMS delay spread
C
Typical large open space environment for NLOS
condition and 150 ns RMS delay spread
E
Typical large open space environment for NLOS
condition and 250 ns RMS delay spread
Tab. 4.5: The HIPERLAN/2 channel models
Model A
Model B
Model C
Model E
Tap
Number
Delay
(ns)
Average
power
(dB)
Delay
(ns)
Average
power
(dB)
Delay
(ns)
Average
power
(dB)
Delay
(ns)
Average
power
(dB)
1
2
3
4
5
6
7
8
9
10
0
10
20
30
40
50
60
70
80
90
0.0
-0.9
-1.7
-2.6
-3.5
-4.3
-5.2
-6.1
-6.9
-7.8
0
10
20
30
50
80
110
140
180
230
-2.6
-3.0
-3.5
-3.9
0.0
-1.3
-2.6
-3.9
-3.4
-5.6
0
10
20
30
50
80
110
140
180
230
-3.3
-3.6
-3.9
-4.2
0.0
-0.9
-1.7
-2.6
-1.5
-3.0
0
10
20
40
70
100
140
190
240
320
-4.9
-5.1
-5.2
-0.8
-1.3
-1.9
-0.3
-1.2
-2.1
0.0
4.5 shows the errors at standards and different modes, results for the IEEE 802.11p
indicate better performance even for the IEEE 802.11a.
The BER performance of IEEE 802.11p is almost the same for the channel Models
A, B, C, and E (Figures 4.6 and 4.7). For IEEE 802.11a the BER are slightly greater
for channel Model E than for channel Model A, B, and C, as the mean delay exceeds
the cyclic prefix.
The IEEE 802.11p has different BER performance than the IEEE 802.11a. This
is based on the difference of bandwidth. BER performance of IEEE 802.11p is found
25
Fig. 4.2: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over the
Rayleigh channel Model A
Fig. 4.3: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over the
Rayleigh channel Model B
to be superior when compared to that for IEEE 802.11a owing to a longer CP time
and increases with growing path gain and delay channel.
26
Fig. 4.4: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over the
Rayleigh channel Model C
Fig. 4.5: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11a and 802.11p over the
Rayleigh channel Model E
4.1.4
BER performance of 802.11p standard over an ITU-R
Multipath channel
Channel model ITU-R is an empirical model and developed in accordance with the
ITU-R M.1225 Recommendation. This type of modeling was used to simulate the
27
Fig. 4.6: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11p with coding rate 1/2
over the Rayleigh channels with different Models
Fig. 4.7: BER as a function of 𝐸𝑏 /𝑁0 for signals of 802.11p with coding rate 3/4
over the Rayleigh channels with different Models
IEEE 802.11p signal. Among the recommendations for channel ITU-R, which are
described in [89], there are two models of channels, characterizing different propagation environments. For each model, pedestrians and motor vehicles, different delay
spread profiles are set. Low delay spread profile A, describes the rms delay spread
370 ns for Pedestrian. Profile B is 4000 ns for Vehicular. The model for Pedestrian
28
Channel A has 4 taps and Vehicular Channel B has 6 taps which are presented in
Table 4.6.
Tab. 4.6: The ITU-R channel models for Pedestrian and Vehicular test environments
Pedestrian Channel A
Vehicular Channel B
Taps
Relative
delay (ns)
Average
power (dB)
Relative
delay (ns)
Average
power (dB)
1
0
0
0
-2.5
2
110
-9.7
300
0
3
190
-19.2
8900
-12.8
4
410
-22.8
12900
-10.0
5
-
-
17100
-25.2
6
-
-
20000
-16.0
Simulation model is used from 4.1.2. Table 4.7 presents modes for simulation.
The Pedestrian Channel A and Vehicular Channel B in simulation model of the
IEEE 802.11p standard are the components of MATLAB SIMULINK.
Tab. 4.7: Simulation parameters over ITU-R channels with different speed
Parameters
Mode 1
802.11p
Mode 2
802.11p
Bandwidth (MHz)
20
20
Inner modulation
Coding rate
BPSK
1/2 and 3/4
Location of pilots (№)
1/2 and 3/4
7 21 35 49
Transmission
channel
models
Pedestrian
Channel
A
Vehicular
Channel
B
Speed of
transmitter (km/h)
5, 30 , 60
30, 60 , 90
130, 260
One of the key factors affecting the performance of the IEEE 802.11p standard
model ITU-R M.1225 channels is the effect of the Doppler shift. Figures 4.8 and 4.9
show the errors at different speeds of the transmitter relative to the receiver over
the ITU-R Pedestrian Channel A, specified in Table 4.6. The simulation was carried
out by BPSK OFDM modulation and coding rates 1/2 and 3/4, which corresponds
29
to the transmission rates 3 Mbps and 4.5 Mbps respectively. At low speed, about 5
km/h, better performance is achieved, with an increase in speed of the transmitter,
the probability of error increases. Results of 3 Mbps rate indicate better performance
even at a speed of 30 km/h, when the rate of 4.5 Mbps BER is not reduced below
0.007 units.
Figures 4.10 and 4.11 show the simulation results over ITU-R Vehicular Channel
B. The model also includes a BPSK OFDM modulation and coding rates 1/2 and
3/4. With the growth of the Doppler shift and intersymbol interference (ISI) for a
vehicle speed of 260 km/h the BER performance does not show favorable results.
Figure 4.12 presents side-by-side comparison of PHY model performance in various propagation test environments. All curves are plotted for coded BPSK transmission without being punctured. Pedestrian model Channel A assumes a vehicle
velocity of 5 km/h. For Vehicular Models, the speed of the transmitter is 90 km/h.
ITU-R Pedestrian Channel A has a maximum relative delay of 410 ns and RMS
45 ns, so the impulse response of the channel is shorter than the guard interval.
Thus, when the speed of movement of the transmitter is 5 km/h, BER corresponds
to 10−3 at 𝐸𝑏 /𝑁0 of 20 and 26 dB, depending on the data rate. The ITU-R Therefore
the efficiency of the model is reduced to a level of 10−1 at the speed of the transmitter
90 km/h and data rate is 3 Mbps, and efficiency drops with increasing data rates
up to 4.5 Mbps, while maintaining the speed of the transmitter.
Fig. 4.8: BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 1/2 over Pedestrian Channel A
30
Fig. 4.9: BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 3/4 over Pedestrian Channel A
Fig. 4.10: BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 1/2 over Vehicular Channel B
31
Fig. 4.11: BER as a function of 𝐸𝑏 /𝑁0 on relative vehicle velocity for signals of
802.11p with coding rate 3/4 over Vehicular Channel B
Fig. 4.12: Simulation results of coded transmission with coding rate 1/2 (3 Mbps)
and 3/4 (4.5 Mbps) over Pedestrian Channel A and Vehicular Channel B.
32
4.2
Intra-vehicle channel measurement based on
the IEEE 802.11p
4.2.1
Measurement setup
Measurements were carried out on the basis of the car Skoda Octavia Liftback. This
car was parked on the lower (sixth) floor of the underground parking garage of the
Faculty of Electrical Engineering (FEEC), Brno University of Technology (BUT).
The car is a typical mid-size vehicle with dimensions outside: 4.659 × 1.814 ×
1.462 m. Due to the car location of the underground and walls and floors are from
reinforced concrete, the narrowband interference is suppressed. There were no other
vehicle around the measured car. Figure 4.13 presents the parking lot with the car
under test and used equipments.
Rx 2
Rx 3
Tx
Rx 2
Rx 1
Tx
Rx 1
(a)
(b)
Fig. 4.13: The underground parking lot with the vehicle under test. The receive/transmit antennas inside the car.
The measurements were performed using one transmitter antenna (Tx) and three
receiver antennas (Rx), which are shown in Figure 4.13. The four identical omnidirectional conical monopole antenna were used for the Tx and Rx. The the circular
azimuth H-plane pattern of the antennas can be confirmed from the radiation patterns shown in Figure 4.14.
33
-30o
E-plane
0o 10dBi
30o
0dBi
-10dBi
-60o
f = 3.0 GHz
f = 6.5 GHz
f = 10.0 GHz
60o
-30o
30o
60o
-20dBi
90o
-90o
-90o
120o
-120o
180o
0dBi
-10dBi
-60o
-20dBi
-150o
H-plane
0o 10dBi
90o
-120o
150o
120o
-150o
180o
150o
Fig. 4.14: The antennas patterns for different bands.
Measuring equipment was installed at the outside, near to the car. All doors
and windows were closed, except for the driver’s window, which was slightly open
due to the cables connecting the antennas to measuring and recording equipment.
The four-port vector network analyzer (VNA) E5071C (Agilent Technologies)
[90] was used to measure and record the scattering parameters. To connect the analyzer and the Tx and Rx antennas phase stable coaxial cables were used. Calibration
of VNA was performed only with cables, without connected antennas. The transmit
power was chosen at 5 dBm.
The measurement results are the complex valued transfer function in the frequency domain. The VNA presents CTF as s-parameter 𝑆𝑥1 , where x ∈ {2, 3, 4} is
number of Rx port and 1 is Tx port of VNA.
Measurement parameters are set in accordance with recommendations of the
IEEE 802.11p except frequency range of 100 MHz (5.775 to 5.875 GHz). Frequency
step of 𝑓𝑠 = 0.125 MHz was chosen for 801 carriers. The bandwidth, 𝐵𝑤, for 1
sub-channel is 10 MHz. The time range was calculated as 𝑡𝑑 = 1/𝑓𝑠 = 8 𝜇s. The
information converted into the spatial domain presents a maximum propagation
distance is 𝐿𝑑𝑚𝑎𝑥 = 𝑐/𝑓𝑠 = 2.4 km and a propagation space resolution 𝑃𝑠𝑟 = 𝑐/𝐵𝑤
= 3 m, where 𝑐 is the light speed.
Figure 4.15 shows the schematic diagram of the measurements setup using VNA
for frequency domain channel measurement. After recording the CTF complex 𝑆𝑥1
was uploaded to MATLAB. Since the IEEE 802.11p standard has bandwidth of 10
MHz, the data in frequency domain with bandwidth of 100 MHz were partitioned
into 10 MHz segments, response of each of them are close. All results were translated
from frequency to time domain using the complex IFFT with rectangular window
[91]: (ℎ(𝜏 ) = ℱ −1 𝐻(𝑓 )), where 𝑆𝑥1 used as 𝐻(𝑓 ). Results in the time domain were
presented as averaging over ten sub-channels.
The Tx antenna was placed at three different locations inside and at two locations
34
outside the car. Positions inside the vehicle were chosen at the right rear set, in the
middle of the at armrest, and at the driver’s seat. Outside positions were set in
front of the car and near the right headlamp, the Tx antenna was installed on a
tripod with height of 1.09 m. The Rx antennas positioned inside of the car: one - on
the roof in the rear part of the vehicle and two - on the windshield, in the left and
right upper corners. A total of 15 different combinations between the Tx and Rx
antennas were performed with distances from 0.53 to 3.38 m. All transmitter and
receiver antenna positions are shown in Figure 4.16. Table 4.8 presents parameters
of all combinations for measuring. Both the LOS and NLOS scenarios were realized.
Tx
Antenna
X(ω)
Port 1
Transmission
Rx
Antenna
Vector
network
analyzer
Y(ω)
Port 2
S21 = Y(ω) / X(ω)= H(ω)
IFFT
h(t)
Fig. 4.15: The schematic diagram of the intra-vehicle measurement setup.
35
Fig. 4.16: The transmitter and receiver antenna positions for the intra-vehicle channel measurements.
4.3
Intra-vehicle channel description based on the
IEEE 802.11p
The generic multipath actual CIR is described as:
ℎ𝑐ℎ (𝜏 ) =
𝑁
−1
∑︁
𝑎𝑖 exp (𝑗𝜃𝑖 )𝛿(𝜏 − 𝜏𝑖 ).
(4.1)
𝑖=0
where 𝜏𝑖 is the propagation delay of the 𝑖-th multipath component (MPC), 𝑎𝑖 exp (𝑗𝜃𝑖 )
is the complex amplitude coefficient of the 𝑖-th MPC, and 𝛿(.) is the Dirac delta
function, and N is the total number of MPCs.
As we have one realization, the PDP can be presented through the squared CIR,
𝑃 (𝜏 ) = |ℎ𝑐ℎ (𝜏 )|2 , and mathematically decomposed into a sum of large scale variation
(LSV) and small scale variation (SSV): 𝑃 (𝜏 ) = 𝛾(𝜏 ) + 𝜉(𝜏 ), where 𝛾(𝜏 ) denotes the
LSV and 𝜉(𝜏 ) is the SSV.
The measured PDP for different Tx-Rx combinations are shown in Figure 4.17.
As expected, power levels for LOS are significantly higher compared to the NLOS
36
Tab. 4.8: Detailed settings for intra-vehicle measurements
Measurement
Tx-Rx
separation (m)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0.53
0.69
0.74
0.85
0.87
0.94
1.26
1.28
1.41
1.86
2.03
2.08
2.74
3.15
3.38
Tx
position
Rx
position
Remarks
2R
4R
2M
2M
2M
2R
4R
2R
4R
0R
0M
0M
0R
0R
0M
2R
4M
2L
2R
4M
2L
2R
4M
2L
2L
2L
2R
2R
4M
4M
LOS
LOS
LOS
LOS
LOS
LOS
NLOS
NLOS
NLOS
Tx at an angle
Tx in front
Tx in front
Tx at an angle
Tx at an angle
Tx in front
measurements. The power levels for NLOS conditions for both inside and outside
of the vehicle present show rapid variations near the noise floor (-110dB).
4.3.1
Large-scale variations for Intra-Vehicle channel
Analytical performance calculated for closed and small areas presented an exponential decay of the PDP [92, 93]. The intra-vehicular signal propagation environments
are similar to small spaces, the LSV can be demonstrated a two-term exponential
model:
𝛾(𝜏 ) = 𝐴 exp (𝐵𝜏 ) + 𝐶 exp (𝐷𝜏 ) ; 0 < 𝜏 < 𝑡𝑑 /2
(4.2)
The first term describes power from direct and major reflected rays. The second
term is close to linear with a very low slope includes the power from diffused MPCs.
The two-term exponential model 4.2 gives more flexible fitting than single-term exponential or linear models. This model avoids discontinuities in the separated LSV,
which appear when PDP divided into several parts, and the individual description
for each of these parts [93].
To evaluate the quality of the definition of LSV model (4.2), according to the
37
-30
0.53m LOS Tx:2R Rx:2R
0.94m LOS Tx:2R Rx:2R
1.28m NLOS Tx:2R Rx:4M
2.03m NLOS Tx:0M Rx:2L
3.38m NLOS Tx:0M Rx:4M
-40
-50
PDP [dB]
-60
-70
In-car
-80
-90
-100
-110
-120
Out-of-car
-130
0
0.5
1
1.5
2
2.5
Time Delay [s]
3
3.5
4
x 10
-6
Fig. 4.17: The PDPs for in/out-side measurements for different Tx-Rx distances.
PDP, the mean square error (MSE) between the PDP and the LSV was used:
MSE =
𝑀
1 ∑︁
[𝑃𝑘 (𝜏 ) − 𝛾𝑘 (𝜏 )]2
𝑀 𝑘=1
; 0 < 𝜏 < 𝑡𝑑 /2
(4.3)
The minimum MSE is 6.59 × 10−5 , averaged over all the 𝑀 = 15 measurements.
Figure 4.18 presents the LSV for the two-term exponential model (4.2) for different Tx-Rx distances of 0.53 m, 0.87 m, 1.28m, 2.74 m, and 3.15 m. It is observed
that LSV for the NLOS case drops more slowly than for the LOS scenario as in
earlier observations [94].
Table 4.9 describes the parameters of the two-term exponential model for five
distances from Figure 4.18. The coefficients A and C show similar values for groups
in-car and out-of-car. The coefficient B decreases steadily with increasing Tx-Rx
separation whereas D increases.
4.3.2
Small-scale variations for Intra-Vehicle channel
As stated earlier, the measured PDP is the mathematical sum of LSV and SSV,
consequently SSV can be separated from the PDP, 𝜉(𝜏 ) = 𝑃 (𝜏 ) − 𝛾(𝜏 ). SSVs for 5
different distances were calculated, and the results are shown in Figure 4.19.
The characterization of the SSV is focused on finding the appropriate random
process using the normal, the logistic, and the generalized extreme value (GEV)
distributions. For both UWB [77] and milimeter wave [95] SSVs were based on the
38
0
0.53m LOS Tx: 2R Rx:2R
0.94m LOS Tx: 2R Rx: 2L
1.28m NLOS Tx: 2R Rx: 4M
2.03m NLOS Tx: 0M Rx: 2L
3.38m NLOS Tx: 0M Rx: 4M
Normalized LSV (γ) of PDP [dB]
-10
-20
-30
-40
Out-of-car
-50
-60
In-car
-70
0
0.5
1
1.5
2
2.5
Time Delay [s]
3
3.5
4
x 10
-6
Fig. 4.18: Normalized LSV (𝛾) of the PDP for LOS/ NLOS measurements carried
out for different Tx-Rx separations for in/out-side measurements.
Tab. 4.9: Parameter values for LSV of in/out-side setups.
Tx-Rx
separation (m)
Parameter values
𝐵 (×104 )
𝐶
𝐷 (×105 )
𝐴
0.53
0.94
1.28
2.03
3.38
-49.81
-48.56
-50.66
-47.36
-45.32
0.63
0.64
0.46
0.34
0.14
45.98
43.89
45.37
38.82
38.25
-7.05
-6.47
-5.20
-3.68
-4.28
GEV distribution [96]. The logistic family of distributions [97] appeared to be a good
fit in ray-cluster based modeling at 5 GHz [98] and modeling the probability density
functions (PDF) of the RMS delay spread at 2.35 GHz [99].The normal distribution
was also added as a reference. The PDF of these three distributions: the normal,
the logistic, and the GEV are given in (4.4), (4.5), and (4.6), respectively.
1
−(𝑥 − 𝜇)2
𝑓 (𝑥|𝜇, 𝜎) = √ exp
2𝜎 2
𝜇 2𝜋
[︃
𝑓 (𝑥|𝜇, 𝜎) =
exp
[︁
(︁
𝑥−𝜇
𝜎
𝜎 1 + exp
39
(︁
]︃
(4.4)
)︁
𝑥−𝜇
𝜎
)︁]︁2
(4.5)
10
SSV (ξ) of PDP [dB]
5
0
-5
-10
-15
0
0.53m LOS Tx: 2R Rx:2R
0.94m LOS Tx: 2R Rx: 2L
1.28m NLOS Tx: 2R Rx: 4M
2.03m NLOS Tx: 0M Rx: 2L
3.38m NLOS Tx: 0M Rx: 4M
0.5
1
1.5
2
2.5
Time Delay [s]
3
3.5
4
x 10
-6
Fig. 4.19: SSV (𝜉) of the PDP for LOS/ NLOS measurements carried out for different Tx-Rx separations for in/out-side measurements.
𝑓 (𝑥|𝑘, 𝜇, 𝜎) =
(︁
)︁
1
−1
−1
exp −𝛽 −𝑘
𝛽 −1−𝑘
𝜎
(4.6)
where 𝛽 = 1 + 𝑘 𝑥−𝜇
. The parameters, 𝜇, 𝜎, and 𝑘 are the location, scale, and shape
𝜎
parameters, respectively.
The two-sample Kolmogorov-Smirnov (K-S) test [100] was performed to compare
SSV and distributions. From Table 4.10 𝑝-values for the logistic distribution inside
the car and for the GEV distribution outside the car are highest. Figure 4.20
shows the cumulative distribution function (CDF) of measured SSV fitted with
three distributions for results measured at different distances. The logistic and the
GEV are the best candidates among the three for describing the SSV in in-car and
out-of-car situations, respectively.
4.3.3
BER-performance of the 802.11p over Intra-Vehicle
Channel
For BER simulation MATLAB SIMULINK was used. A detailed description of the
simulation methodology and parameters for generating the signal can be found in
Section 4.1.2 and in [84]. A block-scheme diagram that corresponds to the SISO
transmission model is presented in Figure 4.1.
40
Tx
position
Tx-Rx
separation (m)
GEV
Logistic
Normal
0.53
0.94
1.28
2.03
3.38
0.3456
0.8838
0.7085
0.9997
0.8838
0.8838
0.8838
0.7085
0.6297
0.7085
0.7085
0.7085
0.8838
0.5142
0.8838
In-car
1
1
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.6
0.5
0.4
0.6
0.5
0.4
0.3
0.2
0.2
-4
-2
0
2
Data
Logistic
GEV
Normal
0.1
0
-6
4
-4
-2
SSV (ξ)
0
(a)
0.9
0.9
0.9
0.8
0.8
0.8
0.7
0.7
0.7
0.4
0.3
Cumulative Probability
1
0.5
0.6
0.5
0.4
0.3
Data
Logistic
GEV
Normal
-5
0
SSV (ξ)
(c)
5
0
-10
6
-5
0
SSV (ξ)
(c)
0.6
0.5
0.4
0.2
Data
Logistic
GEV
Normal
0.1
0
-15
4
0.3
0.2
10
0.1
(b)
1
0.6
2
-10
-5
0
5
Data
Logistic
GEV
Normal
0.1
10
0
-10
SSV (ξ)
-8
-6
-4
-2
0
2
4
6
8
SSV (ξ)
(d)
(e)
Fig. 4.20: Empirical CDF of SSV fitted with three statistical distributions (logistic,
GEV, and normal) for different Tx-Rx separations, from left to right, top: d = 0.53
m, d = 0.94 m, d = 1.28 m, bottom: d = 2.03 m, d = 3.38 m.
For the channel block in Figure 4.1, the tap gains were calculated by sampling
the LSV, after every 10 ns. The respective CIRs are shown in Table 4.11. It is
41
0.6
0.5
0.4
0.3
Data
Logistic
GEV
Normal
SSV (ξ)
1
0
-10
0.4
0.3
0
-6
0.1
0.5
0.2
0.1
0.2
0.6
0.3
Data
Logistic
GEV
Normal
Cumulative Probability
1
Cumulative Probability
1
Cumulative Probability
Cumulative Probability
Out-of-car
Cumulative Probability
Cumulative Probability
Tab. 4.10: Two sample K-S test 𝑝 values for fitting SSV with different continuous
distributions.
5
0.2
0.1
10
0
-15
interesting to find that real-life measurements exhibit a very fast decrease of the tap
gains, similar to some earlier reports [101]. Thus the derived tap-delay line model
exhibits power-delay characteristics found in LOS multipath propagation dominated
by a strong first peak and followed by several other peaks. This implies that we can
use a Rician model. Rician distribution had been successfully utilized for nongeometrical stochastic modeling of vehicular channels [53]. A Rician model also
encompasses the Rayleigh NLOS model as a special case and this can be seen in
Table 4.11 that the for NLOS situations the tap gains do not decrease very fast.
We have not used the SSV (and the corresponding fitted distributions) for BER
calculation as BER is an average measure. In fact the idea to separate SSV was
to develop a model free from such local measurement variations. However, the
variations may be added by simulating an appropriate fitted distributions in order to
accurately reconstruct the PDP. This may be useful in studying other time domain
channel parameters such as RMS delay spread etc which is out of scope for the
current thesis.
Tab. 4.11: Characteristics of the channel models for in/out-side measurements.
K-factor (dB)
Tap delay (ns)
0
10
20
30
40
50
60
70
80
90
0.53m LOS
21.30
0.94m LOS
20.07
0
-11.78
-17.69
-20.69
-22.26
-23.11
-23.62
-23.95
-24.20
-21.41
0
-10.61
-16.24
-19.27
-20.93
-21.87
-22.45
-22.83
-23.10
-23.33
1.28m NLOS 2.03m NLOS
19.61
15.15
Tap gain (dB)
0
-9.61
-14.90
-18.27
-20.32
-21.58
-22.39
-22.91
-23.27
-23.53
0
-6.06
-10.28
-13.22
-15.28
-16.74
-17.76
-18.50
-19.04
-19.43
3.38m NLOS
15.40
0
-6.69
-11.06
-13.93
-15.80
-17.04
-17.85
-18.40
-18.76
-19.01
The BER results for different distances between Tx and Rx, for LOS and NLOS
cases, and for intra-vehicle and out-of-vehicle situations are shown in Figure 4.21.
Results can be classified quite unambiguously into two groups, one for intra-vehicle
and the other for out-of-vehicle measurements. The BER worsens for higher TxRx distance inside the car, but outside the vehicle the BER values almost remain
unaffected when the Tx-Rx separation is altered. The BER results for intra-vehicle
simulations are slightly better than the results for the out-of-vehicle channel. For a
42
target BER of 10−5 the required (𝐸𝑏 /𝑁0 ) differs by 6 dB.
10
10
BER
10
10
10
10
10
0
-1
-2
Out-of-car
-3
In-car
-4
-5
-6
-10
0.53 m LOS Tx: 2R Rx: 2R
0.94 m LOS Tx: 2R Rx: 2L
1.28 m NLOS Tx: 2R Rx: 4M
2.03 m NLOS Tx: 0M Rx: 2L
3.38 m NLOS Tx: 0M Rx: 4M
AWGN
-5
0
5
10
15
20
25
Eb/N0 [dB]
Fig. 4.21: BER performance of IEEE 802.11p standard over the measured channels.
4.3.4
Comparison with UWB
UWB possesses a great potential for high-speed data communication and precise
localization operations in cluttered closed-spaces hostile towards radio frequency
(RF) signal propagation. Traditionally the PDP for UWB channels are described
with a modified S-V model [74]. However, in a recent work by Demir et al. [102],
the authors found that the PDP for UWB transmission in vehicular networks may
be characterized by segmenting the PDP (in dB scale) into several linear slopes.
Inspired by these facts, we tried to compare our results for 802.11p protocol with
measurements for UWB (3 GHz - 11 GHz) performed using the same VNA based
setup. The goal was to find if one can attain similar LSV trends in wider bandwidths
as well.
The number of measured points were the same, however, due to the larger BW
(8 GHz) and a frequency step size of 𝑓𝑠 = 100 MHz, we have a smaller time range,
𝑡𝑑 = 1/𝑓𝑠 =10 ns. Thus we cannot compare the PDPs in an one-to-one basis.
For analyzing LSV models we use a scaled comparison instead. For the UWB
measurement, the propagation space resolution is 𝑃𝑠𝑟 = 𝑐/𝐵𝑤 = 3 cm, where 𝑐 is
the light speed, and the maximum propagation distance is 𝐿𝑑𝑚𝑎𝑥 = 𝑐/𝑓𝑠 = 30 m.
43
0
1.28 m (802.11p)
1.28 m (UWB)
0.53 m (802.11p)
0.53 m (UWB)
-10
Normalized PDP [dB]
-20
-30
-40
-50
-60
-70
-80
-90
-100
0
5
10
15
20
25
30
35
40
Normalized Time Samples
Fig. 4.22: The UWB and the 802.11p normalized PDP vs normalized time samples.
Figure 4.22 shows the UWB and the 802.11p normalized PDP vs normalized
time samples, which used as basis instead of delay of MPCs. For the UWB, it was
found that the PDP constitutes one (or a few) major peaks followed by somewhat
linear decreasing slope. The reader may also note that there are the delay of the
first peak and lower power of PDP for the UWB. In addition, the delay and the
maximum value of the peak are more for larger distances between the transmitting
and receiving antennas. However, this phenomena is not observed in narrowband
5.8 GHz measurements for 802.11p.
4.4
Around of vehicle channel measurement based
on the IEEE 802.11p and UWB systems
The Section presents out-of-vehicle channel measurement results in the 5.8 GHz
frequency band and UWB. Experiments for a total of 23 points for different distances
and different angles around the parked car are carried out. The LSV separated by the
two-term exponential decay model is used to calculate the corresponding CIR and
to construct a Rician tap-delay multipath channel model for 5.8 GHz band. BER
performance for the IEEE 802.11p for some measured locations is then evaluated
through MATLAB based simulation.
44
4.4.1
Out-of-vehicle channel measurement setup
Measurements are performed with one transmitter and three receiver antennas
around the car Skoda Octavia. The car is parked in the same underground garage as
in Section 4.3. Experiments are realized with VNA following the basic principle as
outlined in [103]. For the measurements car is parked at the middle of the six floor,
away from walls and other cars. Figure 4.23 presents equipments for measuring and
data recording and the car with closed doors and windows. Equipments are installed
outside the car on the opposite side of the measurement area.
Rx2
Rx1
VNA
Tx
Rx3
Fig. 4.23: The vehicle under test and measuring equipments.
The measurements were performed in the ultra-wide band (3 GHz to 11 GHz)
and for the 802.11p standard (5.8 GHz) in the same points for Tx and Rx, for that
reason the VNA Agilent Technologies E5071C was used. The Tx and Rx antennas
are connected to the four ports of the VNA through phase stable coaxial cables. The
principles of measuring of the 𝑠-parameter values, 𝑠𝑥1 ; 𝑥 ∈ {2, 3, 4}, are described
also in Section 4.2.1. The frequency domain data are converted to time domain CIR
using IFFT.
For measurements omnidirectional identical conical antennas are used. Figure
4.14 shows the measured radiation patterns of antennas for 5.8 GHz band and for
UWB. For both bands the H-planes are circular and there is no effect of antenna
45
orientation when antennas are placed at same height.
The positions of the receiving antennas are displayed in Figure 4.24 The first
receiving antenna, Rx1 , is installed on the roof of the vehicle near car radio antenna.
The second receiver, Rx2 , is mounted also on the roof at a distance of 1.13 m from
Rx1 . The third antenna, Rx3 , is located on the car bonnet near windshield. All
three antennas create one axis, Rx1 and Rx2 are installed at a height of 1.5 m and
Rx3 – 1 m.
Rx1-Rx2
1,04m
1,13m
Rx2
1,5m
1,0m
Rx3
Rx1
Rx3
Fig. 4.24: Placement of the receiving antennas for around-vehicle measurements.
The transmitting antenna is set at a height of 1.5 m above ground (at the same
level of Rx1 and Rx2 ) with the help of a tripod stand and its position is changed to
23 different locations as depicted with blue solid circles in Fig. 4.25. The distances
of Tx from all three Rx antennas are listed in Table 4.12. Tx installation points
follow concentric semi-circles around Rx1 with an increasing radius of 3 m, 4.5 m, 6
m, 7.5 m, and 9 m at an uniform angular separation of 45∘ , i.e. at angles of 0∘ , 45∘ ,
90∘ , 135∘ , and 180∘ relative to the roll axis which connects Rx1 with Rx3 via Rx2 .
This arrangement ensures that there always exists a direct LOS path between Tx
46
RX3
RX2
RX1
1.5m
3m
4.5m
6m
7.5m
9m
Fig. 4.25: Placement of the transmitting antenna for around-vehicle measurements.
and Rx1 (as well as between Tx and Rx2 ) whereas the path between Tx and Rx3 is
mostly NLOS with LOS path being available at only certain angles and distances.
4.5
Out-of-vehicle channel description in 5.8 GHz
band
The PDP is calculated from CIR and described in previous Section 4.3. Figure 4.26
presents the measured normalized PDP for measurement points for LOS condition
outside of the car for 𝑇 𝑥1 . The power reduces to about 45 dB in the considered
delay range, from -8 to -53 dB. The noise for non-normalized PDP is -110 dB level
and corresponds to previous out-of-vehicle measurements.
Following previous Section 4.3, the measured PDP at each location can be expressed as a sum of LSV and SSV. The LSV are separated from PDP by two-termexponential model (4.2) and presented in Figure 4.27. It can be seen that the LSV
curves form a distinct group. Table 4.13 shows the parameters of the model for
different measurement points.
4.5.1
BER-performance of the 802.11p over Out-of-vehicle
Channel
For constructing of BER as a function of (𝐸𝑏 /𝑁0 ) the simulation model was used
from Section 4.1.2. Simulation was prepared in MATLAB utilizing system parame-
47
Tab. 4.12: Distances between transmitting and receiving antennas
Measurement
Angle
Distance
(degrees) for Rx1 (m)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
0
45
90
135
180
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
3.0
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
9.0
Distance
for Rx2 (m)
Distance
for Rx3 (m)
3.37
4.87
6.37
7.87
2.34
3.79
5.26
6.75
8.24
3.21
4.64
6.11
7.58
3.88
5.36
6.85
8.34
9.83
4.13
5.63
7.13
8.63
10.13
2.33
3.82
5.32
6.82
2.14
3.35
4.72
6.16
7.62
3.73
5.01
6.39
7.82
4.81
6.25
7.71
9.18
10.66
5.2
6.69
8.19
9.69
11.19
ters based on the recommendations for the IEEE 802.11p protocol and described in
detail in [84] and Section 4.1.2.
Following Section 4.3 LOS cases are described by the Rician distributions [53].
To construct a tap-delay multipath Rician channel the LSV was used. Table 4.14
presents the calculated 10 tap gains and 𝐾 factors for different positions.
The BER performance using the Rician channel based on the data in Table 4.14
for six locations are presented in Figure 4.28. The BER curves can be grouped
easily as the maximum difference is less than 4 dB at a BER = 10−4 . An average
channel BER performance was calculated over channel based on average coefficients
of the LSV. The average channel error result has non-significant deviation from BER
curves for six locations and can effectively serve as a design basis for intra-vehicle
48
Fig. 4.26: The PDPs for measurements points in 5.8 GHz band.
Fig. 4.27: LSV of PDP for measurements points in 5.8 GHz band for out-of-vehicle
measurements.
link budget calculations.
49
Tab. 4.13: Parameters of LSV for measurements points in 5.8 GHz band for out-of
vehicle measurements.
No. of
Measurement
Parameters
𝐵
𝐶
𝐴
1
2
3
4
5
6
-109.3
-114.9
-112.0
-114.6
-111.7
-121.6
0.002080
0.001692
0.002545
0.001976
0.001641
0.003020
34.01
35.29
35.72
29.04
40.39
38.98
𝐷
-0.2872
-0.2595
-0.4345
-0.2492
-0.2780
-0.4942
Tab. 4.14: Parameters for Out-of-vehicle channel.
Tap
delay (ns)
0
10
20
30
40
50
60
70
80
90
𝐾 factor (dB)
4.6
4.6.1
1
0
-4.36
-7.65
-10.14
-12.03
-13.48
-14.59
-15.46
-16.13
-16.67
17.93
Tap gain (dB)
3
4
2
0
0
-4.34 -6.73
-7.65 -11.01
-10.17 -13.75
-12.10 -15.53
-13.59 -16.70
-14.74 -17.48
-15.63 -18.03
-16.34 -18.43
-16.91 -18.74
18.24 19.86
5
6
0
0
0
-3.31 -4.99 -7.78
-5.92 -8.79 -12.60
-7.98 -11.70 -15.61
-9.61 -13.92 -17,52
-10.91 -15.62 -18.76
-11.94 -16.93 -19.59
-12.78 -17.95 -20.17
-13.45 -18.74 -20.60
-14.00 -19.37 -20.93
15.43 20.36 22.40
Out-of-vehicle channel description in UWB
Path Loss
The PL (𝛼) data is computed from VNA record through averaging the inverse of
squared CTF over the whole frequency range
⎡
𝛼 = 10 log10 ⎣
1
𝑁∑︁
VNA
𝑁VNA
𝑛=1
⎤
1
⎦
|𝐻(𝑓𝑛 )|2
(4.7)
where the index 𝑛 is used to signify discrete tones generated by VNA.
The PL values calculated from (4.7) are listed in Table 4.15. Unavailability
of data is occurred at (9m, 90∘ ) due to walls and at (3m, 0∘ ) the car bonnet ob-
50
Fig. 4.28: BER results based on the 802.11p over measured channels.
structed the measurement location. As expected, the PL value increase monotonically with Tx-Rx separation following a log-distance model for a given angle and
antenna height. However, for a particular distance, random PL variation around
the average loss value is observable when the angle is changed. This indicates that
received signal strength indication (RSSI) based ranging and localization algorithms
would perform poorly around a car. Another interesting aspect is, although the elevation and Tx-Rx separation do not change much with antenna height, there is
a large increase in the PL when the Tx antenna is brought down from 1.5m (Rx
antenna level) to 1m above the ground.
4.6.2
PDP Clusters
In this subsection, we would analyze the PDP variability, when the PDP can be
characterized with clustered propagation as described by the S-V model. According
to the S-V model, the discrete impulse response may be expressed as
ℎ(𝑡) =
𝑟,𝑛
𝑁𝑐 𝑁
∑︁
∑︁
𝛽𝑚,𝑛 exp(𝑗𝜃𝑚,𝑛 )𝛿(𝑡 − 𝑇𝑛 − 𝜏𝑚,𝑛 )
(4.8)
𝑛=1 𝑚=1
where 𝑁𝑐 is the number of clusters, 𝑁𝑟,𝑛 is the number of rays in the 𝑛th cluster,
and 𝑇𝑛 is the arrival time of the 𝑛th cluster. The magnitude, phase, and additional delay of the 𝑚th ray within the 𝑛th cluster are given by 𝛽𝑚,𝑛 , 𝜃𝑚,𝑛 , and 𝜏𝑚,𝑛 ,
respectively. The inter- and intra-cluster exponential decay rates, namely Γ and
2
2
𝛾, define the magnitude of individual rays according to 𝛽𝑚,𝑛
= 𝛽1,1
exp[−(𝑇𝑛 −
51
Tab. 4.15: PL values (in dB) for all Tx locations.
Tx-Rx
distance (m)
3.0
4.5
6.0
7.5
9.0
Tx-Rx
distance (m)
3.0
4.5
6.0
7.5
9.0
Tx height (ℎ) = 1.5m
Tx angles
∘
∘
0
45
90∘
135∘ 180∘ Average
58.57 58.99 60.99 60.82
59.84
61.82 62.65 62.58 64.47 64.13
63.13
64.30 65.16 64.91 66.10 66.24
65.34
65.94 67.23 66.65 68.41 68.03
67.25
67.42 68.31
70.02 70.01
68.94
Tx height (ℎ) = 1m
Tx angles
0∘
45∘
90∘
135∘ 180∘ Average
64.33 61.73 62.10 61.38
62.39
66.50 66.34 64.32 64.45 64.13
65.15
67.49 67.83 66.27 66.54 66.45
66.92
68.65 69.51 67.93 68.19 68.46
68.55
69.67 69.91
69.69 69.53
69.70
𝑇1 )/Γ] exp(−𝜏𝑚,𝑛 /𝛾). The arrival time of clusters and rays within clusters follow independent Poisson processes, i.e. Pr(𝑇𝑛 |𝑇𝑛−1 ) = Λ exp[−Λ(𝑇𝑛 − 𝑇𝑛−1 )] and
Pr(𝜏𝑚,𝑛 |𝜏𝑚−1,𝑛 ) = 𝜆 exp[−𝜆(𝜏𝑚,𝑛 − 𝜏𝑚−1,𝑛 )], where Pr(·) denotes probability, and Λ
and 𝜆 represent cluster and ray arrival rates, respectively.
Figures 4.29 and 4.30 depict the PDPs at two indicative distances, namely at
3 m and 9 m, for the three different Tx angles, 45∘ , 135∘ , and 180∘ . The MPCs
in the PDPs are grouped into clusters, and the overall PDPs are described with
S-V channel model. The individual clusters in each PDP are calculated using the
automatic clustering algorithm described in [104] and validating the correctness of
the algorithm by visual inspection. The automatic clustering algorithm is based on
identifying the changes in the slope of the impulse response based on the assumption
that all changes in the slope correspond to the start of a new cluster.
From Figure 4.29 one can find that the cluster arrival times do not alter significantly with Tx angle. However, the number of resolvable clusters reduces from 4
to 3, when antenna height is changed from 1 m to 1.5 m. For longer distances, as
seen in Figure 4.30, the signal reaches the noise level (-110 dB) within a short delay
span and only two distinct clusters are observable. The effect of antenna height is
also no longer visible when Tx-Rx separation is large.
In Table 4.16, we put the S-V parameter values computed using the mathematical
52
h = 1m, d = 3m, angle 450
h = 1.5m, d = 3m, angle 450
-60
PDP [dB]
PDP [dB]
-60
-80
-100
-120
0
5
10
Delay [ns]
h = 1m, d = 3m, angle 135
15
h = 1.5m, d = 3m, angle 135
PDP [dB]
PDP [dB]
-100
5
10
Delay [ns]
h = 1m, d = 3m, angle 180
0
-80
-100
-120
0
15
0
5
10
Delay [ns]
15
h = 1.5m, d = 3m, angle 180
0
-60
PDP [dB]
-60
PDP [dB]
5
10
Delay [ns]
-60
-80
-80
-100
-120
0
-100
0
-60
-120
0
-80
-120
0
15
PDP
Local peaks
Cluster
5
10
Delay [ns]
15
-80
-100
-120
0
5
10
Delay [ns]
15
Fig. 4.29: PDPs for Tx-Rx separation (𝑑) = 3m.
expressions given in [105]. The variations with angles, as described before, are not
large and is not mentioned in the table. The ray decay, 𝛾, for 1 m height is lower
than 1.5 m, as there are more clusters with shorter durations, i.e. having a larger
1/Λ value. However, 𝛾 increases with distance for 1 m as number of clusters reduce,
which is not necessarily the case for 1.5 m. The cluster decay, Γ, decreases with
distance for both antenna heights, as for larger distances there are only two cluster
heads and the decay slope is high. The ray arrival time is almost constant for all the
cases. The values are also quite close to the values for UWB channel in the range of 410 m (CM3), Γ = 14, 𝛾 = 7.9, 1/Λ = 15, 1/𝜆 = 0.48, all in nanoseconds, as reported
in the IEEE 802.15.3 standard [106]. A direct comparison is not straightforward
as the measurements for the standard are done for NLOS whereas most of our
measurements had a clear LOS path.
53
-80
-100
-120
0
h = 1.5m, d = 9m, angle 450
PDP [dB]
PDP [dB]
h = 1m, d = 9m, angle 450
5
Delay [ns]
10
-100
-120
0
-100
-120
0
5
Delay [ns]
10
-100
-120
0
-120
0
5
Delay [ns]
5
Delay [ns]
10
-80
-100
-120
0
5
Delay [ns]
Fig. 4.30: PDPs for Tx-Rx separation (𝑑) = 9m.
Tab. 4.16: S-V parameter values
S-V
parameters
Γ
𝛾
1/Λ
1/𝜆
S-V
parameters
Γ
𝛾
1/Λ
1/𝜆
10
h = 1.5m, d = 9m, angle 1800
PDP [dB]
PDP [dB]
-100
10
-80
h = 1m, d = 9m, angle 1800
-80
5
Delay [ns]
h = 1.5m, d = 9m, angle 1350
PDP [dB]
PDP [dB]
h = 1m, d = 9m, angle 1350
-80
PDP
Local peaks
Cluster
-80
Tx height (ℎ) = 1.5m
Tx-Rx distance (𝑑) in m
(ns) 3.0
4.5
6.0
7.5
18.80 19.54 25.46 4.60
10.43 8.81 5.31 7.57
4.99 4.93 6.67 5.60
0.40 0.47 0.52 0.45
Tx height (ℎ) = 1.5m
Tx-Rx distance (𝑑) in m
(ns) 3.0
4.5
6.0
7.5
16.19 16.70 11.56 12.52
4.99 6.31 6.16 6.32
3.98 5.01 5.34 4.48
0.42 0.48 0.43 0.43
54
9.0
2.46
4.55
5.88
0.36
9.0
3.18
5.67
5.88
0.39
10
5
LOCALIZATION USING OUT-OF-VEHICLE
MEASUREMENTS IN UWB
5.1
Distance estimation
The frequency domain data obtained through the experiments consists of discrete
channel frequency response recorded at equally spaced 𝑁 (= 801) points spanning
the entire ultra-wide bandwidth of 𝐵𝑤 = 8 GHz. Post-processing of this frequency
domain data involves IFFT operation in combination with a rectangular window.
This window is generally recommended for applications where good separation between the MPCs in the resultant PDP is required [91]. The corresponding distance
resolution is given by the ratio
𝑐
(5.1)
𝑑𝑟 =
𝐵
where 𝑐 = 2.998 × 108 m/s and inverse of the bandwidth is equal to the time
resolution
1
1
𝑇𝑟 =
=
(5.2)
𝐵
𝑓max − 𝑓min
where 𝑓min (= 3GHz) and 𝑓max (= 11GHz) are the lowest and highest frequency points
of the data set. The maximum propagation distance measured
𝑑max =
𝑐
𝑓𝑠
(5.3)
depends on the frequency step 𝑓𝑠 ; 𝑓𝑠 = 𝐵/(𝑁 − 1).
For calculating the distances between Tx and Rx antennas TOA technique is
used. The TOA is based on the detection of the first ray received by a particular
Rx antenna. This is achieved by a search algorithm that compares individual signal
samples of the PDP with a certain threshold to avoid noise and identify the first
amplitude peak that corresponds to the LOS time delay, 𝑇𝑑 . The distance between
Tx and Rx antennas is calculated by
𝑑 = 𝑇𝑑 × 𝑐
(5.4)
In Figure 5.1, we superimpose all PDPs corresponding to Rx1 obtained for different Tx antenna positions. The PDPs are plotted in the distance domain instead
of the delay domain for better understanding. The PDPs for same Tx-Rx separation
but with different angles with Rx1 -Rx2 -Rx3 axis form distinct groups. Thus we may
conclude that there is almost no effect of angular variation in distance estimation.
Further, the peaks in the same group overlaps with each other and is very close to
the actual distance shown with red dotted lines.
55
-15
-20
-25
-30
PDP [dB]
-35
-40
-45
-50
-55
-60
-65
-70
0
1
2
3
4 4.5 5
6
Distance [m]
7 7.5 8
9
10
Fig. 5.1: Superposition of PDPs for Rx1 for all Tx positions.
Table 5.1 gives the real and calculated distance between Tx and Rx1 for all the 23
different measurement points. In most of the cases the deviation is about 2.5% with
the maximum deviation being 5% which translates to an error of 15 cm at a distance
of 3 m. The same information for Rx2 and Rx3 are given in Tables 5.2 and 5.3 where
a maximum of 6% deviation is noticed. The maximum deviation is for measurement
no. 10 for all three Rx antennas, and the deviation for all other measurements are
very low indicating some measurement error that might have occurred during that
particular measurement. Even though the error is 15 cm, 16 cm, and 17 cm for Rx1 ,
Rx2 , and Rx3 respectively, which is small compared to the dimension of the vehicle.
While analyzing Tables 5.1, 5.2 and 5.3, one may find a positive bias in the
calculated distance, i.e. the calculated distance is always longer than the actual
distance. It is interesting to see some correlation among the error statics for all
the three Rx antennas, both on on the roof and on the windshield. This may be
related with the calibration plane and phase center of the antenna. The VNA was
calibrated only for the coaxial cables without considering coaxial adapters of the
antennas. Also due to the discrete nature of the PDP time axis, the first peak
can not be detected accurately. The distances between Rx and Tx antennas were
measured by a ruler, and there is also a possibility of error in the measurement.
56
Tab. 5.1: Distance of Rx1 from Tx antenna positions
Measurement Position
Real
(degrees) distance (m)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
5.1.1
0
45
90
135
180
Measured
distance (m)
Deviation
(m)
Deviation
(%)
4.6125
6.1125
7.65
9.15
3.0375
4.6125
6.15
7.6125
9.225
3.15
4.6125
6.15
7.6125
3.1125
4.575
6.1125
7.6125
9.225
3.0375
4.5
6.0375
7.575
9.075
0.11
0.11
0.15
0.15
0.04
0.11
0.15
0.11
0.23
0.15
0.11
0.15
0.11
0.11
0.08
0.11
0.11
0.23
0.04
0.0
0.04
0.08
0.07
2.5
1.88
2
1.67
1.25
2.5
2.5
1.5
2.5
5
2.5
2.5
1.5
3.75
1.67
1.88
1.5
2.5
1.25
0
0.62
1
0.83
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
3.0
4.5
6.0
7.5
9.0
3.0
4.5
6.0
7.5
9.0
Delay distance dependence
^ and the
Figure 5.2 shows the linear relationship between the Tx-Rx distance (𝑑)
associated delay (𝑇^𝑑 ). The polynomial fitting of measurement results allows us to
construct substantially similar linear functions for all receivers in the form:
𝑑^ = 𝑎 × 𝑇^𝑑 + 𝑏
(5.5)
The results of the fitting are shown in Table 5.4. The calculated coefficients, 𝑎 and
𝑏, are fairly constant for all three Rx while sum of squares due to error (SSE) as
well as and root mean square error (RMSE) are close to zero and the coefficient
of determination (𝑅-square) is close to unity. This indicates the goodness of fit
57
Tab. 5.2: Distance of Rx2 from Tx antenna positions
Measurement
Real
distance (m)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
3.37
4.87
6.37
7.87
2.34
3.79
5.26
6.75
8.24
3.21
4.64
6.11
7.58
3.88
5.36
6.85
8.34
9.83
4.13
5.63
7.13
8.63
10.13
for Rx2
Measured
distance (m)
Deviation
(%)
3.45
4.95
6.49
8.02
2.36
3.86
5.4
6.86
8.5
3.37
4.76
6.22
7.69
4.01
5.44
6.94
8.44
10.05
4.2
5.62
7.2
8.74
10.24
2.37
1.64
1.84
1.91
0.9
2.01
2.62
1.69
3.16
5.25
2.65
1.96
1.36
3.36
1.47
1.34
1.2
2.22
1.69
0.09
0.98
1.25
0.06
and based on the coefficients for the linear function, it is possible to interpolate the
distance for other values of LOS path delay.
5.2
Two dimensional localization
Next, we describe the simple two dimensional (2-D) localization performed from
the distance estimates as obtained from the first peak detection of PDPs in the
distance domain. Two receivers on the car roof, Rx1 and Rx2 , are used to locate
the Tx antenna as the Tx antenna is placed at same height, and it is sufficient to
58
Tab. 5.3: Distance of Rx3 from Tx antenna positions
Measurement
Real
distance (m)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
2.33
3.82
5.32
6.82
2.14
3.35
4.72
6.16
7.62
3.73
5.01
6.39
7.82
4.81
6.25
7.71
9.18
10.66
5.2
6.69
8.19
9.69
11.19
for Rx3
Measured
distance (m)
Deviation
(%)
2.47
3.94
5.47
7.01
2.21
3.45
4.91
6.3
7.87
3.9
5.1
6.49
7.91
4.95
6.34
7.8
9.26
10.87
5.25
6.71
8.25
9.79
11.29
6.22
3.08
2.91
2.82
3.22
3.07
4
2.31
3.38
4.69
1.74
1.47
1.2
2.86
1.41
1.17
0.86
1.98
1.03
0.27
0.69
0.98
0.85
Tab. 5.4: Parameters of polynomial fitting
Receiver
Rx1
Rx2
Rx3
Average
a
3.38
3.38
3.34
3.36
×10−9
×10−9
×10−9
×10−9
b
6.81
9.65
3.27
1.75
×10−11
×10−11
×10−10
×10−10
SSE
R-square
RMSE
7.96 ×10−19
9.36 ×10−19
1.062 ×10−18
2.92 ×10−18
0.9993
0.9993
0.9993
0.9993
1.86 ×10−10
2.02 ×10−10
2.15 ×10−10
2 ×10−10
find only 2-D coordinates. Figure 5.3 summarizes the geometrical topology where
the co-ordinate system has the unknown Tx antenna node 𝐵(𝑥𝑇 𝑥 , 𝑦𝑇 𝑥 ) at the origin
59
-8
4
x 10
3.5
Delay [s]
3
2.5
2
Rx1 calculated
Rx1 measured
1.5
Rx measured
2
Rx calculated
2
Rx measured
1
3
Rx3 calculated
0.5
2
3
4
5
6
7
Distance [m]
8
9
10
11
Fig. 5.2: Delay versus distance.
and node 𝐴(𝑥𝑅𝑥1 , 𝑦𝑅𝑥1 ) and node 𝐵(𝑥𝑅𝑥2 , 𝑦𝑅𝑥2 ) denote the location of Rx1 and Rx2 ,
respectively. It may be noted that 𝑦𝑅𝑥1 = 𝑦𝑅𝑥2 , because Rx1 and Rx2 are on the
same axis, and 𝑥𝑅𝑥1 − 𝑥𝑅𝑥2 = 1.13m, as evident from the placement in Figure 4.24.
y
C(xRx2 , yRx2 )
yRx1
A(xRx1 , yRx1 )
β
α
B(xT x , yT x )
xRx2
xRx1
x
Fig. 5.3: The principle of 2D localization.
As the actual co-ordinates of Rx1 are known (relative to the parking lot floor
plan), it is enough to find the relative co-ordinates (𝑥𝑅𝑥1 , 𝑦𝑅𝑥1 ) in order to locate
60
the Tx antenna. Let 𝛼 be the angle between 𝐴𝐵 and 𝑋−axis, then
𝑥𝑅𝑥1 = 𝐵𝐴 cos 𝛼
(5.6)
𝑦𝑅𝑥1 = 𝐵𝐴 sin 𝛼
(5.7)
From the theory of parallel line angles, it is easy to show that the angle 𝛼 is equal
to an alternate angle 𝛽 between 𝐶𝐴 and 𝐵𝐴. Also it is possible to find the angle 𝛽
from the sides of Δ𝐴𝐵𝐶
(︃
𝛽 = cos
−1
𝐶𝐴2 + 𝐵𝐴2 − 𝐵𝐶 2
2 × 𝐶𝐴 × 𝐵𝐴
)︃
(5.8)
The side 𝐶𝐴 is fixed and equal to 𝑥𝑅𝑥1 − 𝑥𝑅𝑥2 , whereas 𝐵𝐴 and 𝐵𝐶 are distances
calculated using LOS delays of PDP of 𝑅𝑥1 and 𝑅𝑥2 , respectively. Thus, combining
(5.6, 5.7) and (5.8) through the relation 𝛼 = 𝛽, we obtain
𝐶𝐴2 + 𝐵𝐴2 − 𝐵𝐶 2
= 𝐵𝐴 × cos cos
2 × 𝐶𝐴 × 𝐵𝐴
2
2
𝐶𝐴2 + 𝐵𝐴2 − 𝐵𝐶 2
𝐶𝐴 + 𝐵𝐴 − 𝐵𝐶 2
=
= 𝐵𝐴 ×
2 × 𝐶𝐴 × 𝐵𝐴
2 × 𝐶𝐴
[︃
𝑥𝑅𝑥1
and
(︃
)︃]︃
−1
[︃
(︃
𝑦𝑅𝑥1 = 𝐵𝐴 × sin cos
−1
𝐶𝐴2 + 𝐵𝐴2 − 𝐵𝐶 2
2 × 𝐶𝐴 × 𝐵𝐴
(5.9)
)︃]︃
(5.10)
Fig. 5.4: Actual and estimated position of the Tx antenna.
In the second step, a detailed map of calculated Tx coordinates are constructed
in Fig. 5.4 and compared with the measured coordinates. The goal of constructing
61
this 2-D localization map is to assess whether the simple TOA algorithm results
corresponds at least roughly to reality, i. e. whether it possible to reliably detect a
vehicle on a particular parking lot. In our situation it is not necessary to have an
accurate coordinates (in mm) but only rough estimation of the car position (in cm).
Table 5.5 lists the measured and calculated coordinates of Tx antenna along with
the deviation for each measured location. The deviation is few centimeters at most
and indicates that UWB is robust in locating other cars in the vicinity.
The main problem associated with the localization using two receivers is the
inability to determine the zone location of the transmitter relative to the receiver
axis. However, in this particular case, it was not a serious issue as we considered
only one side of the vehicle with LOS scenario for roof-antennas.
Tab. 5.5: Comparison between measured and calculated coordinates of Tx
Measurement
𝑥𝑎
𝑦𝑎
𝑥𝑎
𝑦𝑎
𝑥𝑎
𝑦𝑎
number
measured measured calculated calculated deviation deviation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
4.50
6.00
7.50
9.00
2.12
3.17
4.25
5.29
6.36
-0.01
0.00
-0.02
0.03
-2.11
-3.19
-4.27
-5.32
-6.35
-3.00
-4.50
-6.00
-7.50
-9.00
0.00
0.00
0.00
0.00
2.12
3.19
4.23
5.31
6.37
3.00
4.50
6.00
7.50
2.13
3.18
4.22
5.28
6.38
0.00
0.00
0.00
0.00
0.00
4.71
6.26
7.84
9.11
2.18
3.38
4.40
5.37
5.87
-0.08
-0.06
0.15
0.06
-2.27
-3.26
-4.20
-5.30
-6.47
-3.16
-4.48
-6.24
-7.83
-9.37
62
0.00
0.00
0.00
0.00
2.12
3.14
4.30
5.40
6.78
3.15
4.61
6.15
7.61
2.13
3.21
4.44
5.47
6.57
0.00
0.47
0.00
0.00
0.00
0.21
0.26
0.34
0.11
0.05
0.21
0.15
0.07
-0.49
-0.07
-0.06
0.18
0.03
-0.16
-0.07
0.07
0.03
-0.12
-0.16
0.02
-0.24
-0.33
-0.37
0.00
0.00
0.00
0.00
0.00
-0.05
0.07
0.08
0.42
0.15
0.11
0.15
0.11
0.00
0.04
0.22
0.19
0.20
0.00
0.47
0.00
0.00
0.00
6
CONCLUSION
This dissertation thesis is focused on the wireless systems aimed at intra-vehicle and
out-of-vehicle communications operating in narrow- and ultra-wide bands.
A description of measurements results as well as the description of a channel
measurement setups realized for the 5 GHz band and UWB systems are given in the
state-of-the-art part. To provide a more specific insight on vehicle communication
and localization systems, the IEEE 802.11p standard and 3-11 GHz ultra wide band
systems have been chosen as indicative examples which currently happens to be the
major and the most promising vehicular communication systems, respectively.
When summarized the main outcomes of the dissertation are:
• Simulation models describing IEEE 802.11p and 802.11a standards are created
and the simulation results are compared. The BER performance gives the best
results for the model 802.11p for channels with large delay. The 802.11p protocol is more suitable for vehicle communications, where a signal is transmitted
over long distances. This is confirmed during the calculation of the BER for
different speeds of movement of the receiver experiencing the Doppler effect.
• Extensive intra-vehicle channel measurements in the 5.8 GHz band were carried out. A double exponential decay model was used for describing the basic
trend of the measured PDPs and we utilized it for IEEE 802.11p BER simulation. The BER achieves the recommended values for all variants of the
channel, and it can be concluded that 802.11p standard (in particular the
PHY protocol stack) can be adopted for in-vehicle communication systems.
• Channel measurements outside car were carried out in the 5.8-GHz and 3-11
GHz bands in an underground garage with different Tx-Rx locations.
For the IEEE 802.11p channel model, a double exponential decay model was
used to separate LSV from measurement PDP. The LSVs were utilized to simulate BER and the BER results satisfy the recommended values for all locations.
An average channel model is proposed that can be used for communication
link design outside the car.
Results for a comprehensive measurement campaign to study the variations of
the channel characteristics are reported for UWB propagation around a parked
car. It was found that MPCs can be roughly grouped into clusters following
the well-known S-V model. The cluster profile do not alter significantly with
angle but changes with antenna height. Also, the number of clusters reduce
with distance as it becomes difficult to segregate clusters. PL values and delay
of the first arriving MPC are also reported which serve as guidelines for ranging
algorithms.
• Based on the measurements results in 3-11 GHz band it is possible to uti-
63
lize a distance-delay linear relationship which is exploited to find distance of
the transmitting antenna from receiver antennas. The estimated distances are
used for TOA based 2-D localization with only two receivers and the calculated coordinates exhibits small error. The proposed solution is sufficient to
determine the positions of nearby vehicles in an underground garage. Obviously the simple mechanism is unable to provide the information whether the
car is on left/right side of the roll axis. However, an extension with a third
receiver which is not on the same axis can solve the problem.
64
LIST OF ABBREVIATIONS
2G
Second Generation
3G
Third Generation
4G
Fourth Generation
ASTM
American Society for Testing and Materials
BER
Bit Error Rate
BPSK
Binary Phase Shift Keying
C2C-CC
Car 2 Car Communication Consortium
CDF
Cumulative Distribution Function
CDMA
Code Division Multiple Access
CIR
Channel Impulse Response
CTF
Channel Transfer Function
DSRC
Dedicated Short Range Communications
FCC
Federal Communications Commission
FEEC
Faculty of Electrical Engineering
FFT/IFFT
fast Fourier transform / inverse fast Fourier transform
GEV
Generalized Extreme Value
GLONASS
GLObal NAvigation Satellite System
GNSS
Global Navigation Satellite System
GPRS
General Packet Radio Service
GSM
Global System for Mobile Communications
ISI
Intersymbol interference
FFT/IFFT
fast Fourier Transform / inverse
ITS
Intelligent Transportation System
65
ITU–R
International Telecommunication Union
Radiocommunication Sector
K-S test
Kolmogorov-Smirnov test
LOS
Line-of-Sight
LS
Least Squares
LSV
Large-Scale Variation
LTE
Long-Term Evolution
M-2-M
Machine to Machine
MIMO
Multiple Input Multiple Output
MPC
Multipath Components
MSE
Mean Square Error
NLOS
non Line-of-Sight
OFDM
Orthogonal Frequency-Division Multiplexing
OSI
Open Systems Interconnection
PDF
Probability Density Function
PDP
Power Delay Profile
PHY
Physical Layer
PL
Path loss
QAM
Quadrature Amplitude Modulation
QPSK
Quadrature Phase Shift Keying
RF
Radio frequency
RMS
Root Mean Square
RMSE
Root Mean Square Error
RSSI
Received Signal Strength Indication
Rx
Receiver
66
SIMO
Single Input Multiple Output
SISO
Single Input Single Output
SSE
Squares Due to Error
SSV
Small-Scale Variation
TOA
Time of Arrival
Tx
Transmitter
UWB
Ultra-Wide Band
V-2-I
Vehicle to Infrastructure
V-2-V
Vehicle to Vehicle
V-2-X
Vehicle to X
VAN
Vehicle Area Network
VANET
Vehicular Ad Hoc Network
VNA
Vector Network Analyzer
WAVE
Wireless Access in Vehicular Environments
WCDMA
Wideband Code Division Multiple Access
WLAN
Wireless Area Network
67
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A
LIST OF PUBLICATION
1. Kukolev, P.; Chandra, A.; Mikulasek, T.; Prokes, A.; Zemen, T.; Mecklenbrauker, C. In-vehicle channel sounding in the 5.8 GHz band. EURASIP
Journal on Wireless Communications and Networking, 2015, roč. 2015, č. 1,
s. 1-9. ISSN: 1687-1499.
2. Kukolev, P.; Chandra, A.; Mikulasek, T.; Prokes, A. Out of vehicle channel sounding in 5.8 GHz band. In 7th International Workshop on Reliable
Networks Design and Modeling. 2015. s. 341-344. ISBN: 978-1-4673-8049- 2.
3. Kukolev, P. BER performance of 802.11p standard over an ITU-R Multipath channel. In Proceedings of 23th International Conference RADIOELEKTRONIKA 2013. 2013. s. 391-396. ISBN: 978-1-4673-5517- 9.
4. Kukolev, P. Comparison of 802.11a and 802. 11p over fading channels. Elektrorevue - Internetový časopis (http://www.elektrorevue.cz), 2013, roč. 2013,
č. 2, s. 7-11. ISSN: 1213- 1539.
5. Chandra, A.; Prokes, A.; Mikulasek, T.; Blumenstein, J.; Kukolev, P.; Zemen,
T.; Mecklenbrauker, C. Frequency-Domain In-Vehicle UWB Channel Modeling. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, roč.
PP, č. 99, s. 1-12. ISSN: 0018-9545.
6. Chandra, A.; Prokes, A.; Blumenstein, J.; Kukolev, P.; Vychodil, J.; Mikulasek, T.; Zemen, T.; Mecklenbrauker, C. Intra Vehicular Wireless Channel
Measurements. In European Project Space on Information and Communication Systems. Portugal: SCITEPRESS – Science and Technology Publications, Lda., 2016. s. 3-28. ISBN: 978-989-758-155- 7.
7. Chandra, A.; Kukolev, P.; Mikulasek, T.; Prokes, A. Frequency-Domain InVehicle Channel Modelling in mmW Band. In In 1st International Forum on
Research and Technologies for Society and Industry (RTSI 2015). 2015. s.
1-5. ISBN: 978-1-4673-8166- 6.
8. Chandra, A.; Kukolev, P.; Mikulasek, T.; Prokes, A. Autoregressive Model of
Channel Transfer Function for UWB Link inside a Passenger Car. In Proceedings of the 19th International Conference on Communications (part of CSCC
’15). WSEAS Applied Informatics and Communications. 2015. s. 238-241.
ISBN: 978-1-61804-318- 4. ISSN: 1790-5117.
9. Kukolev, P.; Chandra, A.; Prokes, A. BER Performance of 802.11p in SISO,
MISO, and MIMO Fading Channels. In Proceedings of the 19th International
Conference on Communications (part of CSCC ’15). WSEAS Applied Informatics and Communications. 2015. p. 89-93. ISBN: 978-1-61804-318- 4.
ISSN: 1790- 5117.
10. Kukolev, P.; Chandra, A.; Prokes, A.; Mikulasek, T. UWB channel variability
79
around a car in an underground parking lot. IEEE Antennas and Wireless
Propagation Letters. VOL. XX, 2016. Submitted.
11. Kukolev, P.; Chandra, A.; Mikulasek, T.; Prokes, A. Out-of-Vehicle TOA
based Localization in Ultra-Wide Band. Advances in Vehicular Sensor Networks and Urban Computing. International Journal of Distributed Sensor
Networks. Hindawi Publishing Corporation. 2016. Submitted.
80