JUSSI TURKKA COVERAGE DIMENSIONING FOR A MOBILE-TO-MOBILE WIRELESS SYSTEM MASTER OF SCIENCE THESIS

Examiner: Professor Jukka Lempiäinen Professor Markku Renfors

SUBJECT APPROVED BY DEPARTMENT COUNCIL ON 5th MARCH, 2008

I

PREFACE This Master of Science Thesis, “Coverage Dimensioning for a Mobile-to-Mobile wireless system”, has been one part of the dCOM project funded by Nokia. The project was done during late autumn 2007 and spring 2008 while I was working at the Department of Communications Engineering at Tampere University of Technology, Finland. I would like to thank my supervisors Professor Jukka Lempiäinen and Professor Markku Renfors for all their advices, support and help as well as the inspiring working conditions they provided for me. I would also like to thank the other project participants and especially Jouni Kössi, Kari Rissanen and Jukka Saarinen from Nokia. Also European Communications Engineering (ECE) Ltd for providing the measurement equipment, Jukka Talvitie and Toni Levanen for the all innovative discussions and constructive criticisms we had during the project, and hopefully after the project as well. Also Special thanks go to my colleagues Tero, Teemu and Panu from Radio Network Group for all the help they provided for me before, during and after the thesis writing process. Finally, I would like to thank my parents Ulla and Tuomo for the support they have been providing for me during these years as well as all my brothers for the time we have been able to spend together at Jurkkola. Tampere, 25th of May, 2008 Jussi Turkka jussi.turkka@iki.fi Tekniikankatu 10 B 65 33720 Tampere FINLAND Tel. +358 44 286 8722

II

ABSTRACT TAMPERE UNIVERSITY OF TECHNOLOGY Master’s Degree Programme in Information Technology Department of Communications Engineering TURKKA, JUSSI: Coverage dimensioning for a mobile-to-mobile wireless system Master of Science Thesis, 68 pages August 2008 Examiner: Prof. Jukka Lempiäinen and Prof. Markku Renfors Funding: National Technology Agency of Finland (TEKES) Nokia Keywords: Coverage dimensioning, Radio channel modelling, Empirical

propagation model, 900 MHz mobile-to-mobile wireless system, Path loss measurements.

All new wireless systems entering to the emerging markets must undergo a careful planning process where coverage, capacity, mobility, and cost-efficiency issues are investigated and optimized. Coverage dimensioning is an essential part of wireless system design process and it is used for verifying whether or not the system under design is capable of meeting the given coverage requirements. The main purpose of this thesis is to provide propagation model parameters which can be used for the coverage dimensioning of a wireless system operating in a mobile-to-mobile environment. However, the main problem was the unavailability of propagation prediction models which would be easy to use and correspond to the mobile-to-mobile environment where the transmitter and the receiver are located close to the ground and operate with relatively low transmission power. This thesis proposes path loss exponent and location variability parameters for a simple power law path loss model which can be used for predicting coverage areas for a mobile-to-mobile wireless system. The path loss model parameters are derived for a typical Finnish urban and suburban environments based on measurements in Tampere. Moreover, these results can be used in the design of future cognitive radio networks, relay radio communications and sensor networks which are surely going to be a part of future communication networks. The results of this thesis show that the propagation environment is more challenging in case of a mobile-to-mobile environment compared with a traditional base station to mobile environment. This indicates that signal attenuates faster and varies more around the mean value. Therefore, the size of the propagation region is reduced dramatically in case of mobile-to-mobile communications.

III

TIIVISTELMÄ TAMPEREEN TEKNILLINEN YLIOPISTO Tietotekniikan koulutusohjelma TURKKA, JUSSI: Peittosuunnittelu mobiilista mobiiliin -radiojärjestelmälle Diplomityö, 68 sivua Elokuu 2008 Pääaine: Tiedonsiirtotekniikka Tarkastaja: Professori Jukka Lempiäinen ja Professori Markku Renfors Rahoitus: NOKIA ja TEKES Avainsanat: Peittosuunnittelu, Radiokanavan mallinnus, Empiiriset etenemismallit,

Mobiilista mobiiliin radiojärjestelmä, radiokanavamittaukset Kaikkien uusien radiojärjestelmien, joiden halutaan pääsevän mukaan kasvaville markkinoille, tulee käydä läpi huolellinen suunnitteluprosessi. Tämän prosessin avulla optimoidaan suunniteltavan järjestelmän peitto-, kapasiteetti-, liikuteltavuus- ja kustannustehokkuusominaisuudet. Yksi keskeinen osa suunnitteluprosessia on peittosuunnittelu, jossa määritetään etäisyys sille, miten kaukana radiolähettimestä vastaanotin voi olla. Keskeinen ongelma joka johti tähän diplomityön tekemiseen oli se, että sopivaa radiokanavamallia mobiilista mobiiliin -radiojärjestelmälle ei ollut saatavilla. Diplomityön tarkoituksena on löytää radiokanavamallille sopivat parametrit, joiden avulla peittosuunnitelu voidaan tehdä. Diplomityössä tutkittiin mobiilista mobiiliin radiokanavanominaisuuksia tyypillisessä suomalaisessa kaupunki- ja lähiöympäristöissä Tampereen seudulla. Diplomityön tuloksena löytyivät parametrit vaimenemiseksponentille ja signaalin hitaan häipymän keskihajonnalle. Näiden parametrien avulla yksinkertainen ja helpokäyttöinen radiokanavamalli voidaan määritellä. Mallin parametrit johdettiin kenttämittausten avulla tutkimalla vastaanotetun signaalin tehoa etäisyyden funktiona. Diplomityön tuloksia voidaan hyödyntää tulevaisuudessa suunniteltaessa älykkäämpiä radioverkkoja, toistinverkkoja ja sensoriverkkoja, joissa kommunikointi tapahtuu mobiilista mobiiliin ympäristössä. Diplomityön tulosten perusteella mobiilista mobiiliin ympäristön havaittiin olevan haastavampi radioympäristö verrattuna radioverkkoihin missä kommunikointi tapahtuu tukiasemasta mobiiliin. Signaalin havaittiin vaimenevan nopeammin ja poikkeavan enemmän keskiarvostaan, jos sekä lähetimen, että vastaanottimen antennit ovat sijoitettuna lähelle maatasoa. Tämä pienentää olennaisesti kommunikointietäisyyksiä kahden mobiilin välillä.

IV

TABLE OF CONTENTS  

1.  Introduction ............................................................................................................. 1 2.  Mobile Radio Channel ............................................................................................ 3 

2.1.  Radio Channel Modeling .................................................................................. 3 2.2.  Small Scale Fading ............................................................................................ 4 

2.2.1.  Channel Statistical Behavior ................................................................ 5 2.2.2.  Time Dispersive Behavior of Radio Channel ...................................... 5 2.2.3.  Frequency Dispersive Behavior of Radio Channel.............................. 7 2.2.4.  Second Order Statistics ........................................................................ 9 

2.3.  Large Scale Propagation ................................................................................... 9 2.3.1.  Large Scale Propagation Modeling.................................................... 10 2.3.2.  Power Law Path Loss Model ............................................................. 11 2.3.3.  Shadow Fading .................................................................................. 12 

3.  Mobile-to-Mobile Radio Environment ................................................................ 14 3.1.  Small Scale Characteristics ............................................................................. 14 

3.1.1.  Statistical Model for Mobile-to-Mobile Communications ................ 15 3.1.2.  Doppler Spectrum for Mobile-to-Mobile Communications .............. 18 3.1.3.  Second Order Statistics for Mobile-to-Mobile Communications ...... 20 

3.2.  Large Scale Characteristics ............................................................................. 22 4.  Coverage Dimensioning Process .......................................................................... 24 

4.1.  Dimensioning Process Phases ......................................................................... 24 4.2.  Maximum Allowable Path Loss ...................................................................... 26 4.3.  Link Budget ..................................................................................................... 27 4.4.  Planning Margins ............................................................................................ 29 

4.4.1.  Shadow Fading Margin ...................................................................... 29 4.4.2.  Other Planning Margins ..................................................................... 32 

5.  Propagation Model Tuning .................................................................................. 33 5.1.  Research Problem ........................................................................................... 33 5.2.  Model Tuning .................................................................................................. 34 

5.2.1.  Model Tuning Process ....................................................................... 34 5.2.2.  Method for Solving the Model Parameters ........................................ 36 

6.  Path Loss Measurements ...................................................................................... 39 6.1.  Measurements Configuration .......................................................................... 39 6.2.  Measurements Environment ........................................................................... 41 

6.2.1.  Kissanmaa Region ............................................................................. 41 6.2.2.  Tammela Region ................................................................................ 43 6.2.3.  Hervanta Region ................................................................................ 45  

V

6.3.  Measurement Results ...................................................................................... 48 6.3.1.  Results for Kissanmaa Region ........................................................... 48 6.3.2.  Results for Tammela Region ............................................................. 51 6.3.3.  Results for Hervanta Region .............................................................. 54 

7.  Coverage Dimensioning ........................................................................................ 57 7.1.  Adjusted Propagation Model........................................................................... 57 7.2.  Coverage Range Estimation ............................................................................ 60 7.3.  Reliability Analysis ......................................................................................... 61 

8.  Conclusions and discussion .................................................................................. 64 REFERENCES .............................................................................................................. 66 

VI

LIST OF ABBREVIATIONS 3G Third generation cellular network system AFD Average fade duration BER Bit error rate C/I Signal-to-interference ratio EIRP Effective isotropic radiated power GPS Global positioning system LCR Level crossing rate LOS Line-of-sight NLOS Non-line-of-sight RMS Root-mean-square WLAN Wireless Local Area Network

VII

LIST OF SYMBOLS h(t,τ) Time varying channel impulse response ai(t,τ) Amplitude of ith multipath component τi(t) Delay of ith multipath component θi(t,τ) Phase of ith multipath component P(τ) Power delay profile τrms Delay spread BC Coherence bandwidth PT Total power of all multipath components τm Mean delay of all multipath components fD Doppler shift fC Carrier frequency v Speed of a transceiver c Speed of light α Angle between a direction of motion and a direction of multipath wave TC Coherence time PL(d) Mean path loss PL(d0) Measured mean reference path loss d Distance between a transmitter and a receiver d0 Reference distance γ Path loss exponent Xσ Shadow fading random variable σL Location variability g(t) Complex based mobile-to-mobile signal An Signal amplitude of nth multipath component Фn Signal phase of nth multipath component fTx Transmitter Doppler shift fRx Receiver Doppler shift αn Arrival angle βn Departure angle Rv(∆t) Time auto-correlation J0() Zeroth order Bessel function σ2 Variance of complex fading signal vRx Speed of receiver vTx Speed of transmitter a Ratio of speeds of a transmitter and a receiver S(f) Doppler spectrum f frequency K[] Complete elliptic integral of first kind R Amplitude threshold level ρ Ratio between R and fading signal root-mean-square value EIRPdBm Effective isotropic radiated power PTx Transmission power GTx Total gain of transmission line LTx Total loss of transmission line RXS Receiver sensitivity level kTBdBm Thermal noise level

VIII

k Bolzmann’s constant T Temperature B Transmission bandwidth F Noise figure C/I Carrier-to-interference ratio MB Body loss margin MS Shadow fading margin MO Arbitrary planning margin PLµ,σ Path loss variable with mean µ and standard deviation σ PLMAX Maximum allowable path loss value u Probability variable r Cell radius RMAX Maximum cell radius ∆r Marginal thickness of ring Pp(r) Point location probability di Distance between a transmitter and a receiver corresponding ith sample PLi Path loss corresponding ith sample ã Distant independent constant part of path loss model bi Distant dependable constant part of path loss model C Path loss offset correction factor err Least squares error between a prediction and a measurement yi Received path loss corresponding ith measurement sample W Prediction matrix c Parameter vector for unknown model parameters Y Measurement sample matrix

LSc Vector solution for optimal model parameters

1

1. INTRODUCTION

The rapid growth of wireless communications during last decades has made it, by any measure, the fastest developing segment of telecommunications nowadays. During these years, the total number of different wireless technologies and services has also increased massively providing many new services and usage possibilities for communications compared with the traditional wired place-to-place communications. The wireless communication systems can be categorized in many ways based on coverage, capacity, portability, mobility and provided services. Therefore, five rather distinct categories can be pointed out, cordless phones, paging and messaging systems, wireless local area networks (WLAN), cellular networks and satellite communications. Nowadays each of these provides a countless number of everyday applications for billions of subscribers. The cordless phones provide low mobility, low power and two-way voice communication for short range and low user speed. The paging and messaging systems are mainly used for one-way messaging with a varying coverage area and limited capacity. Despite the fact that the commonness of cellular systems and mobile handset integration has reduced the popularity of the paging devices, these applications are still commonly used. WLANs provide high data rates over relatively short distances and with low mobility. Therefore, extending wired local area networks over air interface like cordless phones extended traditional phone lines. Surely, the last two groups, cellular networks and satellite communications, have undergone the fastest growth during the recent years. On one hand, the third generation (3G) cellular network systems provide high mobility, wide coverage, two-way voice and data communications. On the other hand, in the field of satellite communications, the availability of positioning applications has grown enormously. The tremendous demand and the massive number of different wireless communication technologies competing for subscribers have made available frequency spectrum a more and more scarce resource. Therefore, all new wireless system competing on emerging markets must undergo a careful planning process where configuration, coverage, capacity, mobility and cost-efficiency issues must be investigated and balanced to correspond to expected system requirements.

2

This Master of Science thesis gives a thoughtful introduction to one of the sections of the wireless system planning process by going through a coverage dimensioning process for a new wireless system operating at the mobile-to-mobile radio environment. The coverage dimensioning process results in an approximation of the coverage range for a system operating at 900 MHz frequency band. The structure of this thesis consists of a theoretical part introducing the mobile radio propagation environment and the coverage dimensioning process, and a practical part concentrating on the radio environment measurements, and analysis of the measurement results In Chapter 2, the essential background information of mobile radio channels is given emphasizing channel modeling aspects by means of a large scale and a small scale fading phenomenon. Chapter 3 characterizes the mobile-to-mobile radio environment and points out the main differences compared with the traditional base-to-mobile radio environment. Chapter 4 describes briefly the essential aspects of the wireless system planning process, emphasizing the coverage dimensioning part. In Chapter 5, the model tuning process is explained. Chapters 6 and 7 describe the measurement environment, show the essential coverage dimensioning parameters derived from the measurements and finally presents the results of the coverage dimensioning. In Chapters 8, the overall conclusions of the thesis are discussed.

3 3

2. MOBILE RADIO CHANNEL

When designing a new wireless communication system, it is essential to understand what is meant by the term mobile radio channel. The mobile radio channel is a medium between a transmitter and a receiver modifying transmitted signals in an unpredictable way [1]. This unpredictable behavior is caused by various phenomena affecting the radio wave propagation via the channel. Moreover, for the mobile radio channel, the relative positions of the transmitter and the receiver may change among any of the contributing objects affecting to the propagation and this causes the mobile radio channel behavior to be a time varying process. This chapter gives readers essential background knowledge about the mobile radio channel and introduces all essential terms which will be used later in this thesis.

2.1. Radio Channel Modeling

The propagation of radio waves through a wireless channel is a complex phenomenon and characterized by reflections from electrically smooth surfaces, refractions between two propagation mediums, diffractions from the edges of buildings, and scattering from electrically rough surfaces [2]. Quite often a precise mathematical description is either unknown or too complicated to model for a practical communication system design point of view. During the years, much effort has been put in statistical modeling and in the characterization of the different propagation phenomena resulting in many accurate and simple statistical fading models which usually depend on the propagation environment and the underlying communication scenario [3]. The channel modeling is divided into the investigations of large scale characteristics and small scale characteristics of the radio channel. The large scale characteristics describe the fading behavior of the radio channel over long distances. Moreover, if the model predicts the mean signal strengths as a function of distance between a transmitter and a receiver, then they are often called path loss models [1], [4]. These models are usually characterized by environment dependable parameters such as location variability and path loss exponent. In contrast to the large scale fading, the small scale fading describes the fading behavior of the radio channel over very short distances. Therefore, channel modeling is divided into the investigations of time dispersion, frequency dispersion and statistical fading distribution [1], [4]. Moreover, the small scale fading is characterized by the slow or fast fading behavior and the frequency-flat or frequency-selective behavior of the channel. Figure 1 shows the contributions affecting the mobile radio channel.

4

Figure 1: Contributions affecting to the mobile radio channel

2.2. Small Scale Fading

Small scale fading describes the rapid fluctuations of the signal amplitudes, phases, arbitrary frequency modulation and time dispersion of the received signal over a short period of time or short traveling distances [4]. The three most common reasons influencing the nature of small scale fading are the presence of multipath components, the motion of the receiver or the transmitter and lastly, how rapidly environment affects the random amplitudes, phases and delays of the multipath components [4]. A multipath propagation environment occurs because of scatterers, which are objects affecting the radio wave propagation by reflections, diffractions and scattering, causing the combined received signal to consist of multiple replicas of the transmitted signal. Figure 2 shows how the small scale fading may occur in practice. In the figure, the line-of-sight component is absorbed by the star shaped scatterer and the received signal is a contribution of three non-line-of-sight (NLOS) replicas of the transmitted signal. All three NLOS signal components undergo different propagation paths with different lengths and therefore all received signal replicas differ in phase.

Figure 2: Non-line-of-sight multipath environment

5

2.2.1. Channel Statistical Behavior

In mobile radio channels, a fading distribution describes the statistical time varying nature of the fading multipath signal which is caused by the constructive or destructive combination of the multipath components [3]. In practice, the distribution gives a probability for the signal to be below or above a certain threshold level [4]. This is an easier way to describe the channel behavior for the given radio environment. An accurate prediction of the effects of multipath components would require exact information about the electromagnetic properties and positions of the scatterers. This is often an impractical and complex approach for system designing. However, those do not give any information about the time varying properties of the radio channel or how rapidly signal levels vary between different threshold levels. Depending on the nature of the radio propagation environment, there are different kinds of distribution describing the statistical behavior of the fading envelope [3]. For narrowband communications, the Rayleigh distribution is commonly used for non-line-of-sight communication environments. In contrast to the NLOS channel, the Rician distribution is used to describe a line-of-sight (LOS) fading channel, where one of the received signal components undergoes a line-of-sight radio path which is not obstructed or affected by any obstacles. Section (3.1.1) gives a more precise description and illustrations for LOS and NLOS fading channels.

2.2.2. Time Dispersive Behavior of Radio Channel

In mobile communications, the radio waves arrive to the receiver via different propagation paths. As these paths may have different lengths, the received signal is a sum of slightly delayed replicas of the transmitted signal. This kind of mobile radio channel is often modeled as a time-variant linear filter which has an impulse response expressed as [4]

( ) ( ) ( ) ( )( ) ( )( )

1

0

, , exp ( , )N

i i i ii

h t a t j t t t tτ τ ωτ θ τ δ τ−

=

= + −∑ , (2.1)

where ai(t,τ) and τi(τ) represent the amplitudes and delays of the ith multipath component at time t. The phase term θi(t,τ) presents the phase shifts because of different propagation phenomena. The power delay profile P(τ), the RMS delay spread τrms and the coherence bandwidth BC are common parameters which are used to quantify the behavior of the time dispersive channel. The power delay profile specifies the mean relative powers of the delayed multipath components over time given by [1]

6

( )( ) 2

,

2

E h tP

ττ

⎡ ⎤⎣ ⎦= , (2.2)

Usually, the power delay profile is presented in a discrete form where each tap in the delay profile has a mean power level, a delay and a statistical behavior defined to be either Rayleigh or Rician distributed [1]. Figure 3 shows the power delay profile presentation for the arbitrary multipath channel, where the mean relative powers decay exponentially as the delay increases.

 

Figure 3: A power delay profile The power delay profile is used for defining the RMS delay spread τrms of the channel, which is the second central moment of the of the power delay profile given by [1]

( ) 2 2

1

1 n

rms i i miT

PP

τ τ τ τ=

= −∑ (2.3)

where PT is the total power of all taps and τm is the mean delay of all the multipath components. The RMS delay spread takes into account the mean powers as well as the mean delays of the multipaths. This makes RMS delay spread a commonly used and a good indicator characterizing the radio channel time dispersion [1]. The RMS delay spread can be used to understand the impact of the used signal transmission bandwidth to the signal distortion at the receiver due to the multipath components. A narrowband radio channel definition is used for a channel where RMS delay spread is relatively shorter than the symbol duration [1]. In contrast to the narrowband channels, if the RMS delay spread is longer than the symbol duration, then a wideband radio channel definition is used [1].

7

Furthermore, the RMS delay spread is used to define the coherence bandwidth of the multipath channel. The coherence bandwidth is a measure of the maximum frequency difference for two signals which would still strongly correlate in amplitude regardless of the frequency difference [4]. The frequency difference defines the bandwidth for certain correlation and this is known as coherence bandwidth. The exact relationship between the RMS delay spread and the coherence bandwidth depends on the exact multipath structure but they are roughly inversely proportional to one another. [4]

1C

rms

Bτ

∝ (2.4)

The coherence bandwidth is used to define whether the multipath channel is a frequency-flat or a frequency-selective channel. In a frequency-flat channel the signal bandwidth is narrower compared with the coherence bandwidth [4]. This means that the whole signal band fades equally. In the frequency-selective channel, the signal bandwidth is much wider than the coherence bandwidth and in this case, some parts of the signal band fade unequally [4]. The delay spread and the coherence bandwidth, as presented above, are used to describe the time dispersive nature of the channel. However, these parameters do not provide any information about the rate of change in the channel or the frequency dispersive nature of the channel.

2.2.3. Frequency Dispersive Behavior of Radio Channel

The Doppler shift, the Doppler spectrum and the coherence time are parameters which are used to describe the frequency dispersive nature of the mobile radio channel. These parameters can be used to estimate the time varying nature and the rate of the fading in the channel. This frequency dispersion occurs due to the random frequency modulation caused by the Doppler effect. The Doppler effect occurs because a moving mobile passes an incoming wave front at the different rate compared to the situation in case of the stationary mobile [4]. This results in a change of the carrier frequency of the arriving waves, as observed by the moving mobile. The Doppler shift describes how big change in carrier frequency is observed by the moving mobile. The Doppler shift fD is a function of speed of the mobile and angle between the direction of movement and direction of incoming wave-front and it is given by [1]

cosD Cvf fc

α= (2.5)

8

where fC is the carrier frequency, v is the mobile speed, c is the speed of light and α is the angle between the direction of the motion and the direction of the arrival waves in a horizontal plane. The maximum Doppler shift and frequency modulation occurs when α is 0. In a multipath environment, the waves arrive to the receiver from several directions and each of the incoming waves is frequency modulated and therefore associated with different Doppler shifts. This phenomenon results in the Doppler spectrum where the exact shape of the spectrum depends on the relative amplitudes and on the arriving angles of the incoming multipath components [1], [4]. The term coherence time Tc is used to describe the time varying nature of the channel in a small scale region and it is a statistical measure of the time duration over which the channel impulse response is basically time invariant [4]. A time autocorrelation function is derived from the Doppler spectrum and it describes the time duration over which the channel can be assumed to behave as a slow fading channel. In the literature, this time difference is stated to be inversely proportional to the Doppler spectrum and usually given as [1], [4]. 1

CD

Tf

∝ (2.6)

Thus, the coherence time defines the channel to be either a slow fading or a fast fading channel. For the fast fading channel, the symbol duration is long compared to the coherence time and channel impulse response varies rapidly within the symbol duration [4]. On the other hand, for the slow fading channel the coherence time is relatively shorter than the symbol duration [4].

As we have seen above, the shape and the extent of the Doppler spectrum has an impact on the second-order fading statistics of the mobile channel [1]. However, if the baseband signal bandwidth is much wider than the Doppler spectrum, then the effects of the Doppler spread are negligible at the receiver [4]. Anyhow, the problem concerning the time behavior of the channel remains. This information is often crucial for defining the duration of the symbol burst, where all the symbols would undergo approximately the same channel. Moreover, the channel time correlation information is used to define how often the channel needs to be estimated. This might sometimes be difficult as mobile systems need to support mobility over several speed scenarios. Figure 4 summaries the characteristics of the different types of small scale fading [4].

9

Small-Scale Fading(Based on multipath delay spread)

Frequency-flat Fading1. signal Bandwidth < coherence bandwidth2. Delay spread < Symbol period

Frequency-selective Fading1. signal Bandwidth > coherence bandwidth2. Delay spread > Symbol period

Small-Scale Fading(Based on Doppler spread)

Fast Fading channel1. High Doppler spread2. Coherence Time < Symbol period3. Channel variations faster than baseband signal variations

Slowly Fading channel1. Low Doppler spread2. Coherence Time > Symbol period3. Channel variations slower than baseband signal variations

Figure 4: Different types of small scale fading

2.2.4. Second Order Statistics

Exact knowledge of the time autocorrelation function or the Doppler spectrum is seldom available but second order statistics of the radio channel can be used in practice to model the channel time variant behavior while designing error control coding or diversity schemes for mobile communications [4]. The second order statistics joints together the Doppler spectrum and the statistical behavior of the channel and this study usually incorporates the investigations of the level crossing rate (LCR) and average fade duration (AFD). The level crossing rate defines an expected rate at which the fading envelope, normalized to the local root-mean-square value, crosses a specific level in a positive-going direction [4]. Usually the level crossing rate is given as a number of level crossings per second. The average fade duration defines the average period of the time for which the received signal is below a specific threshold level [4]. In practice, those two variables can be observed easily from the signal level measurements.

2.3. Large Scale Propagation

In contrast to small scale fading, large scale propagation describes the radio wave propagation over large distances, and investigations are usually divided into the investigations of large scale path loss models and shadow fading behavior. The path loss models predict the mean received signal strength as a function of distance and how this

10

attenuation behaves in different propagation environments. On the other hand, the shadow fading is used for explaining why and how this received signal strength value varies around the mean predicted value for spatially different locations within the same propagation distance.

2.3.1. Large Scale Propagation Modeling

The large scale propagation models are usually divided into three categories, which are empirical propagation models, semi-deterministic and deterministic propagation models [5]. Moreover, the large scale path loss models are usually divided into different categories based on different environments, and one can find many different models for satellite, macro, micro and indoor propagation environments. Empirical models are usually derived from massive field measurements using curve fitting methods giving simple and easy to use models. These models are accurate enough for the system design point of view over long propagation distances, for example for macro cellular concepts [5]. These are the easiest and the fastest models to take into use in practice. Semi-deterministic models are models which take into account all or at least some of the different propagation phenomena in an analytical form. These models are later often readjusted with correction factors based on actual measurements [5]. One example of a semi-deterministic model could be a problem to model signal propagation over a mountain. This could incorporate a simple model for predicting diffractions and a correction coefficient for the diffraction variable which is adjusted according to the measurements. Deterministic models are the most accurate ones incorporating Maxwell’s equations and all propagation mechanism to predict the signal strength in certain locations [5]. Besides the signal strength, these models can provide useful information about the multipath delay profile. However, these models require accurate information about the propagation environment as well as the electromagnetic properties of the obstacles affecting the propagation of radio waves. This makes the deterministic model too complex to be used in practice. The main problem with all the above-mentioned models is that they cannot be used effectively in all environments and require environment specific fine-adjusting. However, the empirical models provide a simple approach for the coverage prediction from system design point of view. Therefore, a power law empirical propagation model is used in this thesis to predict the coverage regions for coverage dimensioning purposes.

11

2.3.2. Power Law Path Loss Model

The power law path loss model is one of the simplest path loss models to be used. It consists of two propagation environment related parameters and two reference parameters which are used to adjust the model to correspond to the propagation environment. The path loss model is used to predict the mean path loss values and a formula for it in decibels is given by [4]

0( ) ( ) 10 log0

,dPL d PL d X

d σγ= + +⎛ ⎞⎜ ⎟⎝ ⎠

(2.7)

where PL(d0) is the reference path loss at the reference distance of d0 in decibels. The variable γ is a propagation exponent and Xσ is a random variable describing the shadow fading variation over the mean path loss value. The reference path loss value is approximated either using the free space path loss formula or through field measurements at distance d0 [4]. The propagation environment related parameters γ and Xσ are usually defined assuming a pre-defined system configuration which means that the center frequency, the propagation environment and the antenna heights are given. The propagation exponent γ is typically used to classify propagation environments and it describes how quickly the signal level attenuates as a function of distance. The typical propagation exponents for macro cellular base-to-mobile environments are presented in Table 1.

Table 1: Propagation exponents for different environments [6]

Environment Propagation exponent γ Rural 2.5

Suburban 3 Urban 4

Dense urban > 4.5 Table 1 shows that in rural terrain the propagation environment is easier and signals can propagate further compared to the densely built urban areas, where a higher propagation exponent indicates stronger signal attenuation as a function of the distance. Figure 5 illustrates the impact of the propagation exponent on the signal attenuation as a function of distance in practice.

12

Figure 5: Radio propagation in different environments

When the propagation exponent increases, the propagation environment becomes more and more challenging and the signal attenuates faster. This reduces the maximum achievable communication distance between a transmitter and a receiver.

2.3.3. Shadow Fading

Empirical large scale path loss models are often incorporated with a shadow fading parameter which describes the path loss variations over the mean predicted path loss value. Sometimes, the term slow fading is used as a synonym for the shadow fading in some literature sources, but during this thesis, these two terms do have a different meaning, as slow fading is also used for describing small scale characteristics of the radio channels [4]. The shadow fading variation occurs due to the fact that two spatially different locations having the same transmitter-receiver separation may undergo totally different kinds of radio paths. This causes the measured signals to differ greatly in their mean predicted signal levels regardless of the equally long separation. This is illustrated in Figure 6, where receivers RX1 and RX2 are located equally far from the transmitter TX. However, in both cases, the received signal consists of multiple multipath components each having totally different kind of reflection, diffraction and scattering properties. This causes the received signal to vary around the mean expected signal level.

13

TX

RX1

RX2

Distance r

Figure 6: An illustration of the shadow fading

Field measurements have verified this variation to be environment dependable and distributed log-normally around the mean predicted signal value [1], [3]. The term location variability σL is used to describe the standard deviation of the shadow fading and it varies according to the frequency, antenna heights and the environment [1]. The location variability is one of the parameters used for coverage dimensioning as it is used to predict the reliability of the coverage estimate. In macro cellular environments, where transmitter antennas are located above the average building height, the location variability is approximately 7 dB in urban environment and 8 dB in suburban environment when operating at 900 MHz frequency band [1].

14 14

3. MOBILE-TO-MOBILE RADIO ENVIRONMENT

The growth and the popularity of cellular networks have resulted in many publications concerning the traditional base-to-mobile radio environment. However, do the same radio characteristics hold for a mobile-to-mobile radio environment too? The base-to-mobile radio environment is often defined to consist of a stationary transmitter which is located above the rooftops and a mobile receiver which is placed a few meters above the ground. In a built-up environment, the propagation for this kind of configuration occurs mainly above the rooftops and is then diffracted and reflected to the ground level just before the receiver. In contrast to the base-to-mobile radio environment, the mobile-to-mobile radio environment is defined to consist of two non-stationary transceivers and both of them are located close to the ground level. The environment in this case is much more dynamic as both of the mobiles can be in motion. For a built-up environment, the signal propagation is characterized by multiple diffractions, reflections and a more challenging scattering environment. This chapter gives a brief summary of previous studies related to the small scale and large scale characteristics of the mobile-to-mobile channel and compares these two different propagation environments.

3.1. Small Scale Characteristics

One of the first studies concerning the statistical analysis of the mobile-to-mobile radio environment was done by Akki et al. [7], [8] and [9]. Linnartz et al. [10] was one of the many studying the mobile-to-mobile radio environment in case of short range vehicular line-of-sight communications. They carried out radio frequency (RF) measurements in 900 MHz band studying delay spread, probability distributions and Rician fading parameters and path loss rates. Moreover, Kovács studied the characteristics of the mobile-to-mobile communications in a forest environment and a short distance built-up environment [11]. Several methods for simulating narrowband mobile-to-mobile fading channels exist already in literature. A discrete line spectrum model for a mobile-to-mobile environment was introduced by Wang and Cox approximating the desired continuous Doppler spectrum [12]. Patel et al. introduced a double ring model for a mobile-to-mobile channel based on a modified sum-of-sinusoids method [13], [14]. A deterministic sum-of-sinusoid model has fixed phases, amplitudes and Doppler frequencies over all simulation trials. A statistical sum-of-sinusoid model has

15

randomness over the same parameters and therefore simulation results vary over simulation trials. On the other hand, the results converge to the theory better when the results are averaged over large number of trials. Later on, Zajic et al. [15] introduced a new modified statistical sum-of-sinusoid model which converges a bit faster compared to the previous statistical model. For Rician channel, a modified sum-of-sinusoid model was investigated in [16] covering mainly the vehicle-to-vehicle communication situations.

3.1.1. Statistical Model for Mobile-to-Mobile Communications

A statistical model for received signal consists of many uncorrelated multipaths due to the multiple reflections and diffractions from the surrounding scattering points. Each multipath component undergoes a random frequency modulation or Doppler shift because of the motion of a transmitter and a receiver. Moreover, arrival angles and departure angles for the multipath components are assumed to be uniformly distributed over the horizontal plane following Clarke’s 2D scattering model assumptions [4]. The statistical model is simplified to cover a narrowband channel only when there are no significant differences between the delays of the multipath components and all multipaths are approximately equally long. The narrowband presentation for the received complex baseband signal is derived from the results given by [7], [11] and [13],

( ) ( )( )( )1

( ) exp 2 cos 2 cosN

n Tx n Rx n nn

g t A j f t f tπ α π β φ=

= + +∑ , (3.1)

where An is an arbitrary signal amplitude and Φn is a uniformly distributed signal phase over [-π, π] for nth multipath component due to the propagation phenomena. The maximum Doppler shifts due to the movements of the transmitter and the receiver are modeled as fTx and fRx where αn and βn are the departure and the arrival angles for the nth multipath component, respectively. A typical propagation path from the transmitter to the receiver is illustrated in Figure 7, where one multipath component is reflected from a scatterer and frequency modulated due to the motion of the transmitter and the receiver. Moreover, the occurring Doppler shift depends on the differences between the direction of the motion and the direction of the propagated multipath component.

16

Figure 7: A typical propagation path in mobile-to-mobile environment [13]

When a large number of uncorrelated multipaths are combined in the receiver, the channel statistical behavior corresponds to a complex Gaussian random process based on the central limit theorem [1]. This leads to the Rayleigh distributed amplitude of the received signal due to the fact that the magnitude of a complex Gaussian random variable is a Rayleigh distributed random variable [1]. Previous investigations also suggested different scattering scenarios for the mobile-to-mobile environment where Rician and double Rayleigh distributions were found [11]. Figure 8 illustrates the scattering environment which is a favorable one for the Rician distribution. The Rician distribution arises when there is a dominant stationary and non-fading signal component present, such as a line-of-sight propagation path. In practice, this may occur when operating at relatively small distances and scarce scattering environment such as vehicle-to-vehicle communications on highways [10], [16]. Rician distribution was also observed to be present in forest and suburban environments on measurement carried by Kovács, but in these cases the measurement distances were less than 500 meters [11].

17

Figure 8: Rician fading channel in mobile-to-mobile environment

A Rayleigh distribution arises when there is only one scattering region present such as in traditional base-to-mobile communications. On the other hand, if the scattering regions around the transmitter and the receiver are rather isotropic and the propagation environment is assumed to be uniform to all directions, then the Rayleigh distribution is a good approximation for the fading distribution. This situation is illustrated in Figure 9. The Rayleigh distribution was observed especially in the measurement cases when the distance was short between the mobiles [11].

Figure 9: A Rayleigh distributed channel with uncorrelated propagation paths

Perhaps a more realistic propagation model for the mobile-to-mobile communications is a cascade or double Rayleigh distributed fading model [11]. If both of the mobiles are moving through local scattering environments in such a way that the distance between the mobiles and the local scattering environments is relatively long compared to the sizes of the scattering regions. Therefore, each of the independent scattering obstacles around the receiver receives waves from all of the scattering obstacles around the transmitter. This results in partly correlating multipaths leading to a cascade Rayleigh model [11]. This is illustrated in Figure 10 where the signals received from the scatterers are correlating partly.

18

Figure 10: A cascade Rayleigh channel with correlated propagation paths

A general mobile-to-mobile propagation model was also proposed in [11]. In this case the transfer function of the mobile-to-mobile channel was the sum of a possible Rician component, the Rayleigh component due to the uncorrelated propagation paths, and the double Rayleigh component because of partly correlated propagation paths. This seems to be a rather accurate model by means of the fading distribution based on measurements carried in [11], but it is too complex for practical coverage planning purposes as well as for an analytical treatment.

3.1.2. Doppler Spectrum for Mobile-to-Mobile Communications

Assuming omni-directional transmit and receive antennas and Clarke’s 2D-isotropic scattering environment, where departure and arrival angles αn and βn are uniformly distributed over [-π, π] then under these conditions, the time auto-correlation for a mobile-to-mobile channel is given by [7] ( ) ( ) ( )2

0 02 2 2v Tx RxR t J f t J f tσ π πΔ ≈ Δ Δ (3.2)

where J0(.) is a zeroth order Bessel function and σ2 is the variance of the complex fading signal. Parameters fTx and fRx are the maximum Doppler shifts for the transmitting mobile and the receiving mobile, respectively. Moreover, if vRx is presented in terms of vTx in such a way that vRx is equal to avTx, where vTx and vRx are speeds of a transmitter and a receiver respectively, then the time auto-correlation can be written as [7]. ( ) ( ) ( )2

0 02 2 2v Tx TxR t J f t J af tσ π πΔ ≈ Δ Δ (3.3)

If the variable a is 0, then either the receiver or the transmitter is stationary and the time auto-correlation function reduces to the traditional base-to-mobile case. On the other hand, if the variable a is 1, then both of the mobiles are moving with the same speed. Time varying statistical properties can also be observed from the Doppler spectrum point of view, as the Fourier transform of the time auto-correlation represents the power

19

spectrum of the channel [1]. As the time auto-correlation (3.3) for the mobile-to-mobile channel is a product of two different time auto-correlations, the resulting spectrum is a convolution of two classical Doppler spectrums and therefore the results is a double Doppler spectrum. Furthermore, an analytical expression for the Doppler spectrum is given by [7]

( ) ( )

22

2

2 1 112 TxTx

a fS f Ka ff a a

σπ

⎡ ⎤⎛ ⎞+⎢ ⎥= − ⎜ ⎟⎜ ⎟⎢ ⎥+⎝ ⎠⎢ ⎥⎣ ⎦

, (3.4)

where K[.] is the complete elliptic integral of the first kind and variable a is the ratio of the mobile speeds. Figure 11 shows the Doppler spectrum of the mobile-to-mobile environment for different mobile speed ratios. When the variable a is 0, either the transmitter or the receiver is stationary and the Doppler spectrum curve in the figure resembles the classical U-shaped spectrum. When the transmitter moves twice as fast as the receiver, the parameter a equals 0.5 and the resulting Doppler spectrum resembles the two-tap spectrum. Lastly, if both of the mobiles move with the same speed, the parameter a equals 1 and the resulting Doppler spectrum resembles the one-tap spectrum.

-1 -0.5 0 0.5 1

Dop

pler

spec

trum

Normalized frequency [Hz/Fd]

a=1.0a=0.5a=0.0

Figure 11: Doppler spectrum for mobile-to-mobile channel

20

The Doppler spectrum properties for a three-dimensional environment and different antenna patterns were investigated in [17]. The results were quite similar to the Clarke’s 2D environment mainly due to the low arrival angle distribution in vertical plane [18] and, therefore, the 2D model seems to be sufficient for mobile-to-mobile channel modeling purposes.

3.1.3. Second Order Statistics for Mobile-to-Mobile Communications

As seen in Figure 11, the dynamic operation environment changes the shape and wideness of the Doppler spectrum in case of mobile-to-mobile communications. This does not really tell how this affects to the overall behavior of the channel. This can be understood better by analyzing the level crossing rates and fade durations for the mobile-to-mobile channel. Studies showed that the level crossing rate increased and the durations of the fades decreased in the mobile-to-mobile environment compared to the base-to-mobile environment [7]. The envelope level crossing rate describes the rate at which the signal envelope crosses a specific signal threshold level R in the positive direction. For the mobile-to-mobile environment it is given by [7] 222 1R TxN f a e ρπ ρ −= + , (3.5)

where ρ is the ratio between the threshold level R and the received signal root-mean-square (rms) value and it is given by [7]

22Rρσ

= (3.6)

where σ2 is the variance of the received signal. As seen from the formula (3.5), the level crossing rate increases as the speed of the mobiles increases compared to the traditional base-to-mobile case. This can be observed in Figure 12 which compares the level crossing rates between the base-to-mobile and the mobile-to-mobile environments.

21

Figure 12: Level crossing rate statistics

The curve presenting the level crossing rate for mobile-to-mobile environment is always above the curve presenting the similar situation in case of base-to-mobile environment. This means that there is more positive level crossings in case of mobile-to-mobile environment. On the other hand, the average fade duration describes the duration of time during the signal is below the threshold value R and for mobile-to-mobile channel it is given by [7] ( )2

2

1 1 12 (1 )

r

Tx

ef a

ρτρπ

= −+

(3.7)

As seen from formula (3.7) the duration of the fades for mobile-to-mobile channel is reduced compared to the traditional base-to-mobile channel when the variable a increases. This can be observed in Figure 13 which compares the average fade durations between the base-to-mobile and mobile-to-mobile environments.

22

Figure 13: Average fades duration statistics

The curve presenting the average fade duration for mobile-to-mobile environment is always below the curve presenting the same situation in case of the base-to-mobile environment. This means that the time duration that the channel spends in deep fades is always shorter in case of the mobile-to-mobile environment compared to the base-to-mobile environment with an equal Doppler shift.

3.2. Large Scale Characteristics

Surely, a lot of studies about large scale characteristics of a mobile-to-mobile environment might have been done but hardly any information about these studies is publicly available in literature. Most of the time, all published material focuses on understanding the small scale issues of the mobile-to-mobile channel. Some of the large scale studies focus on understanding how the propagation environment changes when the height of the transmitting antenna is reduced from macro cellular heights to micro cellular heights [19], [20], [21], [22] and [23]. But in all cases, a typical micro cellular environment was assumed and the minimum antenna height was around four meters, which is not exactly the best assumption for the mobile-to-mobile communication scenario. On the other hand, some of the studies investigated really near-ground propagation and considered only really short distances between a transmitter and a receiver. However, this does not give a reliable solution for a coverage dimensioning purposes for long distance mobile-to-mobile systems either [24], [25]. Roughly speaking, the result of all

23

the above-mentioned studies is that the propagation exponent increases when the height of the transmitter antenna is decreased. This indicates more challenging propagation environment and reduced coverage areas. Also some measurement surveys for mobile-to-mobile environment were found but both the environment and the used carrier frequency were not suitable [11] or a proper analysis of the measurement results for coverage dimensioning purposes was missing [26].

24

4. COVERAGE DIMENSIONING PROCESS

The target of coverage dimensioning is to estimate the maximum distance between a transmitter and a receiver while fulfilling the given system performance requirements under specific propagation conditions [6]. In general, this yields to the investigations of the maximum achievable distance, where the minimum possible signal-to-noise ratio is still obtained if the maximum available transmission power is used. The main parameters affecting the maximum distance are the carrier frequency, the heights of the receiver and the transmitter, the transmission power, and the propagation environment [6]. Furthermore, the communication range between a transmitter and a receiver can be obtained using the maximum allowable path loss taken from the link budget assessment and some propagation path loss model which is suitable for the given communication scenario. This usually requires some prior knowledge about the propagation environment and the initial system configuration. On one hand, knowledge of the propagation environment helps in choosing a proper path loss model to predict the signal propagation. On the other hand, knowledge of the system configuration is required to define the maximum allowable path loss. Even if coverage dimensioning is mainly done based on the large scale characteristics, knowledge of the small scale characteristics has a big part in coverage dimensioning too. The understanding of small scale characteristics is used while deriving the system performance requirements which are used on link budget. This chapter introduces the purpose of the coverage dimensioning process and describes the things that must be done before and during this process. Moreover, a general link and necessary planning margins are defined.

4.1. Dimensioning Process Phases

The entire planning process for a totally new wireless communication system is a long and demanding process which may vary depending on the type of the designed system and the planning organization. Therefore, the thorough explanation of the whole planning process is out of the scope of this thesis. Dimensioning is one of the first phases of the radio system planning process and its sole purpose is to initially draft the radio network configuration and the long term deployment strategy [6]. During dimensioning, the essential radio parameters and technologies are decided in order to be able to deploy a new wireless system. Figure 14 illustrates the process which is partly

25

based on the writer’s own experiences and ideas, and partly adopted from the general radio network planning process [6].

Coverage Planning Process

Coverage dimensioning1. Investigate the propagation environment ( e.g. Macro cell, Micro cell or mobile-to-mobile)

2. Choose a proper path loss model for the environment > Location variability > Path loss Exponent

3. Solve the coverage range

- Link Budget & Maximum allowable Path Loss

Configuration Planning

- Transmission Powers- Antennas & Other Link Budget elements- Diversity schemes

- C/I Requirement to Link Budget

Link Performance planning

- Small scale radio channel investigations- Delay spread & Doppler spread- AFD & LCR

System Description

- Purpose of the system & long-term strategy- Coverage & Capacity Requirements- Spectrum & Carrier Frequency

Figure 14: Coverage dimensioning process

Before coverage dimensioning can be done, a few things must be defined in order to be able to predict reliably the coverage range. The target of the system description phase is to define the purpose of the system and thus rationalize why this system is required and planned. The purpose of the system defines at least the usage environment; coverage, capacity and service requirements; mobility requirements; and long term strategy for the system. After this phase, designers might have ideas about alternatives for the possible technologies to be used and ideal carrier frequency among the available frequency spectrum. After defining the purpose of the system, the chosen technology alternatives can be studied in detail. The target of link performance planning is to investigate the link level performance for the chosen technologies over the propagation medium. This phase consists of the investigation of the small scale characteristics of the radio channel and it

26

is affected by the chosen technology, the propagation environment, the frequency, and the mobility requirements. After this phase, a draft of the required system performance indicators should be defined. Moreover, at least the carrier-to-interference (C/I) requirements for different services should be defined in order to be able to continue with the configuration planning. The configuration planning phase consists of defining the link budgets for different services or usage profiles. During this phase, an initial configuration incorporated with the transmission powers, receiver and transmitter architectures, and antenna types are defined. The link budgets are used to specify the maximum allowable path losses which are required before the coverage dimensioning can be done. The coverage dimensioning phase consists of the investigations of the propagation environment. This is required for choosing a proper path loss model with the proper parameters. After the link budgets and maximum allowable path losses are solved, the maximum operation range for different usage profiles can be predicted by using the chosen path loss model. After the coverage dimensioning phase, a capacity dimensioning could also be done. If the coverage and capacity dimensioning seems to fulfill the long term system strategy requirements then the actual wireless system planning can be continued in more detail.

4.2. Maximum Allowable Path Loss

To be able to solve the maximum operation distance, a maximum allowable path loss and a path loss model must be specified. The maximum allowable path loss is defined as the maximum difference between the transmitted power and the received power and it is often presented in decibel scale. The maximum allowable path loss describes the maximum allowable signal attenuation over the propagation path. The path loss, or equally, the received power level can be predicted based on the path loss models if the radiated transmission power is known. Those models are usually given as a function of distance, carrier frequency, intercept parameter and a path loss exponent characterizing the propagation environment. The maximum allowable path loss is derived from the link budget and it strongly depends on the transmission power and the current system configuration, as the receiver sensitivity level is affected by the receiver and the transmitter components such as antenna gains, amplifier gains, cables and connector losses, and diversity techniques among many other things.

27

4.3. Link Budget

A generic link budget for a narrowband mobile-to-mobile system is presented in Table 2. In Table 3, additional planning margins are given which are used to improve the accuracy of the link budget. Uplink and downlink parameters for the link budget are assumed to be identical as for the mobile-to-mobile communications, as the mobile receiver and the mobile transmitter have similar configurations.

Table 2: A generic link budget Transmitter end Transmission power 30 dBm Gains 0 dBm Losses 0 dBm EIRP 30 dBm

Receiver end Bolzman constant (k) 1.38E-23 J/K Temperature (T) 290 K Channel Bandwidth (B) 6.25 kHz Thermal noise level -136 dBm Mobile Noise figure (F) 7 dB System noise floor -129 dBm C/I requirements 17 dB Receiver sensitivity -112 dBm

Table 3: Link budget margins

Margins Pathloss without margins 142 dB Body loss margin 5 dB Shadow fading margin 7 dB Building penetration loss - dB Maximum Path loss 130 dB

28

The maximum transmission power for a mobile transmitter is assumed to be 30 dBm and there are neither gains nor losses due to the antennas or connectors present in this configuration. Effective isotropic radiated power (EIRP) is calculated based on formula (4.1) ,dBm TX TX TXEIRP P G L= + − (4.1)

where PTX is the transmitter power. GTX is the total signal gain due to the amplifiers, antenna gain and other transmission enhancements. LTX is the total signal loss due to connectors and other attenuators. For a mobile device, the gains and losses are often neglected. The sensitivity of a mobile receiver, which gives the minimum reception level at the mobile end, is affected by temperature, transmission channel bandwidth, mobile noise figure and system signal-to-interference requirements (C/I). The minimum sensitivity level is calculated based on the formula (4.2), / ,SENSITIVIY dBm dB dBRX kTB F C I= + + (4.2)

where kTB defines the thermal noise level and it is converted to the dBm scale. The variable k is the Boltzmann’s constant. The variable T is the temperature in Kelvin units and B is the transmission channel bandwidth. The variable F is the mobile noise figure and it is added to the thermal noise level while defining the total system noise level. Finally, the variable C/IdB is the signal-to-interference ratio providing the required bit error rate (BER) for the reception of the selected service. The C/I requirement is affected by the fading conditions and it can be improved by using enhanced reception and transmission techniques such as different diversity schemes or frequency hopping. The improvements due to the advanced receiver techniques can be included in or excluded from to the C/I requirement. Therefore, system designers should always be aware of how the C/I requirement is defined and what is already included into it. For example, if C/I requirements were derived from an additive white Gaussian noise channel, some extra margins would be required because of the fading phenomenon in mobile radio channels. The path loss without any additional margins is calculated by subtracting the receiver sensitivity level from the effective isotropic radiated power level of the transmitter and it is given by the formula

_ ,dB dB SENSITIVIY dBPath loss EIRP RX= − (4.3)

29

For the link budget presented in Table 2, EIRP is defined to correspond to 30 dBm and the receiver sensitivity is defined to be -112 dBm. This leads to the path loss value of 142 dBm based on the above-described formulae. However, this does not take into account any of the system planning margins such as shadow fading margin (Section 4.4.1) or body and building penetration loss margins (Section 4.4.2).

4.4. Planning Margins

Because of the shadow fading phenomenon and other attenuating elements, certain margins must be included into the path loss estimation as this leads to a more reliable coverage probability for the planned system. The most commonly used planning margins are margins due to a body loss, a building penetration loss and a shadow fading margin. The maximum allowable path loss with the additional margins is calculated based on the formula

_ ,dB dB SENSITIVIY dB Body Shadowing otherPath loss EIRP RX M M M= − − − − (4.4)

As seen in formula (4.4), the extra margins reduce the maximum allowable path loss and thus shrink the maximum achievable coverage area. On the other hand, this ensures a better service probability and a more reliable coverage probability is achieved.

4.4.1. Shadow Fading Margin

As mentioned earlier in Chapter 2, empirical large scale path loss models are often incorporated with a shadow fading parameter, which describes the path loss variations over the mean predicted path loss value. For this kind of models, the mean path loss value only gives 50 % likelihood for the signal level to be below or above the mean predicted signal level. The shadow fading parameter with the location variability incorporated to those models defines the statistical properties for the signal variation with the mean value [1]. Without taking into the account the shadow fading margin, the communication range for the system would be defined based on the mean path loss value, where the maximum allowable path loss value from the link budget equals the mean path loss value of the empirical model. This would indicate that, at the edge of the communication range, the probability for a successful connection would be only 50 % for the required block error rate requirement. However, this is seldom an acceptable quality and performance level for commercial communication systems and the additional shadow fading margin has to be included into the link budget according to the required reliability requirements for the coverage [6].

30

The shadow fading margin depends on the reliability requirement, the statistical properties of the shadow fading and the environment itself. The margin is usually defined based on the location variability and the path loss model. Moreover, a point location probability pp at the given distance is the probability where path loss at the given distance is less than or equal to the maximum allowable path loss PLMAX. An equation for the point location probability is shown in formula (4.5) and is given by [1] 2

2,1 ( )Pr exp

22

MAXPL

MAXu

uPL PL duμ σμσσ π =−∞

⎛ ⎞−⎡ ⎤≤ = −⎜ ⎟⎣ ⎦⎝ ⎠

∫ (4.5)

where PLμ,σ is the path loss model with a mean path loss part µ and the location variability σ. The latter one follows the Gaussian normal distribution if the path loss model is given in decibel scale. Although the locations at the edge of the communication range may only have a low probability for a successful communication, most of the mobile will be closer to the transmitter than this, and they will therefore experience a considerably better coverage [1]. Thus, it is better to design the systems based on the area location probability instead of the point location probability. The area location probability is calculated as the weighted average of the point location probability over the whole communication area. Figure 15 shows an omni-directional cell of radius RMAX with a representative ring of radius r, marginal width ∆r and within this ring the point location probability pp is given based on the formula (4.5).

Figure 15: Calculating area location probability by summing point location probabilities

31

The area covered by the narrow ring is 2πr∆r. The area location probability for the whole area is then the sum of areas associated with all such rings with the radius r between 0 and RMAX, multiplied by the corresponding point location probability and divided by the area of the whole cell [1]. This may be solved numerically for any desired path loss model by following the formula (4.6) given in [1].

area p20

1 ( ) 2MAXR

MAX r

p p r rdrR

ππ =

= ×∫ (4.6)

Table 4 shows the corresponding shadow fading margins for the area location probabilities of 75%, 90% and 95% for different values for the location variability σL and the propagation exponent γ.

Table 4: Shadow fading margins for different propagation conditions Area Location

Probability σL = 7dB σL = 8dB

γ =3 γ=4 γ=5 γ=3 γ=4 γ=5 75 % -0.0 dB -1.1 dB -2.1 dB 0.4 dB -0.7 dB -1.8 dB 90 % 4.8 dB 3.9 dB 3.2 dB 5.9 dB 5.0 dB 4.2 dB 95 % 7.6 dB 6.9 dB 6.2 dB 9.1 dB 8.3 dB 7.6 dB

Table 4 shows that the required shadow fading margin increases as the location variability increases. This can be easily explained by the fact that the standard deviation of the fading is higher. On the other hand, the higher propagation exponent γ allows the usage of the smaller shadow fading margin. This can be explained by the fact that the signal attenuates faster and the coverage area is thus smaller. In this case, the portion of the coverage area with a low point location probability is smaller compared to the overall coverage area and thus higher overall area coverage probability can be achieved with a smaller margin. Moreover, negative margin values indicate that even without any shadow fading margins the overall area coverage probability would be higher than 75%. On the other hand, the 95% area location probability corresponds approximately to 83% point location at the cell edge if 6.9 dB shadow fading margin is included to the link budget for the environment where the location variability is 7 dB and the path loss exponent is four. This might be already enough for the practical communication system and the value of 7 dB is chosen for the link budget. However, the statistics of the location variability and the exact behavior of the propagation exponent are unknown for the mobile-to-mobile environment.

32

4.4.2. Other Planning Margins

Besides the shadow fading margin, many other planning margins can be incorporated to the link budget to make it more realistic for a specific propagation environment and communication scenario. Other planning margins used for the link budget presented in Table 3 are the body loss and the building penetration loss. The attenuation of the transmitted or received power due to the presence of the users is known as the body loss. The body loss depends on the location of the antenna as well as the type of the antenna. Measurements have indicated values varying between 3 dB and 8 dB [27], [28]. The attenuation due to the signal propagation through the walls of the buildings is known as the building penetration loss. This must be included to the link budget margins if the outdoor-to-indoor propagation environment is under the investigations. If outdoor-to-outdoor environment is investigated the building penetration losses can be omitted from the path loss calculations. Exact value for the building penetration loss depends on the types of the construction materials and thickness of the walls as well as the carrier frequency, and the typical values vary between 10-30 decibels [29], [30]. For the link budget presented in Table 2, the system is dimensioned for outdoor-to-outdoor communications and thus only the body loss and shadow fading margin are incorporated to the total path loss calculation. In this case, the body loss is defined to be 5 dB and the shadow fading margin is defined to be 7 dB. This leads eventually to the path loss value of 130 dBm.

33

5. PROPAGATION MODEL TUNING

Propagation model tuning is a process where the theoretical propagation model is adjusted to fit better to the current propagation conditions with the help of measurements [6]. This results in a propagation model where the predicted signal level corresponds to the measured signal level as closely as possible and, thus, gives a more accurate prediction. This chapter describes the actual research problem of this thesis and shows how that problem can be solved with model tuning. Moreover, the model tuning process is described briefly. Finally, a mathematical solution for solving the required large scale model parameters for a power law path loss model is presented at the end of this chapter.

5.1. Research Problem

The target of this thesis is to give an estimation of the coverage range for a mobile-to-mobile system. However, it was shown in Chapter 3 that not much detailed information of large scale statistics or reliable path loss models are available. It was also explained in Chapter 4 that a reliable coverage dimensioning requires knowledge of the propagation environment in order to be able to choose the best possible propagation model to predict the path loss. The above-mentioned problem could be solved by finding the large scale parameters, such as the value for the path loss exponent and the location variability, which could be used later to define the reliable propagation models for a mobile-to-mobile wireless system operating at 900 MHz frequency band. To find those parameters, a measurement campaign or a reliable propagation simulator is required. In this thesis, the measurement based model tuning was chosen. This method is a more reliable one compared to the simulator based approach, if measurement configuration is done carefully and enough measurement samples are collected in a right manner. The simulator based method would be faster and less time consuming but there were no reliable propagation simulators available.

34

5.2. Model Tuning

The target of the measurement based model tuning is to adjust the theoretical model to match the measurements. In order to be able to do this, first a suitable model must be chosen. For macro cells, an empirical Okumura-Hata model is often used, but due to the limitations of the model [5], it is not wise to use it for the mobile-to-mobile environment. If the measurements and the model tuning are done carefully, then a model with rather accurate predictions can be obtained. However, models of this kind are always somehow related to the measurement environment and the configuration. Totally environment independent predictions would require a more sophisticated model with general and trustworthy parameters. This would also require massive measurements campaigns.

5.2.1. Model Tuning Process

Figure 16 illustrates the model tuning process which was used to derive the results of this thesis.

Figure 16: Model tuning process

35

At first, a basic propagation model is selected. The type of the chosen model depends on the number of tuned model parameters as well as the underlying propagation environment. Simple models are less sophisticated and have only a few parameters which can be tuned. However, tuning takes less time and the resulting model is likely to be easier to use. Sophisticated models provide more parameters such as diffraction corrections, clutter corrections and effective height corrections to model [31]. These models would be performing better by means of prediction accuracy, but those also require more information about the propagation environment. Due to the fact that the investigated propagation environment was unknown and there were no proper path loss models available, a simple power law path loss model was chosen (Section 2.3.2) for the model tuning purposes. For the given model (2.7), the path loss exponent as well as the offset correction can be solved using simple mathematics (Section 5.2.2). The second phase of the tuning process consists of planning and making the actual measurements. Careful planning ensures reliable results and minimizes the need for additional measurements. The measurement planning phase includes defining a measurement configuration and drive test routes. During the configuration planning, all antenna gains and cable losses as well as antenna installations must be taken into account. This reduces the possibility of the systematic errors which causes biased measurement results. The measurement routes should be planned in such a manner that enough samples can be collected over the whole measurement area. This ensures more versatile and reliable statistics for the measured samples. Moreover, the actual measurement phase consists of collecting received signal level samples. For each of the samples, the distance between the transmitter and the receiver must be known. The measurement configuration related to this thesis is present in more details in Chapter 6. The third phase of the tuning process contains pre-processing the measured samples. By doing this, the reliability of the sample set is increased, as the samples which may be corrupted or biased are removed. The samples which are taken too close or too far from the transmitter should be removed. The samples from the vicinity of the transmitter might be influenced by the near field and therefore give erroneous results. The samples which are taken too far from the transmitter might be influenced by the noise level of the receiving equipment. Also those samples that may be influenced by the line-of-sight component must be removed, if the tuned model is assumed to predict the path loss for the non-line-of-sight environment. The fourth phase of the tuning process includes tuning the actual model parameters. In general, this can be done based on iterative search methods, determinant based methods or neural network methods [31]. In this thesis a determinant based approach was chosen to solve the path loss exponent and offset correction for the power law path loss model.

36

The target of model tuning is to adjust the tunable parameters in such a way that the squared error between the prediction and the measurement is minimized. A mathematical method and all the related formulae for the problem of how to solve the model parameters are presented later in this chapter (Section 5.2.2). Finally, the fifth phase is to analyze how well the tuned model actually fits to the measurements. In general, the error performance between the prediction and the measurements is evaluated by means of the error standard deviation (STD) and the maximum absolute error. If the results satisfy the requirements, then the actual coverage dimensioning can be done based on the tuned model. However, if the model has many tunable parameters or the results are unreliable, then extra iterations might be required and the model must be tuned again.

5.2.2. Method for Solving the Model Parameters

The fourth step of the model tuning process is to adjust the tunable parameters of the basic path loss model. This section describes the mathematical background and shows the formulae which were used during this thesis. The model tuning process was done for a simple power law path loss model. The target of this model tuning process is to adjust the path loss exponent and intercept offset in such a manner that the prediction would correspond to the measurement samples as closely as possible. The basic power law path loss model as well as the initial condition for the model tuning is shown below.

00

( ) ( ) 10 log ii

dPL d PL dd

γ⎛ ⎞

= + ⎜ ⎟⎝ ⎠

(5.1)

Formula (5.1) shows the mean predicted value of the simple power law model (2.7), but in this case the random variable presenting the shadow fading component is omitted. Moreover, for the tuning purpose this model can be yet simplified and given as

i iPL a b Cγ= + + (5.2)

where the variable ã describes the constant reference offset value for the path loss model. This value depends on the system configuration, the carrier frequency and the propagation environment. The variable bi describes the constant part of the path loss exponent corresponding to the separation of a transmitter and receiver in case of the ith sample. The variable C is a correction coefficient which is used to adjust the constant offset ã to fit better to the measurements. The variable γ is a correction coefficient for the path loss exponent adjusting the slope of the prediction to fit better the measurements. Variables ã and bi are defined as follow,

37

0

20log4

adλπ

⎛ ⎞= ⎜ ⎟

⎝ ⎠ (5.3a)

0

10log ii

dbd

⎛ ⎞= ⎜ ⎟

⎝ ⎠ (5.3b)

The reference path loss value ã is approximated by using the free space path loss formula [1]. The variable λ is the carrier frequency and the variable d0 is the break point distance for the model. The break point distance describes the distance where the propagation path loss does not follow the free space loss anymore but instead attenuates faster corresponding to the path loss exponent γ. Therefore, for distances less than d0 the free space loss is assumed and the model given by formulae (2.7) and (5.1) is applicable only for the distances greater than d0. The target of model tuning is to find the best possible values for the variables γ and C in such a manner that the squared error between the prediction PLi and the measurement samples yi is minimized [32]. The squared error is given as

[ ]2

1( , )

n

i ii

err c y PLγ=

= −∑ (5.4)

where n is the volume of the measurement sample set. To fulfill the above-mentioned condition (5.4), all the partial derivatives of the err(c, γ) function must equal to zero, 0

0

err

errC

γ∂⎧ =∂⎪⎨∂ =⎪ ∂⎩

(5.5)

This problem (5.4) fulfilling the condition (5.5) can be solved with Cramer’s determinant based solution where n equations have to be solved.

1 1

2 2

1:2 :

: n n

i b C y ai b C y a

i n b C y a

γγ

γ

= + = −= + = −

= + = −

(5.6)

38

A matrix form for n equations (5.6) can be written as [32],

1 1 1

2 2 2

11

: : ,

1n n n

b y ab y a

W c YC

b y a

γ−⎡ ⎤ ⎡ ⎤

⎢ ⎥ ⎢ ⎥−⎡ ⎤⎢ ⎥ ⎢ ⎥× = × = =⎢ ⎥⎢ ⎥ ⎢ ⎥⎣ ⎦⎢ ⎥ ⎢ ⎥−⎣ ⎦ ⎣ ⎦

(5.7)

The optimal correction coefficients γ and C fulfilling the least squares condition are obtained from the least-squares solution of the matrix equation given as [32] 1

.LST Tc W W W Y

−⎡ ⎤= ⎣ ⎦ (5.8)

The above-mentioned formulae were implemented in Matlab and a measurement assisted path loss model was used to solve the general system coverage planning parameters as well as an accurate approximation of the propagation coverage range for the system under investigation.

39

6. PATH LOSS MEASUREMENTS

Path loss measurements consisted of a measurement campaign, where a received power level is observed over long propagation distances. This chapter describes how the path loss measurements were done as well as the results of the measurements. Firstly, the measurement configuration is explained in details. Afterwards, the measurement environment and the measurement routes are shown. The results section consists of the path loss exponent investigations (Section 6.3.1) and the shadowing standard deviation investigations (Section 6.3.2) based on the path loss measurements.

6.1. Measurements Configuration

Figure 17 illustrates the measurement configuration used for the path loss measurements. The measurement configuration consisted of a transmitter end and a receiver end elements. The main component of transmitter end is a signal generator which worked as a transmitter. An omni-directional quarter-wave dipole antenna with 3 dBi gain was used with the transmitter. The stationary transmitter antenna was mounted on a tripod and connected to the signal generator with a 10 meter feeder cable. The location of the transmitter was measured with a global positioning system (GPS) receiver. The receiver configuration was mainly made up of a spectrum analyzer which was used to store the received signal level values. The spectrum analyzer was located inside of a moving car and it took samples at intervals of one second. An omni-directional half-wave dipole antenna with 3 dBi gain was mounted on the roof of the car. The GPS receiver was mounted on top of the car. By doing this, a separation between a transmitter and receiver can be solved for each of the measurement samples. The height of the transmitter and receiver antennas was 1.5 meters, which corresponds well to the assumed mobile-to-mobile propagation scenario. The transmitter antenna location was stationary during the different measurements, which is not exactly the case for the mobile-to-mobile communications. However, as discussed in Chapter 2, the mobility of the receiver and the transmitter affects mainly the small scale characteristics and the Doppler spectrum. Therefore, while performing the large scale path loss measurements, the results are reliable even though the transmitter is stationary.

40

Signal Generator

Transmitting Antenna

Feedercable

Receiving antenna

GPS receiver

Spectrum Analyzer

Feedercable

Radio path

Figure 17: The measurement configuration for path loss measurements

In addition, some extra equipment such as extension cords, a car power inverter, a keyboard and a mouse were used to build up the measurement configuration but those have only a minor effect on the measurements or the results. The details of the measurement equipments are listed in Table 5.

Table 5: The list of the used measurement equipment Measurement equipment

Transmitter MLJ PCS-20 Frequency 914.2 MHz Amplitude 30 dBm Modulation CW

TX cable type RG214 TX cable length 10 m Attenuation @ 900MHz 2.4 dB

Transmitter antenna AV1950 TX antenna gain 3 dBi

Receiver antenna λ/2 Omni RX antenna gain 3 dBi RX antenna cable loss 3 dB

Receiver R&S FSL 3 Before performing the measurements, it must be checked that the equipment work properly. Also, the calibration of the receiver-transmitter chain must be performed. This is essential in order to know the exact real EIRP values and, therefore, to reduce the possibility of an unknown offset which affect the path loss measurements.

41

6.2. Measurements Environment

The measurements were performed during late November 2007 and the measurement campaign consisted of two slightly different kinds of propagation environments in Finland. The first set of the measurements was performed in Kissanmaa district which resembles a typical Finnish suburban region. The second set of the measurements was performed in Tammela district which consisted of only one longer measurement survey reflecting a typical Finnish urban region. In addition, some extra measurements were performed later during May 2008. The third measurement set was carried out in Hervanta which is another suburban district of Tampere.

6.2.1. Kissanmaa Region

The Kissanmaa district was chosen to characterize the typical Finnish suburban region which consists of a scarcely build-up environment with small houses and backyards. An average building height is relatively small and houses may have two to three floors. In addition, the region is characterized with parks and forest areas. Figure 18 illustrates the overall view of the typical Finnish suburban in Kissanmaa.

Figure 18: An overview photo of Kissanmaa [33]

42

In Kissanmaa, approximately 13% of the area is filled with the buildings and wood and brick are the most commonly used building materials. The average building height is less than eight meters. These statistics were evaluated from a digital map.

Kissamaa:Route 1

Kissamaa: Route 2

Kissamaa:Route 3

Kissamaa:Route 4

Figure 19: Measurement routes in Kissanmaa

The measurement set in Kissanmaa consisted of four different routes. The measurement routes and the transmitter location are shown in Figure 19. The three dashed lines show the measurement routes, which were fixed directly away from the transmitter. The purpose of this kind of measurement was to investigate the propagation slope behavior and the signal attenuation without significant impact of shadowing. However, this kind of measurement routes might be vulnerable for guided waves propagation phenomenon, where street structure and orientation help radio waves to propagate through the streets and give too optimistic measurement results. The fourth line shows the longer measurement route where samples were collected from different equidistant locations. This kind of measurement is more suitable for illustrating the behavior of the shadowing. The Kissanmaa region is a relatively flat region which can be seen in Figure 20 presenting the topographic point-to-point profile for the transmitter and end points of Route 1 and Route 3. The upper topography profile shows the ground height variation for the Route 1 and lower topography profile shows the ground height variation of Route 3. The square blocks illustrate the obstruction buildings along the measurement routes.

43

Figure 20: A topography profile for Kissanmaa

6.2.2. Tammela Region

Tammela district was chosen to represent a typical Finnish urban region which consists of blocks of densely build-up buildings. The average building height is relatively small but higher than in typical Finnish suburban regions as buildings may have five to eight floors. In addition, the region is characterized by parks and open parking place areas. Figure 21 illustrates the overall view of the typical Finnish urban area in Tammela. At Tammela, approximately 20% of the area is filled with buildings and the average building height is more than 15 meters. Buildings are mainly concrete buildings. The measurement plan for Tammela consisted of one longer route which is shown in Figure 22, together with the transmitter location which is marked with a big arrow. The line marked in the map shows the route of the measurement and the main purpose of this kind of sample collection strategy was to collect samples in all radiated directions. This kind of measurement is more suitable for the measurements which illustrate the behavior of the shadowing. Moreover, if the number of the collected samples is high enough, then the path loss exponent can also be evaluated from the results. It was assumed that the impact of the stationary transmitter and near-by objects which affected propagation can be mitigated if the samples are collected all around the transmitter and, thus, give a better overall view of the propagation conditions.

44

Figure 21: An overview photo of Tammela [33]

Figure 22: A measurement route in Tammela

45

Figure 23 presents the topographic point-to-point profile of the Tammela region. The upper topography profile shows the ground height variation for the point-to-point profile from the upper left corner to the lower right corner of the measurement route figure. The lower topography profile shows the ground height variation for the point-to-point profile from the upper right corner to the lower left corner. Again the square blocks illustrate obstruction buildings along the measurement routes.

Figure 23: A topography profile for Tammela

As seen in the topographic profiles, the building density as well as the average building height is higher in the Tammela region compared with the Kissanmaa. Also, there is more ground height variation in Kissanmaa and Tammela region is more flat compared with Kissanmaa region.

6.2.3. Hervanta Region

Hervanta district represent a typical Finnish urban region which consists of blocks of densely build-up buildings. However, the Hervanta measurement was performed in the vicinity of the campus area of Tampere University of Technology (TUT) and the campus area consists of a few concrete buildings and the average building height is less than 15 meters. In addition, the measurement region is characterized by thick forest and open parking areas and approximately only 8 % of the area is buildings. Figure 24 illustrates the overall view of the measurement area in Hervanta. The measurement plan for Hervanta consisted of one longer route which is shown in Figure 25. The line marked in the map shows the route of the measurement. The main purpose of the Hervanta measurement was to collect more measurement data and ensure a clear near field region for the transmitter.

46

Figure 24: An overview photo of Hervanta [33]

Figure 25: A measurement route in Hervanta

47

Figure 26 presents the topographic point-to-point profile of the Hervanta region. The upper topography profile shows the ground height variation for the point-to-point profile from the upper left corner to the lower right corner of the measurement route figure. The lower topography profile shows the ground height variation for the point-to-point profile from the upper right corner to the lower left corner. Again the square blocks illustrate obstruction buildings along the measurement routes.

Figure 26: A topography profile in Hervanta

As seen in the topographic profile, the building density is quite scarce and the average building height is less than in the Tammela region. However, there is more ground height variation present in Hervanta compared with the two other cases. This affects to the propagation characteristics of the radio waves, and therefore, the comparison of the measurement results of all three cases is not straightforward at all. Table 6 summarizes the building density and height statistics for Kissanmaa, Tammela and Hervanta regions derived from the height profiles and digital map information.

Table 6: Digital map statistics for measurement regions Kissanmaa Tammela Hervanta Average Building density 13 % total area 21 % total area 8 % total area Average building height less than 8 m more than 15 m less than 15 m

48

6.3. Measurement Results

This section shows the measurement results of all the routes in Kissanmaa, Tammela and Hervanta. These results provide a set of parameters for an adjusted path loss model which minimizes the squared error between the prediction and the sample survey. The error standard deviation between the samples and the prediction is a good measure of shadow fading, if the samples are collected in the right manner which smoothes out the fast fading component. This can be identified by observing the error distribution function. For the parameter adjustment and the analysis, all samples further than 50 meters away from then transmitter were considered.

6.3.1. Results for Kissanmaa Region

Figure 27 shows the measurement route and the recorded continuous wave (CW) signal samples in dBm scale. This illustrates the overall propagation situation during the measurements in Kissanmaa. Only the samples between -40 dBm and -120 dBm were included into the analysis.

Figure 27: Kissanmaa sample survey

It can be observed in Figure 27 that the real propagation environment differs from the homogeneous environment. In the vicinity of the transmitter, the signal levels are relatively high and the attenuation of signals occurs rather equally in all directions. However, while moving further away, the different propagation paths have different

49

local propagation characteristics and the paths fade unequally as a function of distance. On Route 3, the signal propagates well and the received power level is still detectable at the end of the route, where the distance between the transmitter and the receiver was approximately two kilometers. On the other hand, on Route 1, the signal is lost after one kilometer. Also some parts of Route 4 measurements were lost and especially the signal levels in the region between the Routes 1 and 3 were extremely low. In that case, only a few samples after 600 meters were above the level of -120 dBm. This indicates that the propagation distance can vary a lot depending on local propagation conditions. Figure 28 shows the received power levels for all Kissanmaa routes as a function of distance. A solid black line shows the mean predicted value for the received power based on all four different Kissanmaa measurements. Colored dashed lines show the mean predicted signal levels for the individual measurements. In the beginning of the measurement, the mean predicted signal level is already at the level -83 dBm and the system noise floor -130 dBm is reached after 2 kilometers.

Figure 28: Received power levels for Kissanmaa routes

Routes 1, 2 and 4 have the same kind of propagation characteristics according to the offset and the path loss exponent. This can be observed in the figure, as the prediction curves are partly overlapping and close to each other. Moreover, Route 3 has a similar kind of behavior according to the path loss exponent which defines the slope of the prediction curve. However, the curve presenting Route 3 is greatly above the other curves which indicate that there has been less attenuation between the transmitter and the receiver within the first 100 meters.

50

The transmission powers for all Kissanmaa measurements were set to 30 dBm and it was assumed that there were no major losses besides the cable loss of 3.2 dB. Therefore, the effective isotropic radiated power was assumed to be approximately 30 dBm due to the fact that the antenna gain is 3 dBi. If this is compared with the received power levels at 100 meters, the difference is rather large and the signal path loss is much more than expected for the first 100 meters. This might be due to the local obstructing obstacles, such as buildings, which are affecting less to Route 3 and more to the other routes. This explains the relative offset differences between the curves. Figure 29 shows the probability distribution function for the difference between the prediction and the measured samples for all Kissanmaa routes. For a measurement set where the fast fading is smoothed out and there are no other external errors biasing the results, then only a shadow fading is present in the error between the prediction and measurements. Therefore, the probability distribution of the error should be log-normally distributed.

Figure 29: A probability function for the difference between the prediction and measurements in Kissanmaa If the error is drawn in a decibel scale, then the measured error should correspond to the normal distribution. The standard deviation of 8.2 dB was measured for the error between the prediction and the measurement. However, the measured probability density function does not correspond perfectly the normal distribution. In this case, the error distribution is biased by Route 3 measurement as in that case, the measured samples were relatively stronger compared to the mean value of the measurements.

51

Table 7: Kissanmaa measurement statistics Kissanmaa Path loss exponent γ Shadowing STD

Route 1 2.6 6.9 dB Route 2 3.8 3.7 dB Route 3 3.7 5.6 dB Route 4 3.7 4.4 dB

All routes 3.6 8.2 dB Table 7 shows the path loss exponents as well as the shadowing standard deviations for all the Kissanmaa measurements. As seen in the table, the path loss exponent varies quite much depending on the selected route and the local propagation conditions. The lowest value 2.6 was measured on Route 1. The rest of the measurement routes provided quite high path loss exponents with the average value of 3.7. However, the overall value for the path loss exponent is 3.6 over all the measurement samples in Kissanmaa. The shadowing standard deviations are rather low for individual straight route measurements as there are not enough measurement samples collected from spatially different locations within an equally long distance. Therefore, a better measurement for the shadowing standard deviation is obtained if the analysis is based on all the measurement samples. The average shadowing standard deviation for Kissanmaa was found to be 8.2 dB.

6.3.2. Results for Tammela Region

Figure 30 shows the recorded measurement route and the recorded values for samples in a dBm scale, which illustrates well the overall propagation scenario during the measurements in Tammela. Only the samples between -40 dBm and -120 dBm were included into the sample survey figure. As seen in the figure, the signals propagated quite well in all directions in the Tammela region within the first 100 meters. On the other hand, the signal levels decreased faster compared with the Kissanmaa region after the first 100 meters. This is a typical behavior between an urban and a suburban region. The transmitter was placed in a rather open area and thereby strong signal components propagate further on the vicinity of the transmitter compared with the Kissanmaa sample surveys.

52

Figure 30: Tammela sample survey

Figure 31 shows the received power for the Tammela measurement as a function of distance. A solid black line shows the mean predicted value for the received power based on the Tammela measurement. The mean received power level in the beginning of the prediction is -73 dBm and a system noise floor -130 dBm is reached already after 1 kilometer. In this case, the mean signal level in the beginning of the prediction is nearly 10 dB higher compared with the situation in the Kissanmaa. This is mainly due to the fact that there were less obstructing clutters in the vicinity of the transmitter and the signal attenuated less within the first 100 meters.

Figure 31: Received power levels for Tammela measurement

53

Figure 32 shows the probability distribution function for the difference between the prediction and the measured samples for the Tammela route. As seen on the figure, the measured shadowing standard deviation corresponds well to the theory and the shadowing is log-normally distributed with the standard deviation of 7.7 dB. Therefore, in both measurement cases the measured shadowing standard deviation was practically the same.

Figure 32: A probability density function for the difference between the prediction and measurements in Tammela The path loss exponent was measured to be nearly 5 in Tammela. This is a high value compared with a similar macro cellular environment where the transmitter antenna would be located above the rooftops. The standard deviation of the error was measured to correspond 7.7 dB. For Kissanmaa and Tammela the standard deviations were similar but there was a dramatic difference in path loss exponents as well as the offsets of the prediction curves. In contrast to Tammela, the path loss exponent was above three in Kissanmaa. For the measurement routes two, three and four the path loss exponent was nearly four.

54

6.3.3. Results for Hervanta Region

Figure 33 shows the measurement route and the recorded values for the measurements in Hervanta. As seen in the figure, the signal propagated quite well in all directions in Hervanta within the first 300 meters.

Figure 33: Hervanta sample survey

Figure 31 shows the received power for the Hervanta measurement as a function of distance. A solid black line shows the mean predicted value for the received power. The mean received power level in the beginning of the prediction is -73 dBm and a system noise floor -130 dBm is reached already after 1 kilometer. In Hervanta case, the mean signal level in the beginning of the prediction is much higher compared with the situations in the Kissanmaa or Tammela. This is mainly due to the fact that the

55

102

103

−120

−110

−100

−90

−80

−70

−60

−50

Rec

eive

d po

wer

[dB

m]

TX−RX separation [m]

MeasurementPrediction

Figure 34: Received power levels for Hervanta measurement

Figure 35 shows the probability distribution function for the difference between the prediction and the measured samples for the Hervanta route. As seen on the figure, the measured shadowing standard deviation corresponds well to the theory and the shadowing is log-normally distributed with the standard deviation of 6.1 dB.

−30 −20 −10 0 10 20 300

0.01

0.02

0.03

0.04

0.05

0.06

0.07Probability density function

Prob

abili

ty

Error [dB]

Measured dataNormal pdf

Figure 35: A probability density function for the difference between the prediction and measurements in Tammela

56

Table 8 summaries the path loss exponents as well as the shadowing standard deviations for all three measurement cases. The measured path loss exponent was nearly 5 in Tammela and Hervanta. In Kissanmaa, the measured path loss exponent was 3.6. These are quite high values compared with similar macro cellular environments (Section 2.3.2), where the transmitter antenna would be located above the rooftops. The standard deviation of the error was measured to correspond 7.7 dB in Tammela, 8.2 dB in Kissanmaa and 6.1 dB in Hervanta. For Kissanmaa and Tammela the standard deviations were similar but there was a dramatic difference in path loss exponents as well as the offsets of the prediction curves.

Table 8: Summary of measurement statistics Path loss exponent γ Shadowing STD

Kissanmaa 3.6 8.2 dB Tammela 4.9 7.7 dB Hervanta 5.1 6.1 dB

57

7. COVERAGE DIMENSIONING

The final coverage dimensioning is done based on the measurement results of the standard deviation and the path loss exponent. In addition, some offset corrections are applied for the chosen propagation model to adjust the propagation model to fit better to the measurement results. This chapter shows the path loss models for a mobile-to-mobile environment in a typical Finnish urban and suburban environment derived from the measurements.

7.1. Adjusted Propagation Model

An adjustable basic propagation model was shown in formula (5.1) and it was chosen for the model tuning due to the fact that the model was simple to use and tune. The original model was based on a dual slope assumption, where it was assumed that the path loss of the signal propagation would follow the free space loss model at least for 50 meters and then attenuate more rapidly due to the mobile-to-mobile environment characteristics. However, a near-field measurement between the transmitter and receiver indicated that this statement was not true always and an extra offset correction was added to the propagation model to provide a better-fit model in case of Kissanmaa and Tammela. The results of the near-field measurements are shown in Figure 36.

20 30 40 50 60−70

−60

−50

−40

−30

−20−20

Rec

eive

d po

wer

[dB

m]

TX−RX separation [m]

LOS PredictionHervantaKissanmaaTammela

Figure 36: A near-field received power levels

58

The near-field measurements show the received signal levels for the first 50 meters. The dashed black line shows theoretical values for the received signal levels which would follow the free space loss path loss model in case of a 30 dBm transmitter. This was the configuration during the all measurements. As seen in the figure, the reference line-of-sight measurement fits well to the free space loss assumption in Hervanta when there are no obstacles in the vicinity of the transmitter. However, the practical measurements in Kissanmaa and Tammela are nearly 30 dB below the assumed free space loss reference point at the 50 meters distance. This might be due to many reasons. There might have been a lot of obstruction obstacles and the line-of-sight connection might have been lost earlier comparing with the assumed reference point. If there were many scatterers, a clutter loss or a loss due to the extra reflections might explain the nearly 30 dB difference between the model and the measured values. On the other hand, there is already a nearly 20 dB loss present in the beginning of the measurement after the first 10 meters. Therefore, it is possible that there might have been some extra losses due to loose cable connections or broken connectors. This is a problematic situation, since for reliable coverage predictions, the effective isotropic radiated power must be known precisely. A loose cable connection may cause the EIRP to be less than the assumption even though the transmission power is set correctly to 30 dBm. In practice, it might be that the offset is a combination of all above-mentioned reasons. Table 9 shows the tuned path loss model parameters for a typical Finnish suburban and urban region with the assumption that the offset parameter is caused only due to the propagation environment characteristics. Therefore, the real radiated power from the antenna was assumed to be 30 dBm.

Table 9: Model tuning parameters for a power law path loss model Environment Clutter Offset C Path loss exponent γ Shadowing STD

Kissanmaa 31.5 dB 3.6 8.2 dB Tammela 23.5 dB 4.9 7.7 dB Hervanta 6.8 dB 5.1 6.1 dB

The clutter offset correction C is because of the obstructing clutter and the multiple reflections. For a suburban environment it was found to be 31.5 dB in Kissanmaa and for an urban environment 23.5 dB in Tammela. In Hervanta, the required clutter offset correction was only 6.8 dB. The path loss exponents for the same environment categories are 3.6, 4.9 and 5.1. Figure 37, Figure 38 and Figure 39 illustrate the adjusted path loss models with the corresponding reference models without the offset correction.

59

Figure 37: A path loss curve for Kissanmaa

Figure 38: A path loss curve for Tammela

60

Figure 39: A path loss curve for Hervanta

In figures, the solid black curves indicate the adjusted models with a proper path loss exponent and the offset correction. The dark grey dashed lines indicate the reference models without offset corrections. The light grey markers show the actual measured path loss values.

7.2. Coverage Range Estimation

An estimation of the coverage range can be done based on the tuned path loss model or the path loss figures. For the configuration defined in the link budget (Table 2), the maximum allowable path loss was defined to be 130 dB. The maximum allowable path loss is incorporated with a body loss margin and a shadow fading margin to provide more reliable coverage prediction range estimation. Moreover, the area location probability of 95% was assumed. Formula (7.1) is used to find out the coverage range which fulfills the maximum allowable path loss of 130 dB providing the required 95% area location probability. The formula (7.1) is derived by re-ordering the power law path loss model (5.1) which is incorporated with the offset correction factor C and the path loss exponent γ. 0

0( ) log( )

1010MAXPL PL d C d

d γ− −⎛ ⎞

+⎜ ⎟⎝ ⎠=

(7.1)

The variable PLMAX is the maximum allowable path loss value and the variable PL(d0) is the path loss value at the reference distance d0. The reference distance was assumed to

61

be 50 meters with the path loss value of 65.5 dB which was derived from the free space loss formula (5.3a). The offset and the path loss variables were shown in Table 9. According to the formula (7.1) following coverage ranges for typical Finnish urban and suburban environments were obtained.

Table 10: An estimation of the coverage range Coverage Range Tammela Kissanmaa Hervanta

With planning margins 340 m 410 m 665 m Without planning margins 420 m 550 m 830 m Reference model without offset C 1.1 km 3.1 km 900 m The estimations in all cases are for an outdoor-to-outdoor radio channel without any building penetration losses. In the first case in Table 10, the maximum allowable path loss was 130 dB incorporated with the planning margins. If the shadow fading margin and the body losses are included to the dimensioning, then coverage range of 95% area location probability corresponds to 410 meters in Kissanmaa, 340 meters in Tammela and 665 meters in Hervanta. Without the planning margins, the coverage range estimation corresponds to 50% point location probability at the edge of the coverage range. In this case, the coverage range estimation is 550 meters in Kissanmaa, 420 meters in Tammela and 830 meters in Hervanta. On the other hand, if the offset correction C is omitted from the solution and it is assumed that the propagation would follow better the reference model, then the coverage range estimations would correspond to three kilometers in Kissanmaa, one kilometer in Tammela and 900 meters in Hervanta with 95% point location probability at the edge of the communication range. This would be the case, if the measured signal levels were not biased and the offset would be caused only due to a malfunctioning transceiver or improperly connected cables. Also the local radio conditions and the obstructing obstacles affect to the propagation greatly. This can be observed in Figure 27, where the bottommost route in suburban environment provided coverage of nearly for 2 kilometers and the top most route provided coverage of only one kilometer.

7.3. Reliability Analysis

Reliability analysis of the measurement results points out two main areas of interest which can affect the accuracy of the results presented in this thesis. The first category is a measurement process. The measurement process consists of configuration of the measurement equipment, planning measurement routes, collecting measurement samples and preprocessing the measurement samples. The second category is analyzing process. The analyzing process consists of selecting measurement samples and a proper model for analysis.

62

During the measurements, a proper configuration of the measurement equipment is required for knowing the radiated power as accurately as possible. This is required if any conclusions of the absolute values are made, such as, what is the exact received power or path loss value for a given distance. If there is uncertainty about the EIRP value, then only relative values can be evaluated like how path loss behaves as a function of distance showing, how rapidly it attenuates or how much it varies over the mean value. To know the EIRP value as accurately as possible, some near field measurements under LOS conditions should always be included into the measurement set. The location of the transmitter and all obstacles in the vicinity of transmitter affect the local propagation conditions and the measurement results. Therefore, to ensure versatile and rich measurement statistics, at least a few different transmitter locations should be included into the overall measurements. For example, if the transmitter is located too close to the nearest building and all measurement routes begins behind the building then all measurements could be biased too early compared to the predicted behavior. Earlier studies which investigated peer-to-peer communications showed that propagation behind the building corner in urban areas can attenuate signal 20-25 dB [26]. This can explain the difference between the offsets of Route 3 and other routes in Figure 28. Planning of the measurement routes as well as collecting measurement samples have a big impact on the validity of the results. Measurement routes should be planned in such a manner that enough samples are collected over the whole investigated area. Even though the analysis is done perfectly but by using too small set of samples which do not give an unambiguous illustration of the area type, then the validity of results is questionable. How accurately these results really correspond with this environment? Also the sample pre-processing affects the accuracy and validity of the results. Line-of-sight samples and samples below the noise level should be removed from the measurements before the analysis. During the analysis of measurement samples, a few things were observed to affect the behavior of the model accuracy. Firstly, the size of the measurement sample set which is included to the analysis. In Kissanmaa, the sample set sizes varied between 400 and 2000 samples depending on the measurement route. If all samples would be included to the analysis then the characteristics of the longest route would bias the results. Another problem considers the sampling. During the measurements, only one sample per second was taken. On one hand, this smoothes out the fast fading component away from the signal. On the other hand, if the mobile speed varies a lot then those areas where mobile moves really slowly are weighted more compared with the areas where the mobile speed is higher. This would also cause biasing to the results and must be taken into account. The above-mentioned problems were solved by taking into account 300 random samples from each of the measurement set in Kissanmaa and half of the

63

samples in Tammela. This provided almost and uniform distribution for the number of collected samples per distance, which was assumed to remove the effect of biasing. However, an insignificant variation was observed to be present in the path loss exponent as well as in the location variability parameter depending on the randomly selected samples. It was also observed during the analysis that the selected distance d0 affected to the absolute values of the path loss exponent and the location variability depending how well the reference model and the measurements correlated. This can be observed in path loss figures Figure 37, Figure 38 and Figure 39, where the behaviors of the slope curves are clearly present. In Tammela and Hervanta, the 50 meter assumption fits well to the reference model but in Kissanmaa a reference distance assumption of 20 meters or 30 meters might have been better.

64

8. CONCLUSIONS AND DISCUSSION

The main purpose of this thesis was to provide practical large scale path loss parameters which can be used for coverage dimensioning of a mobile-to-mobile wireless system. Coverage dimensioning is essential and important part of wireless system design and it is used for verifying whether or not the system under design is capable to meet the given coverage requirements. Coverage dimensioning requires some information about the local propagation conditions and details of the assumed system configuration. A suitable propagation model for path loss predictions is selected based on the local propagation conditions. On the other hand, the path loss requirements for coverage dimensioning are derived from the link budget which outlines the system configuration. The results of this thesis verified that the path loss exponent for a typical Finnish suburban and urban environment is higher for mobile-to-mobile environment (Section 7.1) compared with the corresponding base station to mobile environment (Section 2.3.2). The results indicated that in practice, the value of the path loss exponent can be assumed to be at least one or two units larger. This difference is a consequence of two facts. Firstly, likelihood of the line-of-sight connection between a transmitter and a receiver is reduced when both antennas are placed close to ground level. Secondly, propagation in mobile-to-mobile environment is characterized by multiple reflections which cause the propagation conditions to be more challenging as the radio path is assumed to be obstructed with many obstacles. Therefore, the signal path attenuates more when the separation between a transmitter and a receiver increases. The standard deviation of the shadowing was also investigated during this thesis. The results indicated that the standard deviation is higher for a mobile-to-mobile environment (Section 7.1) compared with the macro cell environment (Section 2.3.3). Moreover, the local propagation conditions have a big impact to the propagation characteristics of individual propagation paths. This was observed while comparing standard deviation of different Kissanmaa routes with the overall Kissanmaa standard deviation which was relatively higher. For a macro cell environment, there might have been more similarities between the routes and less variation. However, the verification of this assumption would require macro cell measurements, and it is left for future investigations. The results of this thesis can be put to use while planning future wireless systems. It is likely that in near future cognitive networks, relay communications and sensor networks

65

are playing a big role in next generation cellular systems. Therefore, it is import to be able to predict how far the signal can propagate in an environment where the transmitter and the receiver are placed close to the ground. This information is needed for predicting the capacity requirements and interference conditions for the coverage area where the signal is assumed to propagate in mobile-to-mobile environment. For example, the coverage area can be used for approximating the number of transmitters which are competing for the available channel resources and therefore affecting to the system capacity requirements. The measurement process and the analysis of the measurements also gave some insights to the issues which are affecting to the final model tuning results. What has to be considered while planning and doing the measurements? What is the impact of the different methods of analyzing the measurements samples? How many samples are required for characterizing different environments or how these samples should be pre-processed in order to able to get reliable results? However, to be able be to understand the impact of the above-mentioned topics more theoretical as well as practical investigations are required and these are left to be covered in the future work.

66

REFERENCES [1] Saunders, S.R., “Antennas and Propagation for wireless communications

systems”, John Wiley & Sons, 2005

[2] Vaughan, R. & Andersen, J. B., “Channels, propagation and antennas for mobile communications”, The institution of Electrical Engineers, London, 2003

[3] M. K. Simon & M Alouini, “Digital Communications over Fading Channels: A Unified Approach to performance Analysis”, John Wiley & Sons, 2000

[4] Rappaport, S. T., “Wireless communications – principles and practice”,

prentice-hall, 2002 [5] COST 231 Final Report, “Digital Mobile Radio towards future generation

system”, European Commission, 1999, chapter 4, pp. 134-135

[6] Lempiäinen, J. & Manninen, M. “Radio Interface System Planning for GSM/GPRS/UMTS” Hingham, MA, USA: Kluwer Academic Publishers, 2001, chapter 5, pp. 102-119.

[7] Akki R.S. and F. Haber, “A statistical model for mobile-to-mobile land communication channel”, IEEE Trans. Veh. Technol., vol. VT-35, no. 1, pp. 2-7, Feb. 1986.

[8] Akki R.S., “Statistical properties of mobile-to-mobile land communication channels”, IEEE Trans. Veh. Technol., vol. 43, no. 4, pp. 826-831, Nov. 1994.

[9] Akki R.S., “The influence of Mobile-to-Mobile Land Communication channel spectrum on the error rate of Binary DPSK and NFSK Matched filter receivers”, IEEE Trans. Veh. Technol., vol. 43, no. 4, pp. 832-836, Nov. 1994.

[10] Linnartz, J. M.G. & Diesta, R.F., “Evaluation of Radio and Networks”, Tech. Rep. UCB-IT-PRR-96-16, University of California, Berkeley, PATH Research, California, 1996.

[11] Kovács Z. I.,” Radio Channel Characterization for Private Mobile Radio Systems Mobile-to-Mobile Radio Link Investigations”, Ph.D. Dissertation, 2002 Sep.

[12] R. Wang & D. Cox, “Channel modeling for Ad hoc Mobile Wireless Networks”, IEEE WTC2002, 2002

[13] Patel C.S., Stuber G.L and Pratt T.G., “Simulation of Rayleigh Faded Mobile-

to-Mobile Communication Channels”, IEEE Transactions on Communications, Vol. 53, No. 11, Nov. 2005

67

[14] Patel C.S., “Wireless channel modeling, simulation and estimation”, PhD Dissertation, Georgia Institute of Tech., May 2006, G

[15] Zajic A. G. & Stuber G. L., “A New Simulation Model for Mobile-to-Mobile Rayleigh Fading Channel”, IEEE WCNC2006, 2006

[16] Wang, L. & Cheng, Y., “A Statistical mobile-to-mobile Rician fading channel

model”, Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st

[17] Vatalaro, F., & Forcella, A., “Doppler spectrum in mobile-to-mobile communications in the presence of three-dimensional multipath scattering” IEEE Trans. Veh. Technol., vol. 46, no. 1, pp. 213-219, Feb. 1997.

[18] Hassan M. & Vainikainen P., ” Impacts of environment and antenna height on angular spread of radio wave propagation in a city street microcell”

[19] Morgensen, P.E., Eggers P., Jensen C., and Andersen J. B., ”Urban Area Radio Propagation Measurements at 955 and 1845 MHz for Small and Micro Cells”, IEEE GLOBECOM ’91, 1991

[20] Maciel, L.R., Bertoni, H. L. and Xia, H.,”Unified Approach to prediction of Propagation Over Buildings for All Ranges of Base Station Antenna Height”, IEEE Transactions on Vech. Tech., Vol. 42, No. 1, Feb. 1993

[21] Harley, P., ”Short Distance Attenuation Measurements at 900 MHz and 1.8 GHz using Low antenna Heights for Microcells”, IEEE Journal on Selected areas in communications, VOL. 7, NO. 1, January 1989

[22] Blackard, K.L., Feuerstein M.J., Rappaport T.S., Seidel S.Y. and Xia H.H., “Path loss and delay spread models as functions of antenna height for microcellular system design", IEEE 42nd Vehicular Technology Conference, 1992

[23] Blackard, K.L., Feuerstein M.J., Rappaport T.S., Seidel S.Y. and Xia H.H., "Path loss, delay spread, and outage models as functions of antenna height for microcellular system design", IEEE Transactions on Vehicular Technology, 1994

[24] Sohrabi, K., Manriquez, B. and Pottie, G., ”Near-ground Wideband Channel Measurements in 800-1000 MHz”. Proc. 49th IEEE Conf. on Vehicular Technology VTC, 1999, Vol. 1

[25] Joshi, G.G., Dietrich Jr. C.B., Anderson C.R., Newhall W.G., Davis W.A., Isaacs J. and Barnett G., “Near-ground channel measurements over line-of-sight and forested paths”, IEEE Proc. Microw. Antennas Propag., Vol. 152, No.6, December 2005

68

[26] Lotse, F. & Wejke A., ”Propagation Measurements for microcells in central stocholm”, IEEE 1990

[27] Harrold, T.J., Nix A.R. and Beach M.A., “Propagation studies for mobile-to-mobile Communications”, IEEE 2001

[28] Nielsen, J.O., Pedersen G.F., Olesen, K. and Kovacs I.Z., "Statistics of measured body loss for mobile phones" Antennas and Propagation, IEEE Transactions on Volume 49, Issue 9, Sept. 2001

[29] Durante, J.M., "Building penetration loss at 900 MHz" Vehicular Technology Conference, 1973. 24th IEEE Volume 24, Dec. 1973

[30] Turney, W., Karam M., Malek L. and Buchwald, G.,"VHF/ UHF Building Penetration Characteristics When Using Low Antenna Heights" New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE International Symposium on April 2007

[31] Nawrocki, M., Dohler, M. and Aghvami, A., “Understanding UMTS Radio Network – modelling, planning and automated optimization”, John Wiley & Sons, 2006, chapter 5, pp. 105-110.

[32] Medeisis, A. & Kajackas, A., “On the Use of the universal Okumura-Hata propagation model in rural areas”, IEEE VTC2000, 2000.

[33] Lentokuva Vallas Oy, “Tampereen Kaupungin viistokuvat”, 1998