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Effect of system and environmental parameters upon coverage provided by the ORBCOMM land mobile satellite… Ma, Jueren (Steven) 2005

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Effect of System and Environmental Parameters Upon Coverage Provided by the ORBCOMM Land Mobile Satellite System by Jueren (Steven) M a B.Eng., Huazhong University of Science and Technology, 1997 A THESIS S U B M I T T E D IN P A R T I A L F U L F I L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F M A S T E R O F A P P L I E D S C I E N C E in The Faculty of Graduate Studies (Electrical and Computer Engineering) T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A Apr i l 2005 © Jueren (Steven) Ma, 2005 11 A b s t r a c t While land mobile satellite systems operating at V H F (150 MHz) should provide better coverage in urban and suburban environments than similar systems that operate at L-band (1 GHz and above), previous work has not quantified the improvement. Here, we fill that gap by presenting results obtained with a 3D-satellite signal propagation model that we have developed around the N E C - B S C U T D code. Results obtained using a representative street geometry show that the mean signal at V H F may be as much as 6 dB stronger than that at L-band with a standard deviation that is almost 2 dB less. Results also show that an antenna with a hemispherical pattern can provide much more effective coverage than the A/4 monopole antenna which has traditionally been the most popular antenna for O R B C O M M applications. Ill C o n t e n t s A b s t r a c t ii Contents iii List of Tables vi List of F igures 1 vii Acknowledgements ix 1 Introduct ion 1 1.1 Statement of Problem 1 1.2 Background and Motivation 1 1.3 Objective and Approach 2 1.4 Thesis Outline 5 2 L i t era ture Rev iew 6 2.1 Introduction 6 2.2 Satellite Signal Blockage Modeling 7 2.2.1 Empirical Model - ERS Model 7 2.2.2 Statistical Model - Loo Model 8 2.2.3 Two-State Markov Model - Lutz Model 9 2.2.4 Physical-Statistical Model 11 2.3 Building Penetration by V H F Satellite Signals 13 2.3.1 Building Penetration Measurement at 137 M H z 13 2.3.2 Measurement of Indoor Attenuation at 144 M H z 14 Contents iv 2.4 Limitations of Previous Work 14 3 M e t h o d o l o g y 16 3.1 Introduction 16 3.2 Simulation Model 17 3.2.1 Signal Flow of Propagation Prediction Model 17 3.2.2 Utilization of Propagation Prediction Model 19 3.2.3 SatTrack 20 3.2.4 Numerical Electromagnetic Code - Basic Scattering Code 21 3.3 Field Measurement 24 3.3.1 Data Collection Kit - Hardware and Software 25 3.3.2 Problems with Data Collection Kit and Further Development . . . . 26 3.3.3 Measurement Location Selection 29 3.4 Validation of Computer Simulation Model 30 3.4.1 Purpose of Validation 30 3.4.2 Feature Selective Validation 31 3.4.3 Validation Results 33 4 System Coverage in S u b u r b a n and U r b a n E n v i r o n m e n t s 39 4.1 Introduction 39 4.2 Influence of Wavelength 40 4.2.1 Theoretical Background 40 4.2.2 Simulation Results and Analysis 42 4.3 Influence of Degree of Buildup 49 4.3.1 Theoretical Background 49 4.3.2 Simulation Results and Analysis 49 4.4 Variation of Coverage with Latitude 56 4.4.1 Theoretical Background 56 4.4.2 Simulation Results and Analysis 57 4.5 Influence of Terminal Antenna Pattern 59 4.5.1 Theoretical Background 59 Contents v 4.5.2 Simulation Results and Analysis 60 5 Conclusions and Recommendat ion 69 5.1 Conclusions 69 5.2 Recommendations for Future Work 70 References 72 A Sample of Sa tTrack O u t p u t 75 B Sa tTrack T L E D a t a U p d a t e and Pred ic t ion R u n n i n g P r o c e d u r e . . . . 76 B . l Updating the Orbcomm . T L E file 76 B.2 Running Simulation for Al l The Orbcomm Satellite 76 C D a t a Col l ec t ion C o d e 78 vi L i s t o f T a b l e s 3.1 Spectrum scan at 2 m wavelength band on Macleod building roof 27 3.2 Satellite to subscriber link budget at minimum elevation angle and edge cov-erage 29 3.3 Field measurement location description 30 3.4 F S V interpretation scale [1] 32 3.5 Validation results - A D M values 33 4.1 Classification of degree of buildup 49 4.2 Comparison of signal strength statistics between low-profile antenna and A/4 monopole 60 vii L i s t o f F i g u r e s 1.1 Constellation of O R B C O M M system . . . 3 3.1 Flow diagram of propagation simulation tool 18 3.2 Simulation geometry 18 3.3 Probability density function of elevation angle for O R B C O M M constellation 21 3.4 Hemispherical radiation pattern approximating L P R antenna 24 3.5 Radiation pattern of A/4 monopole antenna 24 3.6 Connection diagram 25 3.7 L P R antenna 26 3.8 L N A 26 3.9 SC 26 3.10 RSSI in sports field without L N A , 28 3.11 Richmond centre geometry 30 3.12 Marpole residence geometry 30 3.13 Time series excess path loss (long duration) 35 3.14 Time series excess path loss (short duration) 36 3.15 Magnitude response of low pass filter for A D M calculation 37 3.16 Complementary cumulative distribution function of excess path loss for val-idation 38 4.1 Complementary cumulative distribution function of RSSI at 138 M H z and 1.3 GHz 45 4.2 Complementary cumulative distribution function of excess path loss at 138 MHz and 1.3 GHz 46 List of Figures viii 4.3 Mean duration of connection vs. RSSI threshold at 138 M H z and 1.3 GHz . 47 4.4 Time share vs. RSSI threshold at 138 MHz and 1.3 GHz 48 4.5 Mean excess path loss at difference degree of buildup 52 4.6 Standard deviation of excess path loss and RSSI at difference degree of buildup 53 4.7 Mean excess path loss at difference degree of buildup 54 4.8 Linear curve fitting for mean RSSI in urban area, Vancouver 55 4.9 Geometry of elevation angles at different latitude 62 4.10 Complementary cumulative distribution function of free space path loss . . 63 4.11 Mean and standard deviation of free space path loss 64 4.12 Complementary cumulative distribution function of elevation angle at differ-ent latitude 65 4.13 Complementary cumulative distribution function of excess path loss at dif-ferent latitude 66 4.14 Complementary cumulative distribution function of signal strength at differ-ent latitude 67 4.15 Complementary cumulative distribution function of signal strength with dif-ferent antenna pattern 68 ix Acknowledgements This thesis has behind it the advice, suggestions and help of many people. First and foremost, I am very grateful to my thesis supervisor professor David G . Michelson. He has been a continuous source of guidance, support and encouragement throughout my thesis project, providing me with knowledge on both theoretical and experimental aspects of my work. I'd also like to thank professor Victor Leung and professor Vikram Krishnamurthy for their insightful advice to my work. Many thanks to the helpful support from many colleagues at U B C and from industry: Micheal Weatherby (for helping me get familiar with the project at the beginning); Adnan Seddighi, Murtaza, Payam and Arnel Lim (for helping me on field data collection); Rob Calis at Inevitable Technology and Bruce Arnsbarger at O R B C O M M (for offering data collection hardware, software and useful suggestions). Chris Hynes, Nima Mahanfar and Chengyu Wang, my good friends and colleagues. From them, I got many useful ideas and suggestions on my thesis work, paper writing and presentations. And I would never forget the fun time we spent together to discuss music, sports and culture issues, etc., from which I broadened my view to the world. Oh, as well as drinking beer and whiskey together! The final best thanks go to my family. I'd like to thank my parents M a Dafang and Wang Ling, and my brother Zheren Ma. They are always standing behind me and supporting me hundred percent, whenever and wherever. Kristy, my love and best friend, thank you for everything! C h a p t e r 1 i I n t r o d u c t i o n 1.1 Statement of Problem In this thesis, we contribute to the effective use of Land Mobile Satellite Systems (LMSS) in urban and suburban environments by developing a propagation simulation tool that al-lows us to assess the effects of building blockage, terminal antenna radiation pattern and carrier frequency on system coverage. Our comparisons of system performance with differ-ent terminal antenna patterns in different urban and suburban environments at different frequencies are useful to both system engineers and antenna designers. 1.2 Background and Motivation During the past 15 years, various land mobile satellite systems (LMSS) that use small satel-lites deployed in low earth orbit (LEO) to provide simple messaging, asset tracking, meter reading and paging services have been proposed. Such "little L E O " systems include those proposed by O R B C O M M , LeoOne, E - S A T , Final Analysis, Courier, and V I T A . Because their bandwidth requirements were low, and in order to take advantage of cost reductions that are possible when operating at lower frequencies, they were designed to operate in fre-quency bands at V H F and U H F . To date, O R B C O M M (Downlink: 137-138 MHz, Uplink: 149-150 MHz) is the only such system to be deployed. Development of the O R B C O M M LMSS began in 1992; the system was put into service in 1996. It is designed to provide non-realtime text messaging and data communication services worldwide. A n artist's conception of the satellite constellation is shown in Figure 1.1. There are currently thirty O R B C O M M satellites in service; they orbit in planes A , B, C , D, F and G. The primary planes (A, B, C and D) have an altitude of about 825 km and an orbital Chapter 1. Introduction 2 inclination of 45°; each contains eight satellites. The secondary planes include the G plane (107° inclination, altitude 780 km, 1 satellite), the F plane (70° inclination, altitude 780 km, 1 satellite), and the E plane (being planned, 0° inclination, altitude to be determined, 8 satellites). When O R B C O M M began operation, it had two major groups of users. One group includes oil, mining, and other companies that use O R B C O M M ' s service to monitor and remotely control their facilities in distant areas. The other includes transportation and trucking companies that use O R B C O M M ' s service to track and locate their cargoes and fleets. Both groups operate in propagation environments that are close to ideal. Fixed operators have the option of placing the O R B C O M M terminal antenna in optimal locations that are generally free from blockage. Fleet operators operate their O R B C O M M terminals on open highways that normally have fairly unobstructed views of the sky. However, as more and more user terminals are operated in urban and suburban environments, building blockage is becoming an increasingly significant impairment to satellite signal reception and system coverage. Over the past forty years, many earth-space propagation and channel modeling studies have been conducted by various researchers. The vast majority were conducted at fre-quencies above 1 GHz and above; there is little measurement or simulation data available for V H F band systems. This gap in previous work limits our ability to assess or predict the manner in which O R B C O M M coverage degrades in urban and suburban environments. Furthermore, signal blockage and other effects may dramatically affect the coverage pro-vided by antennas with different patterns when they are operated in urban and suburban environments rather than free space. A software package that allows the designer to assess these effects and compare the performance of different antennas would also be helpful. 1.3 Objective and Approach This study seeks to answer the following questions: 1. How does the wavelength influence system coverage and availability under different degree of building blockage? Chapter 1. Introduction 3 G Figure 1.1: Constellation of O R B C O M M system 2. What is the quantitative relationship between degree of building blockage and system coverage? 3. How does the system coverage and availability vary with satellite user location (lati-tude and longitude) in urban environments? 4. How does the terminal antenna pattern affect the system coverage and availability? During the course of this project, we have implemented and validated a physical-statistical 3-D earth-space propagation simulation tool that allows us to efficiently determine how wavelength and building blockage jointly affect Land Mobile Satellite System (LMSS) system coverage. It is based upon N E C - B S C , a well-supported and widely used UTD-based numerical electromagnetics code that was developed at the Ohio State University. We have used N E C - B S C to compute path loss for particular building and path geometries in order to provide reasonable accuracy and ease of use while saving us the time and effort required to develop a custom physical diffraction code. Our simulations use a simple geometric model of a typical street canyon in an urban environment. During a run, the dimensional parameters of this street canyon are randomly chosen from distributions that are represen-Chapter 1. Introduction 4 tative of actual urban environments in order to simulate a wide range of locations. We have used SatTrack, a satellite orbit prediction tool developed at the University of California -Berkeley to predict the orbital position of O R B C O M M satellites during the simulation. The inputs to our simulation tool include: • statistical descriptions of the physical environment, including building height and street width distributions • the simulation geometry, a simplified description of an urban area • the terminal antenna radiation pattern • O R B C O M M orbital elements. The outputs are signal strength statistics, including • the probability that signal strength is higher than certain threshold • the mean duration that signal strength is constantly higher than certain threshold • the mean signal strength under different degree of buildup, etc. These data can be directly applied to system engineering purposes, e.g., predicting coverage probability and system availability, calculating shadowing loss margin in link budget design, and so forth. In order to verify that N E C - B S C is a suitable tool for predicting building diffraction, a field data collection kit was developed and several sets of field measurements were collected at selected locations in and around Vancouver. The model was verified in two ways. In the first method, we compared the statistics of measurement and prediction and visually checked the fit between them. In the second method, we used the Feature Selective Validation method. It gives a point by point comparison between simulation and measurement, and provides a quantitative measure of the goodness of fit. Coverage achieved in different, urban environments are compared in this study: (1) suburban - 1-3 storey residential areas, (2) light urban - 4-7 storey business and residential areas, and (3) heavy urban - densely built areas with buildings greater than 7 storeys. Chapter 1. Introduction 5 Coverage achieved using two different terminal antenna patterns are also compared in this study: (1) a hemispherical pattern approximating the pattern of many low-profile antennas and (2) the pattern of a vertically polarized A/4 monopole antenna. 1.4 Thesis Outline This thesis is organized as follows: • In Chapter 2, we put this study in context by surveying the relevant literature and identifying the limitations of previous work that we seek to overcome. • In Chapter 3, we describe our development and validation of our tool for simulating earth-space propagation in suburban and urban environments. A set of Perl and M A T L A B scripts developed by us integrates the functionality of Bester's SatTrack satellite orbit prediction tool and Ohio State University's N E C - B S C electromagnetic scattering code to yield a simple yet effective research tool. • In Chapter 4, we present results produced using our simulation package that show the manner in which the coverage of an O R B C O M M - t y p e system in suburban and urban environments is affected by system parameters including carrier frequency, degree of buildup, latitude of the service area, and the form of the terminal antenna pattern. Each section begins with a summary of the theoretical issues followed by presentation of the simulation results. Significant trends are identified and discussed. • In Chapter 5, we present the principal conclusions of this work and offer recommen-dations for further work. C h a p t e r 2 6 L i t e r a t u r e R e v i e w 2.1 Introduction Propagation and channel modeling for earth-space communications to and from low earth orbit (LEO) is very different than for land mobile communications. First, the slant path is time varying for L E O system and the free space pathloss of L E O system covers a wide range as the satellite rises from the horizon to the zenith. Secondly, due to the much longer distance between the satellite and the terminal antenna, the link budget for an earth-space communications link is much more challenging than that for a land mobile communications link. In most case, successful satellite communications relies upon line-of-sight propagation paths. In contrast, land mobile links are almost always obstructed. Satellite communication links normally have much higher elevation angles, usually much higher than 5-10 degrees. When the satellite signal is blocked, the obstacles causing the blockage are very close to receiver. However in land mobile case, the influence of terrain and structures along the whole propagation path contribute to the path loss. For a L E O system, the Doppler effect is mainly due to the rapid motion of the satellite. Although very signficant, it can be accurately predicted and compensated for. For land mobile systems, the Doppler effect is caused by the motion of the mobile user relative to the base station and nearby scatterers. In this chapter, we briefly review some of more significant land mobile satellite propaga-tion and channel modeling activities that have been reported in the literature. In Section 2.2, we review studies of blockage effects in suburban and urban environments. In Section 2.3, studies of building penetration loss at V H F are described. Finally, in Section 2.4, the limitations of previous research results are identified. Chapter 2. Literature Review 7 2.2 Satelli te Signal Blockage M o d e l i n g The majority of earth-space propagation studies have been conducted at U H F , L , S and higher frequency bands. Based upon measurement data collected during the course of these studies, several satellite channel models have been proposed which characterize the first1 and/or the second2 order statistics of the satellite signal. 2.2.1 E m p i r i c a l M o d e l - E R S M o d e l The Empirical Road Side (ERS) model, which is described in I T U - R Recommendation P.681-3 [2], is a typical empirical LMSS channel model. A set of empirical formulas that were derived from a large number of field measurements, it gives fairly accurate estimation of attenuation caused by roadside trees. The first measurements were collected in the UHF(870 MHz) and L(1.5 GHz) bands in Maryland (USA) [3] [4]. At 1.5 GHz, the model is expressed as: L(p, 9) = -(3.44 + 0.09750 - O.OO202) • ln(p) - 0.4436 + 34.76 (2.1) where L(p, 9) is the fade depth (dB) exceeds for p percentage of the distance or time, at an elevation angle 9 (degree) to satellite. 9 is from 20 to 60 degree and p is between 1% and 20%. Based on further measurement data [5] [6], Equation 2.1 is extended to other settings: . For / is from 800 M H z to 20 GHz, 20° < 9 < 60° and 1% < p < 20%, the loss can be calculated as: L{p, / ) = LUL) • exp{1.5 • {(J-)05 - (jf5}} (2.2) where L(fi) is the loss at 1.5 GHz (Equation 2.1) and fr, — 1.5 GHz. • For 20% < p < 80%, the loss can be expressed as: L ( p , 0 , / ) = L ( 2 O % , 0 , / ) . ^ ^ (2.3) 'First order statistics: Signal envelope statistics which can facilitate fade margin design. 2Second order statistics: Level Crossing Rate (LCR) and Average Fade Duration (AFD) which can facilitate modem and error correction scheme design. Chapter 2. Literature Review 8 • For 7° < 8 < 20° , the fade distribution is assumed to have the same value as at 6 = 20° , L(p,6,f) = L(p, 20°, f) (2.4) 2.2.2 S t a t i s t i c a l M o d e l - L o o M o d e l In Canada, measurements collected in the U H F (870 MHz) and L (1.542 GHz) bands were reported by Butterworth [7] [8]. These data specifically reflect the shadowing caused by roadside trees. Based on these measurements, Loo [9] developed a statistical channel model to characterize both the first and second order statistics of the satellite channel at U H F and L band in rural areas. In Loo's channel model, the received signal consists of two parts: (1) a shadowed coherent component that is log-normally distributed and (2) a diffusive component that is Rayleigh distributed. Following [9], it is expressed as: r • exp(j0) = z • exp(j^o) + w • exp(j^) (2.5) where r and 0 are channel envelope and phase respectively, z is log-normal distributed amplitude of coherent part, w is Rayleigh distributed amplitude of diffusive part, and both <f>o and 4> axe uniformly distributed phase. The probability density function (p.d.f.) of the signal envelope r is expressed as: p{r)=bjmJ0 ^ e x p [ — 2 * ^r]-hVdz (2-6) where do and /x are the variance and mean, respectively, of the log-normally distributed coherent amplitude, bo is the average power of the diffusive component, and 7n(-) is a Bessel function of zeroth order. The mathematical expressions of L C R and A F D are given in [9]. When applying this model, proper values of do, p and 6o for the specific frequency and environment are selected in order to complete Equation 2.6. Chapter 2. Literature Review 9 Some Variants Several statistical models based on the Loo model have been developed, including the Corazza model [10] and the Hwang model [11]. They both model the coherent component as log-normally distributed (as does the Loo model), but model the diffusive component in different ways. In Corazza's model [10], the diffusive component experiences the same log-normal shadowing effect as coherent component does, hence the correlation between the two is 1. In Hwang's model [11], that correlation is 0, which means that two components experience uncorrelated shadow fading. 2.2.3 Two-State Markov Model - Lutz Model In Germany, researchers at the German Aerospace Research ( D F V L R ) collected several series of channel recordings of L band (1.5 GHz) signals from the M A R E C S geostationary satellite. By collecting data in different European cities, they were able to acquire measure-ments at different elevation angles, such as 13° at Stockholm, 18° at Copenhagen etc. Based upon these measurements, Lutz et al. [12] proposed a two-state discrete Markov model. This model characterizes the channel as in the Good State when signal is unshadowed and as in the Bad State when the signal is shadowed by obstacles. The model reflects the ex-perimental observation that L O S and N L O S propagation in urban and suburban areas have very different statistics. In [12], the p.d.f. of the total received signal power r is mathematically expressed as p(r) = (1 - A) • pgood(r) + A • Pbad(r) (2.7) in which A is the time share of shadowing. pg0od{r) l s a Rician process, Pgood(r) = c • exp(-c(r + 1)) • I0(2c-Jr) . (2.8) It represents multipath signals superimposed upon a signal from the direct path, c is the ratio of direct-to-multipath signal power (Rician factor) and IQ(-) is the Bessel function of zeroth order. Chapter 2. Literature Review 10 Pbad(^) is given by Equation 2.9. It models the bad state as a complex Rayleigh process (Equation 2.10) which accounts for a large multipath signal component. It has a log-normallly distributed mean value ro (Equation 2.11), which accounts for the variation of the mean power of multipath signals caused by shadow fading. /•oo PbadM = / Prayleigh(r|ro)piognormal(ro)*-0 (2.9) JO 1 r Prayleigh(r|r0) = — exp( ) (2.10) P l o g ™ l ( r o ) = T ^ h T T o • * • e x p [ 2 ^ ] ( 2 - n ) Here p is the mean power level expressed in dB and a1 is the variance of the mean power level due to shadowing. The determination of the time share of shadowing A is closely related to the two transi-tion probabilities, namely the good-to-bad probability P g b and the bad-to-good probability Pb g . If Pgb and Pbg are given, the mean duration that the channel stays in the good state Dgood and the mean duration that the channel stays in the bad state £>bad are given by £good - 1/Pgb (2-12) ^bad = 1/Pbg (2-13) •Dgood and Dbad can also be derived from field measurements. After .Dgood and £>bad; have been determined, A is given by -Dbad + -Dgood Then, the probability that the channel stays in the good or bad state for a period of more Chapter 2. Literature Review 11 than n bits in duration can be derived as gg — (2.15) Pbb = 1 - Pb, (2.16) P g ( > n) = PI gg (2.17) Pb(> n) = P b " b (2.18) When applying this model for different satellite elevations, different types of environ-ments and different antennas, the parameters A, c, fi and a must be determined from field measurement statistics, e.g.,, as reported in [12]. Some Variants Several variants of this two-state model have been developed. The three-state model pro-posed by Karasawa et al. [13] improves the sharp transition between L O S and N L O S case. This can more accurately describe the second order statistics of the channel. 2.2.4 Physical-Statistical Model The physical-statistical channel model is a hybrid approach that combines the best at-tributes of the physical and statistical modeling approaches. It utilizes electromagnetic theory, e.g., the Uniform Theory of Diffraction (UTD), to determine path loss over a statistically valid distribution of building heights and geometries. It yields a statistical distribution of path loss values that are valid for a certain type of region, but not for a spe-cific location. Since signal propagation study for LMSS is statistical in nature, the output of a physical-statistical model can provide adequate information for practical engineering applications. The physical-statistical approach was first proposed by G . Butt et al. from the University of Surrey. They collected measurement data in England at L band (1.3 GHz), S band (2.45 GHz) and K u band (10.368 GHz) [14]. Saunders et al. [15] modelled satellite signal shadowing probability by using a statistical description of building height and geometrical calculation of blockage and compared to the results reported by Lutz et al. in [12]. Chapter 2. Literature Review 12 C. Oestges [16] [17] formulated the prediction problem as F(x) = J f(x\ri)-TN(V)dri (2.19) where: (1) F(x) is a function of the parameter x, x is any parameter describing the narrow/wide-band behavior of the satellite channel, i.e., signal strength; (2) rj is the vector of physical parameters describing the propagation environment; (3) T/v is the measured joint p.d.f. of the physical parameters; and (4) f(x\n) is the conditional p.d.f. of the channel parameter x conditioned on physical parameter vector rj. When predicting the first order statistics of satellite channel, the field signal amplitude r is assumed to follow Rician distribution, e.g., where c{rj) is the power of the dominant component and <J2(rj) is the average power of the multipath components. The physical parameters that are considered in the ray-tracing program include: 4> - azimuth angle of the satellite, between 0 and 27r; the corresponding p.d.f. T$(0) is orbit dependent. 8 - elevation angle of the satellite, between 0 and 7r/2; the corresponding p.d.f. TQ(8) is orbit dependent. w - street width, the corresponding p.d.f. Tw(w) follows a log-normal distribution. dm - perpendicular distance from the mobile to the building face, the corresponding p.d.f. isTDm{dm). hi, - building height, the corresponding p.d.f. THb{hb) follows a log-normal distribution. These parameters are assumed to be independent of each other. Replacing r) in Equa-tion 2.20 with the above parameters and applying Equation 2.19 to Equation 2.20, the field Chapter 2. Literature Review 13 signal amplitude p.d.f. is TR(T)= / / / / TR\HbDmW^{r\hbdmw4>B)-T9{4>) Jo Jo Jo Jo Jo • Te(9) • Tw{w) • TDm(dm) • THb{hb) • d{hb)d(w)d(dm)d4>d9 (2.21) in which TR\HBDmWie{r\hbdmW(f)d) = rc(hb,dm,w,<j),6) a2{hb,dm,w,(j),e) r • exp[-r 2 + c2{hb,dm,w,(f>,9) 2a2(hb,dm,w,4>,6) ) (2.22) The parameters a2{hb,dm,w,(j),9) and c(hb,dm,w,<fi,9) are estimated by a ray-tracing program [16]. In the L O S case, a2(hb,dm,w,(j>,9) is the power of the direct components. If non-LOS, it is the power of the diffracted rays from the nearest roof edges. c(hb, dm, w, 4>, 9) is the power contributed by all other reflected and diffracted rays. Given the measured statistical distributions of physical parameters, the physical param-eter generator produces simulation environmental settings. U T D based ray-tracing is then used to calculate the time series signal envelope. At V H F band, there is very limited (published) research results concerning building pene-tration by V H F satellite signals. Two recent ones are summarized in the following sections. 2.3.1 Building Penetration Measurement at 137 MHz A measurement campaign was conducted by R. Zabela et al. [18] at 137 MHz in 1992. It gave a quantitative comparison between the signal strength of outdoor reception (LOS) and that of indoor reception (Non-LOS). The strength of signals from N O A A - 9 3 and N O A A -11 (orbital altitude of about 830 km) were measured using a pair of 5/8 wavelength whip antennas located in indoor and outdoor locations, respectively. A receiver switched between 3The transmitter of NOAA-9 failed on December 18, 1997 and it was permanently deactivated on February 13, 1998. 2.3 Building Penetration by V H F Satellite Signals Chapter 2. Literature Review 14 the indoor and outdoor antennas every two seconds, and a computer automatically recorded the average power level during each two-second interval. For each satellite pass, a series of attenuation value was calculated and formulated as a function of satellite elevation angle. 2.3.2 Measurement of Indoor Attenuation at 144 MHz A measurement campaign was conducted by B. Benzair et al. [19] at 144 M H z in 1991. Its purpose was to assess the engineering feasibility of a local building paging transponder scheme for both satellite and terrestrial applications. In the study, a transmitter was placed on the roof of the building. It transmits horizontally polarized C W signal at 144 MHz with a half wavelength dipole antenna. The receiver unit is an Anritsu (ML 518A) field strength meter connected to a calibrated half wavelength dipole. It was mounted on a trolley 1.3 metres above the floor. The received signal strength was measured in an eleven-floor building. The path loss equation was formulated as a function of distance and number of floors penetrated as PathLoSS = I-FrcoSpace + L(p) [dB] (2.23) Hp) = 20 • log 1 0(p 2) + 4 [dB] (2.24) where p is the number of floors that the signal penetrates. 2.4 Limitations of Previous Work From our review of previous studies, the following limitations or gaps are apparent: 1. Different Frequency Compared with 900 M H z and above frequencies, V H F band propagation has very different propagation properties. Previous satellite channel modeling results (reviewed in Section 2.2) are at U H F , L and higher frequency bands. Because the O R B C O M M system operates at V H F , most previous work does not apply to our study. 2. N o explicit assessment on the influence of t ermina l antenna radiat ion pat-tern on performance Chapter 2. Literature Review 15 In the urban environment, obstructions like buildings make the propagation paths more complex and significently influence the angle of arrival distribution of incoming rays. The antenna radiation pattern that maximizes received power varies with the angle of arrival distribution. Thus, terminal antenna pattern design must take the local environment into consideration. However in previous studies, the influence of antenna pattern on performance in different environments was not explicitly assessed. 3. N o explicit s tudy on the variation of performance due to the variat ion of user locat ion (latitude and longitude) joint ly w i th bui ld ing blockage For a satellite communications link, blockage is affected by the elevation and azimuth angles of the satellite. Depending upon the satellite constellation design, the distribu-tion of elevation and azimuth angles seen by users at different locations on the earth will be different. For a L E O system, the elevation and azimuth angles are time varying parameters. Because the elevation and azimuth distribution changes with user loca-tion, so does blockage and the statistics of performance. However in previous studies, there is no explicit comparison of performance at different latitude and longitude with LMSS based upon L E O satellites. 16 C h a p t e r 3 M e t h o d o l o g y 3.1 Introduction To study the effect of wavelength, building blockage and terminal antenna radiation pattern upon satellite signal propagation, we have implemented a 3-D physical-statistical earth-space propagation simulation tool. As stated in Section 2.2.4, this approach can offer ad-equate information for practical engineering application without being overly complicated. Since, developing our own numerical electromagnetical code would require considerable time and effort we have used N E C - B S C to provide the electromagnetic analysis capability. N E C - B S C is a well developed numerical electromagnetic code with proved accuracy that is available at reasonable cost. In order to validate the propagation simulation tool, several sets of field measurements were collected in Vancouver in residential areas, near shopping malls (light urban) and on the U B C campus (light urban). In order to accomplish this, data collection hardware and software were developed or modified as required. The statistics of the measured data were compared with statistics from computer simulation. Besides visual inspection on the goodness of fit between measurement data and simulation data, Feature Selective Validation was also used to give a quantitative evaluation on the accuracy of the propagation simulation tool. Validation results show that our N E C - B S C based propagation simulation tool can give reasonably accurate prediction. We developed Perl scripts to automatically generate random sets of street canyon ge-ometry data for use in the N E C - B S C simulations. Due to the large number of computer simulations, we used massively parallel computational facilities at Westgrid to run these simulations. Perl and M A T L A B scripts are developed for post data processing and statis-Chapter 3. Methodology 17 tics derivation. The functionalities of each module will be described later. The content of this chapter is organized as follows: • In Section 3.2, we describe our implementation of a simple yet effective tool for ana-lyzing earth-space propagation in suburban and urban environments. • In Section 3.3, we describe field measurements that we collected in order to validate our simulation package. • In Section 3.4, we describe the steps taken to validation the simulation model using the Feature Selective Validation method. 3.2 Simulation Model 3.2.1 S i g n a l F l o w o f P r o p a g a t i o n P r e d i c t i o n M o d e l The physical-statistical approach utilizes electromagnetic theory to analyze randomly gen-erated building scenarios according to the statistical descriptions of actual environments, e.g., heavy urban, light urban, suburban, etc. Each building scenario effective corresponds to a randomly selected location in the service area. The outputs are statistical distributions of parameters of interest for certain types of service areas but not, predictions for a specific location. The signal flow diagram is shown in Figure 3.1. The functionality of each module is as follows: Conste l la t ion S imulat ion Parameters are the input parameters of the SatTrack pro-gram. They include user location (latitude, longitude), simulation time (start time, stop time), and time resolution. The influence and selection of these parameters are addressed in Section 3.2.3. The output of SatTrack is a time series of satellite location predictions as shown in Appendix-A. The output parameters of interest include elevation, azimuth, range and free space path loss. S imulat ion G e o m e t r y in Figure 3.2 is adapted from [16]. It represents a typical street canyon in an urban environment. These four building blocks can give the most significant diffraction rays and reflection rays hence offer the dominant component of total received Chapter 3. Methodology 18 Constellation Simulation Parameter Location, Simuationl Time, Time;Stei Building.Height at Random Location Antenna Pattern Simulation Geometry Time Series Constellation Data Elevation, Azimuth, Range Link Budget Data (El'RP, ,..) 3E 31 3-D Geometry Data at Random Location I NEC-BSC (UTD Based Numerical Electromagnetic Program) 31 me.Series Prediction Data Post Data Processing Module I E Statistics Figure 3.1: Flow diagram of propagation simulation tool power under blockage. We justify this a reasonable simplification that also allows us to com-pare our results with previous results in the literature. Follow on studies with more realistic site-specific geometries could be conducted for use in practical engineering applications. Satellite Satellite ^ \ \ \ \ Satellite Building W depth <• Building depth Figure 3.2: Simulation geometry B u i l d i n g Height at a R a n d o m Locat ion For the geometry in Figure 3.2, the heights of the blocks marked with Hb follow a log-normal distribution [16]. The heights of other two building blocks are equal to the mean building height of that type of environment. The exact values for Hb and mean building heights for different environments are shown in Chapter 3. Methodology 19 Table 4.1. T e r m i n a l A n t e n n a P a t t e r n The antenna radiation pattern must be interpolated sepa-rately and incorporated into the N E C - B S C model as a radiation source. This process is described in detail in Section 3.2.4. N E C - B S C The input applied to N E C - B S C is a set of 3-D geometry data automatically generated by Perl scripts. N E C - B S C executes this input script and generates output pre-dictions. From the standard output provided by N E C - B S C , excess path loss due to building blockage is calculated. The calculation method is described in Section 3.2.4. Post D a t a Process ing M o d u l e This module first converts the N E C - B S C output raw data and SatTrack orbit predictions into excess path loss predictions. Perl scripts read the N E C - B S C output and SatTrack prediction and convert them into a format that is convenient for the M A T L A B scripts to process. Then, applying proper link budget design i.e., in [20], these excess path loss predictions over time at a variety of random locations are used to generate path loss statistics including complementary cumulative probability and good connection duration for signal strength and excess path loss. 3.2.2 Utilization of Propagation Prediction Model The time series satellite orbit predictions are a set of data, in which the most important information is the satellite's elevation angle, azimuth angle and range at different times. For each specific instant, i.e., t\, building heights are assigned to each building blocks in the simulation geometry. N E C - B S C calculates the shadowing loss l\ at time moment t\. Applying a proper link budget formulation, the received signal strength can also be predicted as r\. At another instant £2, the satellite moves to a new location in the sky, and the heights of the building blocks in the geometry change too. This simulates a new randomly selected location. A new shadowing loss I2 is calculated. When the whole simulation completes at time tn, there exists a series of shadowing loss predictions {h,l2---,ln} and signal strength predictions {n,?"2, . . . , r „ } . These predictions can be used to calculate the histogram, mean and variance of signal strength and shadowing loss. The histogram data can be converted to signal outage probability data as shown in Figure 4.1 and used by system engineers to predict the service availability. The mean and Chapter 3. Methodology 20 variance of signal strength prediction can be used in link budget design and to predict coverage probability. The shadowing loss prediction can be used to predict the degradation of signal strength in environments with building height distributions similar to those used in the simulation. 3.2.3 S a t T r a c k The Satellite Tracking Program SatTrack (V3.1) 1. It was written by Manfred Bester at the University of California - Berkeley. It is a satellite orbit prediction program that was written in the C programming language for use in the UNIX environment. The program reads the N O R A D / N A S A two-line Keplerian element ( T L E ) sets directly and uses them to run the Simplified General Perturbations Version 4 (SGP4) orbit propagation algorithm for low-earth orbit satellites. The SatTrack manual [21] provides additional details. The procedures for updating T L E data and running the prediction program are described in Appendix-B. Selection of Appropriate Time Resolution and Total Simulation Time Time series of satellite azimuth, elevation and range is one of the inputs to our propagation simulation tool. The finer the time resolution and the longer the total duration of the time series, the better we are representing the distribution of satellite locations in the sky. However, finer resolution and longer duration also leads to higher computational cost. In order to find the proper value for time resolution, we compared the statistics of elevation angle with different settings as shown in Figure 3.3. It is apparent that • 1 second, 5 seconds and 10 seconds time resolution all have very close p.d.f. • 5 days of simulation has the same statistics as 1 day of simulation Therefore, we choose 10 seconds time resolution and 1 day total simulation time as a setting which can closely represent the statistical properties of the actual orbit data without requiring excessive simulation time. 'SatTrack can be downloaded from: http://www.amsat.org/amsat-new / tools / softwareArchi ve. php#linux Chapter 3. Methodology 21 a> 4 TO c CD ££ Q) o 0 ^ * 1 second step 1 day + 5 seconds step 1 day 0 10 seconds step 1day o 5 seconds 5 days ...8 $ 1 £ & ? $ y . . $ . A . . . $ i 10 20 30 40 50 60 Elevation angle (Degree) 70 80 90 Figure 3.3: Probability density function of elevation angle for O R B C O M M constellation 3.2.4 Numerical Electromagnetic Code - Basic Scattering Code N E C - B S C was developed by the Electro-Science Laboratory at the Ohio State University. It is a user oriented code for electromagnetic analysis at high frequency. It is mainly used to analyze the antenna radiation problem in the presence of scatterers. Ray optical techniques are used to determine components of the field incident on and diffracted by various structures. Components of the diffracted fields are found using the U T D solutions in terms of the individual rays. Since U T D is a high frequency technique, the dimensions of the scatterers are normally at least a wavelength. N E C - B S C has been widely used in many applications, including modeling antennas on ships and aircraft, and has proved to be accurate [22]. In the following sections, some key issues in utilizing N E C - B S C for prediction will be discussed. Chapter 3. Methodology 22 How to Calculate Excess Path Loss Due to Building Blockage Given the great distance (at least several hundred kilometres) between the satellite and the user terminal, it is reasonable to assume that the incoming waves seen by users are plane waves. We define the excess path loss LEXCCSS(#J <t>) due to building blockage as: -£<Excess(#, 4>) = -PUndcrBlockage(#, <f>) - -PNoBlockage(#, 4>) [dB] (3.1) where 9 and <j> are elevation and azimuth of the satellite. PunderBlockage(^) <A) is the received power under blockage in dBm, and -PNoBiockage(#, 4>) i s the received power without blockage in dBm. At the same frequency and through the same propagation path, both cases have the same free space propagation loss and atmospheric attenuation. The difference, L^xcess(9,4>) gives the excess path loss in dB due to building blockage. By the Reciprocity Theorem For Antennas, the receiving problem can be converted to a transmitting problem. Now, let the user terminal antenna transmit at the same frequency. When the satellite is observed in the direction (9, 0), excess path loss LEXCCSS(0, 4>) is written as £ Excess (#,</>) = Pund°irB?ockage(^)</') ~ PNoB\ocL.ge(® > <t>) [d B] (3-2) where PfjnderBlockage^' 0) l s t n e r e c e i v e d power at satellite under blockage in dBm, and -^NoBiockage( '^ i S * n e P o w e r received without blockage in dBm. I f pUnderBl>ckage(^^) a n d ^ f f i & g o ( M ) are normalized by the total input power at transmitting antenna input port, it is apparent from Equation 3.3 that the calculation of LEXCCSS(^I <t>) is not dependent on the absolute value of the power at receiving antenna but the relative difference. LExcoesO?, 0) = ^ U n T r l t & g c ^ 4>) - ^ ( « , <P) [dB] (3.3) Chapter 3. Methodology 23 in which r>Satellite IQ X\ pNormalized in A\ — UnderBlockagel ^> f r . ,N MJnderBlockagel"' Q) — "77 -MVormalizationFactor pSatellite (fi j~\ nNormalizcd/fl i\ _ NoBlockageV17' / q rNoBlockagcl6'' 9) ~ p \ 6 - ° > -'NormalizationFactor Assuming an isotropic antenna pattern on the satellite, we can utilize far field pattern calculation in N E C - B S C to calculate LExcess4>) 3 5 follows. The N E C - B S C command P F calculates the far zone radiation pattern for a specified radiation source and the command P R specifies PNormalizationFactor to normalize the results of P F . The power gain G (in dB) at direction (0,(j>) is one of the outputs of P F and is calculated as G - p — ^ m "Normal izationFactor where U is the total radiation intensity. Since we assume that the antenna pattern on the satellite is isotropic, we have ^UndCTBbckage^' 0) = 4 7 T • ^UndtrBlockage ( 3- 7) ^NoBlockageC^i 0) = 4 7 T • C/NOBlockage (3-8) ^UndcrBiockagc(^' 0) = ^UnderBlockage (3-9) ^NoBbckago(^i0) = GlVoBlockage (3.10) Therefore, we can directly calculate I/Excess(#, 4>) from N E C - B S C standard output as ^Excess(#, 41) = GlJnderBlockage _ ^NoBlockage [dB] (3-H) Interpolat ion of the A n t e n n a P a t t e r n In this thesis project, the performance that can be achieved with a hemispherical pattern and a A/4 monopole antenna pattern are compared. We approximate the hemispherical pattern in the 0 plane as E(0) = 2cos6>, 9 6 [0, n/2] as shown in Figure 3.4. It is omni-Chapter 3. Methodology 24 180 180 Figure 3.4: Hemispherical radiation pattern Figure 3.5: Radiation pattern of A/4 approximating L P R antenna monopole antenna directional in the horizontal (<p) plane. The A/4 monopole antenna is the most popular antenna used in cellular applications. Its pattern is given by E(9) = 2cos( | • cos#)/sine?, 9 £ (0, 7r/2] as shown in Figure 3.5. It is also omni-directional in the horizontal (</>) plane. In N E C - B S C , the radiation source can be specified as an interpolated radiation pattern using the command SI, as described in [22]. 3.3 Field Measurement In order to validate the use of N E C - B S C to predict diffraction by buildings, satellite signal strength data were collected at three different locations in Vancouver using an O R B C O M M subscriber communicator. Our data collection program reads these measurement data and stores them on the PC's hard disk. During the data collection, we made some modifications to improve the dynamic range of the receiver. We also encountered an interference problem which severely degrades the satellite signal reception. In this section, our data collection hardware and software are described. The problems that we encountered and the solutions that we devised are discussed. Chapter 3. Methodology 25 3.3.1 D a t a Collection K i t - Hardware and Software Data Collection Hardware Our data collection hardware includes: 1. L P R antenna (Figure 3.7) 2. Low Noise Amplifier (Model: ZFL-1000LN from Minicircuit. Figure 3.8) 3. O R B C O M M Subscriber Communicator (SC) (Model: KX-G7101, S /N: 8GBDB202657. Figure 3.9) 4. D C Power supply for L N A (Model: I N S T E K lab D C power supply) 5. Laptop computer installed with data collection program ORB Perform 6. Cables and connectors (Insertion loss for both cables are less than 1 dB at 138 MHz) The connection digram is shown as Figure 3.6. The laptop computer is connected with SC through RS232 serial port. Subs'criber, 'Cbm'municatSr" LPR-Antenna Mihi-Gircuits Low Noise-Amplifier, Figure 3.6: Connection diagram ;'<Qfb R'ferfpr'm Data Data Collection Software Two data collection programs were used. One is ORBPerform, a Windows application developed by O R B C O M M . The other is custom C program developed by U B C students using the Software Development Kit (SDK) for the SC. • ORBPerform ORBPerform supports global messaging via the O R B C O M M satellite system, and Chapter 3. Methodology 26 Figure 3.7: L P R antenna Figure 3.8: L N A Figure 3.9: SC runs performance test and V H F GPS antenna test functions. It accesses the OR-B C O M M communication network via a Subscriber Communicator modem that has been installed, provisioned and configured for network operation. For our data collec-tion purpose, we utilize the Antenna Test function to log the RSSI, Satellite ID and Time information [23]. • C u s t o m i z e d D a t a Col lect ion P r o g r a m Utilizing the S D K for SC, we developed a data collection program which allows the SC to log RSSI, satellite ID and time information without an external P C . These data can be retrieved later via the SC's serial port. Eliminating the external P C essentially eliminates the electromagnetic interference that it causes to the SC. The source code for this program is given in Appendix-C. 3.3.2 P r o b l e m s w i t h D a t a C o l l e c t i o n K i t a n d F u r t h e r D e v e l o p m e n t P r o b l e m 1 - Interference The first data collection was conducted in the roof of the MacLeod building (MCLD) at U B C . The O R B C O M M antenna was set outside on the roof. The area is open and no building nearby is much higher than Macleod building. Surprisingly, over a 24 hours' data collection, no connection was established between the SC and the satellite. As a result, no useful data was collected. We carefully checked all the parameter settings on SC and hardware connections and were assured that all are correct. We did data collection again, however SC still couldn't establish a connection with satellite. Suspecting interferences, a Chapter 3. Methodology 27 spectrum scan was conducted in and around the O R B C O M M uplink and downlink. A portable spectrum analyzer (Anristu MS2711D) is connected with O R B C O M M an-tenna with the same coaxial cable we used for data collection. The resolution bandwidth (RBW) of the spectrum analyzer was set to 25 K H z which is the same as the O R B C O M M downlink channel bandwidth (25 KHz) [20]. The video bandwidth (VBW) was set iden-tically to the R B W . The results of spectrum scan are shown in Table 3.1. It is apparent Table 3.1: Spectrum scan at 2 m wavelength band on Macleod building roof Aviation Band 117.975-137 M H z O R B C O M M D L 137-138 MHz Land Mobile 138-144 MHz Amateur Radio 144-148 MHz Clean, noise floor is about -92 dBm Clean, noise floor is about -92 dBm Strong signal from paging service. Such as 139.21 MHz(-65.16 dBm) 140.21 MHz(-46.22 dBm) Detailed channel plan is unknown. Interference appears occasionally. Spike is at 140.21 MHz (-84.77 dBm) that there exist strong signals from the land mobile band (paging service) that are more than 40 dB stronger than the satellite signal. It is possible that such a strong nearby signal will degrade the performance of the O R B C O M M SC through desensitization of the receiver and intermodulation interference. On the roof of the Macleod building, the paging signal can reach the antenna of SC through a distance of just several hundred metres of essen-tially line-of-sight propagation. To avoid the interference, locations with less interference from paging signals were chosen for better reception. A description of them is given in Section 3.3.3. P r o b l e m 2 - Too W e a k S igna l S t r eng th In Vancouver, the elevation angles of O R B C O M M satellites are lower, and the ranges be-tween satellite and SC are much longer than at low latitude locations. As a result, the satellite signal strength measurements that we collected are very weak. Figure 3.10 shows time series RSSI data collected at, a sports field at U B C . The SC's sensitivity is specified as -118 dBm with B E R at 10~5 [20]. Figure 3.10 shows both RSSI and satellites in view over time. Only for very limited time periods is the signal strength above the receiver sensitivity level. Due to the low RSSI, the SC cannot establish connection with the satellite for most Chapter 3. Methodology 28 of the data collection period so very little of the data is useful. The low RSSI also limits the range of signal strength that can be used to characterize the influence of building blockage. -80 E m w w or -90 h -100 -110 -120 -130 -140 . . . . It : W ; % Ll A — Measurement * Satellite in view 300 400 500 Time (x10 seconds) 800 Figure 3.10: RSSI in sports field without L N A Modification to Data Collection Hardware In order to overcome the two problems described above, a low noise amplifier (LNA) was added to the SC front end. The L N A used is ZFL-1000LN from Mini-Circuits (see Fig-ure 3.8). Its working bandwidth is 0.1 - 1000 MHz. With 15 Volt D C power supply, it can provide about 23 dB gain over 137-138 MHz frequency band. The noise figure is specified as 2.9 dB. For detailed specification, please go to www.minicircuits.com/model and search for ZFL-1000LN. A preselector was proposed to reduce interference. The filter proposed is a 2C137-1.5-Chapter 3. Methodology 29 4AA from Lark Engineering. It can provide more than 40 dB attenuation at frequency higher than 139 MHz, which is enough to alleviate the influence of paging band signals. However, we did not use it for our data collection for two reasons: (1) very high price for low quantity order (2) data collection can be conducted without this filter if a low interference site is selected for data collection. With the L N A , the final setup of data collection hardware is shown in Figure 3.6. The satellite-to-subscriber link budget design is show as Table 3.2. Table 3.2: Satellite to subscriber link budget at minimum elevation angle and edge coverage Satellite Altitude 825 km User Elevation Angle 5 Deg User Data Rate 4800 Bps Downlink Frequency 137.5 M H z Transmit EIRP 12.0 dBw Spreading Loss -140.1 d B m 2 Atmospheric Losses 2 dB Polarization Losses 4.1 (SC 2 dB axial ratio, subscriber linear) dB Multipath Fade Losses 5.0 dB Satellite Pointing Loss 0.3 (5 degree off-nadir pointing) dB Area of an Isotrope -4.2 dBm' 2 Power at User Antenna -143.7 dBw Subscriber antenna G / T -28.6 d B / K L N A gain 23 dB L N A noise figure -2.9 dB Received C/N0 77.4 dB Data rate 36.8 (4.8 kbps) d B H Received Eb/No 40.6 dB Idea Eb/N0 10.3 ( K T 5 B E R ) dB 3.3.3 M e a s u r e m e n t L o c a t i o n S e l e c t i o n Three sets of valid measurements were collected at three locations in Vancouver. See Ta-. ble 3.3 for details. A n abstraction of the Richmond Centre and Marpole Residence sites are shown in Fig-ures 3.11 and 3.12, respectively. They are used in computer simulation to validate the propagation simulation tool against measurement. Chapter 3. Methodology 30 Table 3,3: Field measurement location description Location Date Time Terrain Typical Building Height Forestry Building(UBC) Aug-30, 2004 15:27 - 17:00 Light Urban 20 m Marpole Residence(Van) Sep-03, 2004 11:52 - 15:39 Suburban 10 m Richmond Center(Van) Sep-09, 2004 10:24 - 14:46 Light Urban 20 m Figure 3.11: Richmond centre geometry Figure 3.12: Marpole residence geometry 3 . 4 V a l i d a t i o n o f C o m p u t e r S i m u l a t i o n M o d e l 3.4.1 P u r p o s e o f V a l i d a t i o n Figure 3.13 and Figure 3.14 are time series excess path loss data derived from measurements collected at Richmond Centre over both long and short durations. From the short duration observation (Figure 3.14), it is apparent that the general trends of signal strength variations fit well. Both measurement and prediction have the same transitions at the same time moments, though the amplitudes are different. The differences in amplitude is because that in our computer simulation model, the influences of nearby moving objects and structures (I.e., electric poles) other than buildings are not considered currently. Comparing a great amount of simulation and measurement results only through visual inspection is not practical, especially since there exist various discrepancies. We applied the Feature Selective Validation (FSV) technique in order to give a quantitative evaluation on the goodness of fit between the amplitude envelope of simulation and measurement. Chapter 3. Methodology 31 3.4.2 F e a t u r e S e l e c t i v e V a l i d a t i o n The F S V technique was developed by the need for error determination in the validation of numerical models against experimental data. The basic approach of the F S V technique is to decompose the original two sets of data under comparison into only two component measures, and then recombine the two component measures to provide a global goodness of fit measure. The components used are the Amplitude Difference Measure (ADM), which compares the amplitudes and 'trends' of the two data sets, and the Feature Difference Measure (FDM), which compares the rapid changing features. The A D M and F D M are them combined to form a Global Difference Measure (GDM). The A D M , F D M and G D M are usable as point-to-point analysis tools or as a single overall measurement. Following [1], the A D M and F D M are obtained using the following equations. A D M = Y ^ l o w l ~ I l o w 2 \ (3.12) Jmin fmax F D M = 2 £ [FDi{f) + FD2(f) + FD3(f)} (3.13) fmin \f — f I FDi(f) = (3.14) UFDl \f _ f I FD2(f) = h l 9 h l h l 9 (3.15) apD2 \l" -1" I FDz(f) = h % 9 h l high2\ ^ ^ &FD3 in which 1 — fmax 1 , • £ + IW/)ll (3-17) Jmax Jmin f J min frnax aFDi = • £ 0 C i ( / ) l + lC2(/)ll (3-18) Jmax Jmin j. fmax « F 0 2 = ~z _ , • £ [\Ihighl(f) \ + l4flfc2(/)0 (3-19) Jmax Jmin f Jmin CXFD3 = , 7 l 2 f • £ [\Cghdf)\ + \C9h2(f)\} (3-20) Jmax Jmin f Jmin Chapter 3. Methodology 32 howl and IiOW2 are the low pass component of data sets 1 and 2. The subscript low refers to the low frequency components of the data sets. This is obtained by Fourier transforming the data and inverse transforming the lowest 25% of the data, i.e., the range of frequencies 0 < / < /s/8, where / s is the sampling frequency. CCADI is an amplitude normalization factor. Ihigh is the high pass component of the data sets, obtained by Fourier transforming the data sets and inverse transforming the highest 60%. The single prime (') indicates the first derivative of the inverse Fourier transformed data sets with respect to X-axis and the double prime (") indicate the second order derivative of the inverse Fourier transformed data. The term OLPDX, otpui and apD3 are used to equalize the three parts of the F D M measures and are weighted mean intensity values. The Global Difference Measure (GDM) is then obtained as either a single figure of merit or as a point-by-point result: JmaT. G D M = \ / ( A D M ( / ) ) 2 + ( F D M ( / ) ) 2 (3.21) fmin or G D M ( / ) = v / ( A D M ( / ) ) 2 + ( F D M ( / ) ) 2 (3.22) Based upon the above mathematical formulation, the relationship between the numerical values (of A D M , F D M and G D M ) and the common description of fitness is also offered in [1] as Table 3.4. Table 3.4: F S V interpretation scale [1] F S V value A (quantitative) F S V interpretation (qualitative) A < 0.05 Ideal 0.05 < A < 0.1 Excellent 0.1 < A < 0.2 Very good 0.2 < A < 0.4 Good 0.4 < A < 0.8 Fair 0.8 < A < 1.6 Poor A > 1.6 Extremely poor Chapter 3. Methodology 33 3.4.3 V a l i d a t i o n R e s u l t s Our goal in validating the simulation tool is to compare the amplitude envelope of simulation and measurement results. There exist fluctuations on measured excess path loss as shown in Figures 3.13 and 3.14, that axe caused by changes in the environment not accounted in simulation. A D M can provide a quantitative measure between the amplitudes and 'trends' of the two data sets. Therefore, A D M is the figure of merit that we use to evaluate our propagation simulation tool. The procedure for calculating A D M for RSSI and excess path loss is as follows. First, time series simulation or measurement data are passed through a low pass filter whose magnitude response is shown as Figure 3.15. We get low pass filtered time series measure-ment or simulation. Applying Equation 3.12 and Equation 3.17, A D M values for excess path loss and RSSI are computed and shown in Table 3.5. The unit of RSSI data is in mili-watt and that of excess loss is in logaxithm scale. Note that due to the limitation of computer simulation, A D M value for excess loss is computed in logarithm scale. This leads to overly optimistic comparison results. The A D M value for RSSI prediction better reflects the accuracy of the computer model. Table 3.5: Validation results - A D M values A D M Values (quantitative) Interpretation (qualitative) Excess path loss 0.0172* Ideal RSSI 0.0724 Excellent The complementary cumulative distribution function plots of excess path loss and RSSI are also shown in Figure 3.16. Conclusion from Validation Results According to Table 3.5, the A D M value shows 'excellent' agreement between simulation results and measurement. Figure 3.16 is the C C D F of excess pass loss and gives more intuitive visualization of the agreement. From those validation results, it can be concluded that though there exist some discrepancies, our computer simulation can provide RSSI and excess path loss predictions with good accuracy. Thus, this computer simulation model is Chapter 3. Methodology 34 used in this thesis project to produce extensive RSSI and excess path loss predictions under different scenarios. Chapter 3. Methodology 35 Figure 3.13: Time series excess path loss (long duration) Chapter 3. Methodology 36 ** * * + *: * • * • * * measurement + simulation * * % * * * * * * * * * * * * * * * #^ : * * * 3.5 3.502 3.504 3.506 3.508 Time (second) 3.51 3.512 x 10 3.514 4 Figure 3.14: Time series excess path loss (short duration) Chapter 3. Methodology 37 Magnitude Response 20 I 1 1 1 1 1 1 1 r -120 -0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency (Hz) Figure 3.15: Magnitude response of low pass filter for A D M calculation Chapter 3. Methodology 38 10 u ro £ 10 TO in vi o 10 i ' 1 * — ' 1 1 * Measurement . + Simulation ..+ : * . H + + . * h . . . » . . . . . . . . It + H SI h * 1 * + i * l l l l 8 10 12 Excess path loss (dB) 14 16 18 20 Figure 3.16: Complementary cumulative distribution function of excess path loss for vali-dation 39 C h a p t e r 4 System Coverage in Suburban and Urban Environments 4.1 Introduction In the previous section, we described our development of a simulation tool for analyzing earth-space propagation in suburban and urban environments. A set of Perl and M A T L A B scripts developed by us integrates the functionality of Bester's SatTrack satellite orbit pre-diction tool and Ohio State University's N E C - B S C electromagnetic scattering code to yield a simple yet effective research tool. Here, we present results produced using this tool that show the manner in which the coverage of an O R B C O M M - t y p e system in suburban and urban environments is affected by system parameters including carrier frequency, building height distribution, latitude of the service area, and the terminal antenna pattern. Each section begins with a summary of the theoretical issues followed by presentation of the simulation results. Significant trends are identified and discussed. Such results provide useful guidance to those involved in assessing system availability in suburban and urban environments and to those involved in the development of antennas for use by O R B C O M M subscribers. The content of this chapter is organized as follows: • In Section 4.2, the influence of carrier frequency on system coverage is considered. Comparisons on statistics of mean signal strength and link availability between 138 MHz and 1.3 G H z signals are conducted under light urban and heavy urban environments. • In Section 4.3, the influence of the degree of buildup on system coverage is considered. At 138 M H z frequency, comparisons on statistics of signal strength under different Chapter 4. System Coverage in Suburban and Urban Environments 40 degree of build-up are conducted. The relationship between mean building height and mean signal strength is presented as a linear equation. • In Section 4.4, the influence of the latitude of the service area on system coverage is considered. Statistics of elevation angle is presented. Statistics of signal strength at different latitude is compared to reveal the influence of user location under O R B -C O M M constellation. • In Section 4.5, the influence of the terminal antenna pattern on system coverage is considered. Patterns of L P R and A/4 monopole antennas are used for comparison. The signal strength statistics of them under light urban and heavy urban environments is presented. 4 . 2 I n f l u e n c e o f W a v e l e n g t h 4.2.1 T h e o r e t i c a l B a c k g r o u n d Signal from a satellite can be received by user terminal through line of sight (LOS) propa-gation, reflections from ground and scatterers nearby, as well as diffraction from roof edge and building side edges. The total field at any point in space is given by Nr Nt Nd £ t o t a l = E 0 A l o s e - j k o r o + Yl REiAie-jkiri + £ TEjAje-jk^ + £ TEmAme-^Tm (4.1) j=l j=l m=l where ^4ios Ai Aj and Am are spreading factors for L O S , reflected, transmitted and diffracted rays; R, T and D are reflection, transmission and diffraction coefficients; rn is the distance along the nth ray; kn is the wave number of the nth ray. En is the incident field immediately adjacent to the corresponding transmission, reflection or diffraction point. Nr Nt and JV^ are the total numbers of rays corresponding to reflection, transmission and diffraction. Generally, a signal with a longer wavelength experiences less attenuation than one with a shorter wavelength under the same blockage. For the geometry in Figure 3.2, when LOS is blocked, signals diffracted from roof edges contribute the most to the total received power since they have the shortest propagation path and interact with scatterers only once. Chapter 4. System Coverage in Suburban and Urban Environments 41 Assuming the incoming signal is a plane wave, the total received power contributed by the first order diffractions off roof edges can be expressed as: i where E% is the incident field at diffraction point; D is diffraction coefficient; Ai is spreading factor and equals to \fd~i for plane wave; fcj is the wave number and di is the distance between the observation point and the diffraction point. For 90-degree roof edges, wedge diffraction is a very close approximation to the actual case. Then, the U T D diffraction coefficients are given in [24] as ( { c o t r + ( 2 ^ - ^ W f ^ - ^ i + c o t r - ^ - ^ ] F [ 2 7 r ^ - ( 0 - 0 O ] } T { c o t r + ^ + ^ V [ 2 ^ ( 0 + 0 O ] + c o t [ ^ ± ^ ] F [ 2 7 r ^ - ( 0 + 0')]}) where n=1.5 for 90° wedge; cp, cf)' and L are geometry dependent and seen as constant here. When observation points are away from shadowing boundary, Equation-4.3 can be simplified as Keller's diffraction coefficients as Equation-4.4: Ds,h = _ e s i n ( i ) . ^ (4.4) 1 1 cos(^) - c o s ( ^ ) c o s ( £ ) - COSi • <t>+4>' > It can be seen that as wavelength A increases, the diffraction coefficients increase and so does £ ^ o t a l when incident field E% is the same. Chapter 4. System Coverage in Suburban and Urban Environments 42 4.2.2 S i m u l a t i o n R e s u l t s a n d A n a l y s i s In order to show the influence of wavelength on coverage and service availability with the O R B C O M M constellation, computer simulations were conducted at 138 M H z and 1.3 GHz under light urban (mean building height 20.6 m) and heavy urban (mean building height 40 m) conditions. Simulation results are shown in Figures 4.1, 4.2, 4.3 and 4.4. Link Budget Consideration Figure 4.2 gives the probability density function of predicted excess path loss. Figure 4.1 gives the probability density function of predicted RSSI for both frequencies based upon the link budget in Table 3.2 and predicted excess path loss. It can be seen that 150 MHz signals experience less excess path loss hence stronger RSSI than 1.3 GHz signals, which conforms with the theoretical prediction. The mean RSSI values for 138 MHz signals in light urban and heavy urban environments are -120.7 dBm and -123.7 dBm respectively, while the mean RSSI values for 1.3 GHz signals are -126.4 dBm and -130.8 dBm in light urban and heavy urban environments. Operating at 150 M H z rather than 1.3 GHz gives a 5-7 dB advantage within similar environments. In previous work, Saunders and others have shown that building heights tend to be lognormally distributed. Its variation will cause excess path loss and RSSI to vary as well. Measuring the degree of variation for RSSI at both frequencies, the standard deviation (STD) values for 138 M H z signal are 5.1 dB and 5.3 dB in light urban and heavy urban environments, while those for 1.3 GHz signal are 7.5 dB and 7.3 dB respectively. The 138 M H z signal is less sensitive to changes in the environment. In link budget design, coverage probability and shadowing margin are found according to the standard deviation of RSSI. The 138 MHz signal requires less shadowing margin to guarantee the same coverage probability. Service Availability Consideration From a propagation perspective, satellite system service availability is mainly influenced by (1) the constellation design which determines how often the satellite can be seen by the user, Chapter 4. System Coverage in Suburban and Urban Environments 43 and (2) the propagation path which determines how the terrain and land usage degrade the signal strength and quality. As described in Section 3.2, our simulation model takes system constellation and building blockage into account. To quantify the availability of the satellite service, two indicators are important: (1) the percentage of time that RSSI prediction is higher than threshold values and (2) the duration over which RSSI is constantly higher than a threshold value. • Percentage of time that RSSI prediction is higher than threshold values Figure 4.4 gives the percentage of time that RSSI prediction is higher than the thresh-old from -145 dBm to -95 dBm when there is a satellite in view. If the sensitivity of the SC is -125 dBm, from Figure 4.4 we can see that for 138 M H z signals in light urban and heavy urban environments, 91% and 53% of time the service could be available. For 1.3 GHz signals, those time percentages drop to 39% and 17% respectively. • Connection duration over which RSSI is constantly higher than threshold values In order to successfully establish a communication link, signal strength needs to be constantly above the SC sensitivity for a short period of time to complete processes like synchronization, authorization, channel assignment and sending datagrams. Assume that short period of time is one minute. Figure 4.3 gives the mean duration for threshold from -145 dBm to -95 dBm for both 138 M H z and 1.3 GHz. If the sensitivity of SC is -125 dBm, at 138 MHz, SC is able to have one minute's signal constantly stronger than SC sensitivity in light urban environment, while very unlikely to have that in heavy urban environment. Therefore, a communication link can be established successfully in light urban, but not heavy urban. Using the results in Figure 4.3 and given the SC sensitivity, we can have an intuitive idea on the possibility of a successful message delivery. By examining Figure 4.3 and 4.4, we found that in the same type of environment, the 138 M H z signal has better service availability both in overall time percentage and connection duration. For example at -125 dBm RSSI level, 138 M H z signal has about 45 — 50% more coverage avaialble time. For the mean connection duration, at -125 dBm threshold, 138 MHz Chapter 4. System Coverage in Suburban and Urban Environments 44 has more than 1 minute connection which provides enough time to establish the connection. However, the 1.3 GHz signal is very unlike to provide service at the -125 dBm RSSI level. Chapter 4. System Coverage in Suburban and Urban Environments 45 100ft 4 Z $ . \ "A \ A \ y - * - 1.3GHz Light Urban - A - 138MHz Light Urban - o - 1.3GHz Heavy Urban - 0 - 138MHz Heavy Urban \ \ \ A • * : - \ : -* N ^ . A . . -150 -145 -140 -135 -130 -125 -120 -115 -110 -105 -100 Signal Strength (dBm) Figure 4.1: Complementary cumulative distribution function of RSSI at 138 M H z and 1.3 GHz Chapter 4. System Coverage in Suburban and Urban Environments 46 10 to ID ID O CO X) TO c TO 0) 2 10"1' 03 in O TO CL ID to a> o X CD TO _ 2 S 10 ' CD D> TO "c cu a. A ^ ; ^ . > ..:. \ * . . . ; . . . \ : \ A j . \ \ .A. - * - 1.3GHz Light Urban -A- 138MHz Light Urban -o- 1.3GHz Heavy Urban -0- 138MHz Heavy Urban 4: : x \ j & u \ i_ 10 15 20 Excess Path Loss (dB) 25 30 35 Figure 4.2: Complementary cumulative distribution function of excess path loss at 138 MHz and 1.3 GHz Chapter 4. System Coverage in Suburban and Urban Environments 47 n ' — r " 1 — I — r I I I I I I " I I I I - * - 1.3GHz Light Urban -A- 138MHz Light Urban - o - 1.3GHz Heavy Urban - 0 - 138MHz Heavy Urban i i i i i i i i i i i i i i i i i \ i i i i i i i i i * i -145 -140 -135 -130 -125 -120 -115 Signal Strength (dBm) -110 -105 -100 -95 Figure 4.3: Mean duration of connection vs. RSSI threshold at 138 M H z and 1.3 GHz Chapter 4. System Coverage in Suburban and Urban Environments 48 CO 90 CO CO o CO Ab 80 c CO . c •4—' 70 CD -C O) X 60 _co W 50 (Z> a: CO sz 40 CD E i -30 o CD O) CO ' c 20 CD o CD Q- 10 9 c * \ ? . \ A A ; A - * - 1 .3GHz Light Urban - A - 138MHz Light Urban - o - 1 .3GHz Heavy Urban - 0 - 138MHz Heavy Urban i \ \ \ .\ \. . i \ i . . \ \ A 9 * i \ . . \ \ <*> i i. \ \ \ \ \ \ * . . i t i \ \ A \ \ © I \ \ i * < . . \ \ > * ( \ . . . . J . . . 1 9, 1 4 » A t . i \ \ \ \ 6 < \ * : A V A A A \ A ^ \ -145 - 1 4 0 - 1 3 5 - 1 3 0 - 1 2 5 - 1 2 0 - 1 1 5 - 1 1 0 - 1 0 5 - 1 0 0 - 9 5 RSSI (dBm) Figure 4.4: Time share vs. RSSI threshold at 138 M H z and 1.3 GHz Chapter 4. System Coverage in Suburban and Urban Environments 49 4.3 Influence of Degree of Buildup 4.3.1 Theoretical Background For satellite downlink, the elevation angles of satellites viewing from users are normally higher than 5-10 degrees depending on constellation design. The buildings that are the closest to the user have the most significant influence on the user. From the explanation of Fresnel Zone in Section 4.2.1, the higher the obstacle between the transmitter and receiver, the greater the diffraction loss. It is apparent that highly buildup areas degrade the signal strength more severely than less buildup areas do. In the following section, numerical results will be analyzed to reveal the quantitative relationship between building height and coverage. 4.3.2 Simulation Results and Analysis Classification of degree of buildup We classify the degree of buildup in cities such as Vancouver into three categories namely suburban, urban and heavy urban. Table 4.1 gives detailed descriptions for each of them. Because it is difficult to model using N E C - B S C , the influence of trees and foliage are not included. It would be valuable to address these issues going forward because such obstacles make an important contribution to path loss in suburban environments. Table 4.1: Classification of degree of buildup Type of Environment Description of Example Suburban 1-3 storey residential area whose building heights are around 5-15 metres. The houses in the same row are close to each other. The street between two rows are around 10-15 metres wide. Light Urban 4-7 storey business and residence buildings along a busy street. Building heights are 20-40 metres. Buildings are almost connected to each other. Depending upon the area, the street width is from 10-30 metres. Heavy Urban Densely buildup with 7 and above storey buildings whose heights are more that 40 metres. Building are almost con-nected to each other. Street width is about 10-15 metres. Chapter 4. System Coverage in Suburban and Urban Environments 50 In order to find a quantitative relationship between degree of buildup and RSSI as well as excess path loss, extensive computer simulations are conducted with mean building heights Hb 6 (10,15,20,25,30,35,40,45,50,55) metres. Excess path loss vs. Mean building height Hb Figure 4.5 gives mean excess path loss with different mean building heights Hb. Generally, more excess path loss is found with higher Hb. For Hb > 20m, there is a 1 dB increment in excess path loss for every 10-metre increment in Hb. The standard deviation of excess path loss is shown in Figure 4.6. It is apparent that excess path loss at Hb=10 m is 3 dB less than that at Hb=20 m. The standard deviation at Hb=10 m is smaller than standard deviation at higher Hb. This difference suggests: • with lower building heights, L O S is more available and be the dominant signal, which results in less overall loss caused buildings and smaller variation. • as the buildings are higher, L O S is almost not possible. Reception relies on reflection and diffraction rays, which are more attenuated and sensitive to small environment changes. These result in more loss and bigger variation (standard deviation) on excess path loss. Signal strength prediction (RSSI) vs. Mean building height Hb Applying parameters in the link budget, predicted excess path loss can be converted to predicted RSSI. Figure 4.7 gives the mean RSSI with different Hb, and Figure 4.6 gives the standard deviation of RSSI. The trend and statistics of the RSSI is very similar to that of excess path loss due to the same reasons. Observing the trend of mean RSSI when Hb >20 m, there exists a linear relationship between RSSI in dBm and mean building height in metres. By doing linear curve fitting as in Figure 4.8, we obtain a linear relationship between predicted mean RSSI PVancouver(hb) Chapter 4. System Coverage in Suburban and Urban Environments 51 and mean building height h0 as Equation 4.5. Vancouver(^b) = -0.10923 • hb - 119.36 [dBm] (4.5) In Equation 4.5, RSSI -FVancouver(hb) is mean RSSI in dBm and H0 is in metres. This equation is derived from numerical simulation results with the O R B C O M M constellation as seen from Vancouver, and where the valid mean building height is higher than 20 metres, i.e., a typical urban area. As shown in Figure 4.8, the norm of the residuals is 1.214. This means a 1.214 dB error margin for the predicted mean RSSI, which is a relatively small error. Using Equation 4.5 and the standard deviation of RSSI in Figure 4.6, the coverage probability can be estimated for different degrees of buildup in Vancouver. Chapter 4. System Coverage in Suburban and Urban Environments 52 10 ! ! ! ! ! 3 It * Mean excess path loss * * * r K * * * 0 5 10 15 20 25 30 35 40 45 50 55 Mean building height (m) Figure 4.5: Mean excess path loss at difference degree of buildup Chapter 4. System Coverage in Suburban and Urban Environments 53 * o of excess path loss + oof RSSI + * H r * h |e H V • t V V h * I * * b } t 2r 1 - : ; • ; -Q I I I I I I I I I 10 15 20 25 30 35 40 45 50 55 Mean building height (m) Figure 4.6: Standard deviation of excess path loss and RSSI at difference degree of buildup Chapter 4. System Coverage in Suburban and Urban Environments 54 t * Mea n RSSI * * K-> It * It it it » 10 15 20 25 30 35 40 Mean building height (m) 45 50 55 Figure 4.7: Mean excess path loss at difference degree of buildup Chapter 4. System Coverage in Suburban and Urban Environments 55 Figure 4.8: Linear curve fitting for mean RSSI in urban area, Vancouver Chapter 4. System Coverage in Suburban and Urban Environments 56 4.4 V a r i a t i o n o f C o v e r a g e w i t h L a t i t u d e 4.4.1 T h e o r e t i c a l B a c k g r o u n d In a L E O system, the satellites are orbiting the earth very rapidly. For example, O R B -C O M M satellites can circle around the earth in about 2 hours. Due to this rapid motion, the range, free space path loss and elevation angle between the user and satellite change quickly. At different locations on the earth, the statistical distributions of elevation angle and range are very different. This difference is closely related to the specific constellation design of the satellite system. In the O R B C O M M system, there are four orbit planes that have 45 degree inclination and two orbit planes that have 70 degree inclination. On each 45 degree plane, there are 8 satellites, and on each 70 degree plane, there are 2 satellites. As illustrated in Figure 4.9, user at 60 degree latitude cannot see a satellite on 45 degree planes with 90 degree elevation angle, however user at latitudes lower that 45 degree can. If observing over a long period of time, the statistical property of elevation angle varies with user locations. Free Space Path Loss vs. Latitude As shown in Figure 4.9, point A is right above the user (90 degree elevation angle) and has the shortest range 780 km; point B is at minimum elevation angle from the user (5 degree) and has the longest range 2800 km. From Equation (4.6), free space path loss at A and B are 133 dB and 144.7 dB respectively. The difference between them is about 12 dB. ^FreeSpacc = 10 • log 1 0 {^f [dB] (4.6) r : Distance between user and satellite [metre] A : Wavelength [metre] By simulations over a longer period of time, we can find the statistics of free space path loss with different latitudes. Figure 4.10 and 4.11 give the statistics of free space path loss at different latitude. It can be seen that latitude 40° has least free space path loss and latitude 60° has the biggest. At latitude 10°, 20° , 30° and 50°, free space path loss have Chapter 4. System Coverage in Suburban and Urban Environments 57 very comparable statistical properties. E levat ion A n g l e D i s t r ibut ion vs. Lat i tude In previous chapters, we have shown that elevation angle of satellite is important in deter-mining the degree of blockage of the signal in urban environments. Figure 4.12 gives the complementary cumulative distribution function of elevation angle at latitude 0°, 10°, 20°, 30° , 40° , 50° , 60° , 70° and 80°. At latitudes 0° , 10°, 20° , 30° , and 40° , the elevation angle can be up to 90 degrees. As latitude goes higher than 45° , the elevation angle for satellites in the four 45-degree inclination orbit planes can not reach 90° . At latitude 40° , elevation distribution shows the biggest portion of high elevation angles. The complementary cumu-lative distribution function of 0°, 10°, 20°, 30° and 50° are very close, except the very high elevation angle part that is not available for latitude 50°. At latitude 70°, higher elevation angles are available by the coverage of two 70-degree orbit planes, which only have two satellites each. However, the coverage is very limited in terms of time continuity. There exist long gaps between successive satellites appearing in view. We also find that statistical distributions of elevation and range do not change much with longitude under O R B C O M M constellation. The reason is that when satellites orbit the earth, the self-rotation of the earth causes the footprint of each satellite cover all 360 degrees of longitude almost evenly. Therefore, in the following discussion, latitude will be the point of interests. 4.4.2 S i m u l a t i o n R e s u l t s a n d A n a l y s i s Simulations are completed under light urban environment (H^ = 20m) at latitudes 10°, 20°, 30° , 40° , 50° and 60°. For higher latitude such as 70° and 80° , there are very few heavily built-up cities and free space path loss information is likely adequate for predicting satellite coverage. Figure 4.13 compares the statistics of excess path loss at different latitudes. Since all simulations are under the same geometry and building height setting, the difference is due to different elevation distribution. It can be found that the statistics are very close for all altitudes, which conforms with the factor that complementary cumulative distribution func-Chapter 4. System Coverage in Suburban and Urban Environments 58 tions of elevation angle 0° , 10°, 20° , 30° , 40° and 50° are very close. Even though latitude 40° has a higher elevation portion, the difference has almost no effect on the statistics of excess path loss. Figure 4.14 give the probability of time that signal strength is higher than different thresholds. At latitude 40° , the best signal strength is available while latitude 60° has the weakest signal strength. This difference is mainly due to differences between the statistics of free space path loss (as shown in Figure 4.10). In the above analysis, the influences of the latitude of user on coverage are studied in two aspects: free space path loss statistics and excess path loss statistics. It can be found that • free space path loss statistics vary with latitudes and influence the coverage differently at different latitudes. At latitude lower than 50°, the free space loss statistics are very similar. At higher latitudes, since these area are mainly covered by satellites on orbit planes with less satellites, the coverage is not as good as low latitudes. • though the statistics of elevation angle changes with latitude (as shown in Figure 4.12), the differences among latitudes 10° 20° 30° and 50° are very small. And the difference among the excess path loss statistics at different latitude are even smaller. There-fore, the elevation angle distribution does not influence the signal strength statistics significantly. In this section, simulations are only completed at light urban environment. However excess path loss statistics doesn't change much with latitude, the excess path loss statistics under different degree of buildup in Section 4.3 can be used in conjunction with free space loss statistics at different latitudes in this section to predict the signal strength statistics at different latitudes under different degree of buildup. Chapter 4. System Coverage in Suburban and Urban Environments 59 4.5 Influence of Termina l A n t e n n a Pa t t e rn 4.5.1 Theoretical Background If the distance between transmitter and receiver is d, and radio wave arrive at the antenna from every angle defined by the direction (9,cf>), the power at receiving antenna output Pr(d) can be expressed as: where -Ppi-cc-Space^ ) is the power at receiver before into antenna through free space propaga-tion; gr are the antenna gain factor as a function of three parameters at the receiving sites: (i) Gr(0,4>) the pattern of radiation received by the mobile unit's antenna, (ii) a is a loss factor depending on the angle of signal arrival, and (iii) pr(8, <f>) P .D.F. of incoming waves' angle of arrival. Then gr can be expressed as: where k\ is a constant for normalization. For land mobile satellite communication link with L O S propagation, pr(9, 4>) is mainly influenced by orbit design and user location. Compared with terrestrial wireless system, satellite links tend to have higher elevation angles. If receiving antenna radiation pattern Gr(9,(p) has more gain at higher elevation angles, from Equation 4.7 and Equation 4.8 the overall gain factor gr would be bigger and received power at antenna output Pr(d) is stronger. This is why most antennas for satellite applications have higher gain toward the zenith. While in urban environment, scattering makes the propagation paths more complex and significantly influence the angle of arrival distribution pr(9,<{>).. Pr(d) = -PFreeSpace(d) • gr(0, <p) (4.7) (4.8) Chapter 4. System Coverage in Suburban and Urban Environments 60 4.5.2 Simulation Results and Analysis Simulation Settings System coverage is also affected by the radiation pattern of the terminal antenna. Here we compare antennas with the pattern of a vertically polarized quarter wave monopole and a hemispherical pattern, respectively. Both antennas are omini-directional in the horizon-tal (4>) plane; only the vertical patterns differ. The hemispherical pattern is an approx-imation of the pattern of a low-profile antenna similar to those currently used by many O R B C O M M subscribers. The vertical pattern (8 plane) of the hemispherical antenna is shown in Figure 3.4 and is mathematically expressed as GT{8) = 2cos#, 8 G [0,7r/2]. The vertical plane of a A/4 monopole is in Figure 3.5 and mathematically expressed as Gr{8) = 2cos( | • cosf?)/sinf?, 8 £ [0,7r/2]. A/4 monopole antenna is widely used in ter-restrial wireless application. Simulations are conducted under suburban light urban and heavy urban environment. By doing this comparison, we can quantitatively assess the per-formance of the two antenna patterns under different degrees of blockage with O R B C O M M orbit data acquired at Vancouver. Signal Strength Statistics Figure 4.15 shows the complementary cumulative distribution function of the predicted RSSI. Table 4.2 gives the mean and standard deviation of predicted RSSI. In suburbans Table 4.2: Comparison of signal strength statistics between low-profile antenna and A/4 monopole Mean @ Suburban Mean @ Light Urban Mean @ Heavy Urban low-profile -117.4 dBm -120.7 dBm -123.7 dBm A/4 monopole -122.9 dBm -124.4 dBm -125.2 dBm A 5.5 3.7 1.5 S T D @ Suburban S T D @ Light Urban S T D @ Heavy Urban low-profile 4.23 dB 5.12 dB 5.33 dB A/4 monopole 8.2 dB 8.4 dB 8.14 dB environment, the RSSI difference between low-profile and monopole is the biggest 5.5 dB. As the building height goes up, the difference becomes smaller 1.5 dB. This variation can be explained as follows. Chapter 4. System Coverage in Suburban and Urban Environments 61 • When there is very little blockage, incoming waves have higher elevation angles. A low-profile antenna has more gain in the zenith direction and enhance the reception effectively. On the contrary, monopole's pattern has less gain in the zenith direction, which reduces the received power. The radio waves that reach the monopole from ground and building face reflections are much weaker than L O S waves. Therefore, the greatest difference is observed. • As building height increases to light urban setting, more incoming wave reach the . receiving antenna not through L O S paths but roof edge diffractions, reflections from building face and ground etc. Monopole antenna can more effectively collect sig-nals from ground reflections and other low elevation waves. The difference on signal strength is is reduced. Waves diffracted only once by building roof edge contribute the most. The elevation angle seen from roof edge to mobile is determined by building height Ht, and distance between mobile user and building face d/2. This angle in most case is more than 45 degree. As a result, simulation results suggest that the performance of the low-profile antenna is still better than a monopole. • As building height goes up to heavy urban setting, more and more radio waves reach mobile user through more than one diffraction or reflection. The difference on signal strength is further reduced. And for monopole antenna, as building height goes higher the difference between RSSI statistics in light urban and heavy urban is very small. This is because higher elevation sig-nals reaching the antenna through both LOS and N L O S contribute more significantly. The variation of signal strength on these waves doesn't change the total reception of monopole antenna too much. Chapter 4. System Coverage in Suburban and Urban Environments 62 Figure 4.9: Geometry of elevation angles at different latitude Chapter 4. System Coverage in Suburban and Urban Environments 63 \ • v i L - N \ \ \ \ -*- Latitude=10 -o- Latitude=20 -0- Latitude=30 —H- Latitude=40 -A- Latitude=50 -v- Latitude=60 \ • A N \ A .V. \ A \ \ \ 5 \ -k. N N . \ s \ . . . X ..A . A v \ < A N \ N V \ M 9 .A 136 138 140 142 Free Space Path loss (dB) 144 146 Figure 4.10: Complementary cumulative distribution function of free space path loss Chapter 4. System Coverage in Suburban and Urban Environments 64 1 ^ _ 10 20 30 Latitude (degree) 40 50 60 10 20 30 Latitude (degree) 40 50 60 Figure 4.11: Mean and standard deviation of free space path loss Chapter 4. System Coverage in Suburban and Urban Environments 65 Elevation angle (Degree) Elevation angle (Degree) Figure 4.12: Complementary cumulative distribution function of elevation angle at different latitude Chapter 4. System Coverage in Suburban and Urban Environments 66 1 SS 0.9 CO 'o co "ro 0.8 CD £ 0.7 CO « CO CO •2 0.5 "co CU o X Z 0.3 CO sz • S» 0.2 CO •*—• c g 0.1 v = 4^ ! - * - Latitude=10 - o - Latitude=20 - 0 - Latitude=30 —t- Latitude=40 - A - I a t i t n H e = 5 n ? X .... \ \&v - v - Latitude=60 \* "\ \ Y ^ \ \ i , H > \ \ ^ \ Hv V . ' 5 10 15 Excess path loss (dBm) 25 Figure 4.13: Complementary cumulative distribution function of excess path loss at different latitude Chapter 4. System Coverage in Suburban and Urban Environments 67 RSSI (dBm) Figure 4.14: Complementary cumulative distribution function of signal strength at different latitude Chapter 4. System Coverage in Suburban and Urban Environments 68 RSSI (dBm) Figure 4.15: Complementary cumulative distribution function of signal strength with dif-ferent antenna pattern Chapter 5 69 C o n c l u s i o n s a n d R e c o m m e n d a t i o n 5.1 Conclusions In this thesis, we have implemented and validated a physical-statistical 3-D earth-space propagation simulation tool based upon N E C - B S C , a well-supported and widely used U T D -based numerical electromagnetics code. Using N E C - B S C to compute diffraction effects for particular building and path geometries gives us reasonable accuracy and ease of use while saving us the time and effort required to develop a custom UTD-based code. Extensive computer simulations were completed over a broad range of building height distributions using a simple geometry representing a street canyon. From these results, we have shown how wavelength and building blockage jointly affect Land Mobile Satellite System (LMSS) system coverage. Our results show that: • Under line-of-sight conditions, or when blockage is negligible, changing wavelength has little effect on coverage. • As the average building height increases, V H F coverage degrades less than L band coverage. In a light urban environment, the difference of mean signal strength is almost 6 dB; In a heavy urban environment, the difference increases slightly to 7 dB. • When building height increases still further, the difference remains constant. • The standard deviation of the signal strength at 138 M H z is around 2 dB less than that observed at 1.3 GHz; this is an indication of higher coverage probability. We have found that in most environments, an antenna with a' hemispherical pattern provides more effective reception than the A/4 monopole antenna that has traditionally Chapter 5. Conclusions and Recommendation 70 been the most popular antenna for O R B C O M M applications. In light urban environments, the difference between the means is nearly 4 dB. In heavy urban environments, this reduces to only 1.5 dB. We also examined the variation of coverage as a user in a light urban environment changes latitude. The influence of latitude on coverage is a function of two factors: the free space path loss and the elevation angle distribution. • The free space path loss distribution changes significantly with latitude. At latitude 40° , about 15% of free space path loss is above 142 dB. At latitude 60° , about 55% of free space path loss values exceed this value. • Although the statistics of elevation angle changes with latitude, the differences in the elevation angle distribution among latitudes 10° 20° 30° and 50° are not as large as those of free space loss. The differences between the excess path loss statistics due to building blockage at different latitudes are even smaller. It appears that the elevation angle distribution doesn't significantly influence the signal strength statistics. In practical engineering applications, these results can provide insight concerning how the environment, user location and antenna pattern influences system coverage in urban and suburban areas. 5.2 Recommendations for Future Work Interference Issue Anecdotal results presented in Section 3.3.2 suggest that terrestrial interference may be more significant than previously realized. If so, it is another factor that should be considered when deploying Orbcomm subscriber/coommunicators in urban environments. We suggest that steps be taken to determine the full nature and extent of the problem. Antenna Design for ORBCOMM Application One of the purposes of this thesis project is to reveal the influence of terminal antenna radiation pattern on system performance in different environments. The physical-statistical Chapter 5. Conclusions and Recommendation 71 propagation model implemented in this study is a useful tool to evaluate whether the an-tenna design can work efficiently under its objective application environment and to predict possible performance. Utilizing the numerical results and software tool, we can provide application oriented terminal antenna design. C a s e S p e c i f i c S i m u l a t i o n w i t h D E M D a t a We have based our simulations upon a simple geometric model of a typical street canyon in an urban environment. The dimensional parameters of this street canyon are randomly chosen from distributions that are representative of actual urban environments. This is a reasonable simplification that also permits easy comparison with previous results in the literature. Studies with more realistic and complex geometries, such as a Digital Elevation Model (DEM) data that incorporates both ground and building data, could be conducted to give site-specific prediction results. This may be more useful in practical engineering applications and would simplify the task of experimentally validating our results. 72 References [1] A . Duffy, A . Martin, G . Antonini, A. Scogna, and A. Orlandi, "Issues in validation of complex-valued simulations for signal integrity analysis," in Proc. 2004 International Symposium on Electromagnetic Compatibility, vol. 3, Aug. 2004, pp. 1011-1016. [2] "Propagation data required for the design of earth-space land mobile telecommunica-tion systems (ITU-R Recommendation P.681-3)," Geneva, Switzerland, 1997. [3] J . Goldhirsh and W . Vogel, "Roadside tree attenuation measurements at U H F for land mobile satellite systems," IEEE Trans. Antennas Propagat., vol. 35, pp. 589-596, May 1987. [4] , "Mobile satellite system fade statistics for shadowing and multipath from road-sode tree at U H F and L band," IEEE Trans. Antennas Propagat., vol. 37, pp. 489-498, Apr. 1989. [5] W . Vogel, G . Torrence, and H.-P. Lin, "Simultaneous measurements of L and S band tree shadowing for space-earth communications," IEEE Trans. Antennas Propagat., vol. 43, pp. 713-719, July 1995. [6] J . Goldhirsh, W . Vogel, and G. Torrence, "Mobile propagation measurements in the US at 20 GHz using A C T S , " in Proc. IEE ICAP'95, vol. 2, Apr. 1995, pp. 381-386. [7] J . Butterworth, "Propagation measurement for land mobile satellite system in the 800 M H z band," Communication Research Center, Ottawa, Ontario Canada, Tech. Note 724, Aug. 1984. [8] , "Propagation measurement for land mobile satellite system at 1542 MHz," Com-munication Research Center, Ottawa, Ontario Canada, Tech. Note 723, Aug. 1984. References 73 [9] C . Loo, "A statistical model for a land mobile satellite link," IEEE Trans. Veh. Tech-nol, pp. 122-27, 1985. [10] G . Corazza and F . Vatalaro, "A statistical-model for land mobile satellite channels and its application to nongeostationary orbit system," IEEE Trans. Veh. Technol, pp. 738-42, 1994. [11] S.-H. Hwang, K . - J . K i m , J . -Y. Ahn, and K . - C . Whang, "A channel model for nongeo-stationary orbiting satellite system," in Proc. IEEE 47th Vehicular Technology Con-ference (VTC'97), vol. 1, May 1997, pp. 41-45. [12] E . Lutz, D. Cygan, M . Dippold, F . Dolainsky, and W . Papke, "The land mobile satellite communication channel-recording, statistics and channel model," IEEE Trans. Veh. Technol, vol. 40, pp. 375-386, May 1991. [13] Y . Karasawa, K . Kimura, and K . Minamisono, "Analysis of availability improvement in LMSS by means of satellite diversity based on three-state propagation channel model," IEEE Trans. Veh. Technol., vol. 46, pp. 1047-1056, Nov. 1997. [14] G . Butt, B . Evans, and M . Richharia, "Narrowband channel statistics from multiband propagation measurements applicable to high elevation angle land-mobile satellite sys-tems," IEEE J. Select. Areas Commun., vol. 10, pp. 1219-1226, Oct. 1992. [15] S. R. Saunders and B. G . Evans, "A physical-statistical model for land mobile satellite propagation in built-up areas," in Proc. IEE ICAP'95, vol. 2, Apr. 1997, pp. 44-47. [16] C. Oestges, S. Saunders, and D. Vanhoenacker-Janvier, "Physical statistical modelling of the land mobile satellite channel based on ray tracing," IEE Proc. on Microwaves, Antennas and Propagation, vol. 2, pp. 554-558, Oct. 2002. [17] C. Oestges and D. Vanhoenacker-Janvier, "Physical-statistical prediction of perfor-mance for land mobile satellite communication systems," IEE Proc. on Microwaves, Antennas and Propagation, vol. 146, pp. 362-368, Oct. 1999. References 74 [18] R. Zabela and C. Bostian, "Measurements of building penetration by low orbit satellite signals at V H F , " in Proc. IEEE APS Int. Symp. Dis., vol. 1, 18-25 Jul. 1992, pp. 604 - 607. [19] I. Benzair, B.and Glover and J . Gardiner, "Vertical propagation of radio signals in an eleven storey building at 144 MHz," in Proc. Sixth International Conference on Mobile Radio and Personal Communications, Dec. 1991, pp. 83-86. [20] ORBCOMM System Overview - (Doc. NO. A80TD0008), E ed., O R B C O M M Global, L.P. , Dulles, Virginia, USA, Feb. 1999. [21] M . Bester, The Satellite Tracking Program SatTrack (V3.1), Mar. 1995. [22] Numerical Electromagnetics Code - Basic Scattering Code V4-2 User's Manual (Pre-liminary Draft), Revised June 2, 2000 ed., ElectroScience Laboratory, Department of Electrical Engineering, The Ohio State University, June 2000. [23] ORBCOMM ORBPerform User Guide, 1.5 ed., O R B C O M M Global, L.P. , Dulles, Vir-ginia, USA, Feb. 2000. [24] C . A. Balanis, Advanced Engineering Electromagnetics. Wiley, 1989, pp. 782-783. Appendix A Sample of SatTrack Output 75 Satellite Name, Date, Time, Azimuth [deg], Elev[deg], Range[km], Sun Ang[deg], Doppler [kHz], Loss[dB], Phs, V A6, Fr i 02Apr04, 12 00 00, 302 8, 5. 9, 2661. 8, 84. 6, + 1. 49, 144. 2, 60, D A6, Fr i 02Apr04, 12 00 10, 304 2, 6. 3, 2631. 8, 84. 7, +1. 44, 144. 1, 60, D A6, Fr i 02Apr04, 12 00 20, 305 6, 6. 6, 2602. 9, 84. 8, +1. 37, 144. 0, 61, D A6, Fri 02Apr04, 12 00 30, 307 0, 6. 9, 2575. 3, 85. 0, +1. 31, 144. 0, 61, D A6, Fr i 02Apr04, 12 00 40, 308 4, 7. 2, 2549. 0, 85. 1, +1. 25, 143. 9, 62, D A6, Fr i 02Apr04, 12 00 50, 309 9. 7. 5, 2524. 1, 85. 2, +1. 18, 143. 8, 62, D C4, Fr i 02Apr04, 12 05 50, 338 7, 13 • 9, 2094 • 7, 89 • 4, +2 .49, 142 • 2, 188, D C4, Fri 02Apr04, 12 06 00, 339 8, 14 .7, 2043 • 9, 90 • 2, +2 .45, 141 .9, 188, D C4, Fri 02Apr04, 12 06 10, 341 0, 15 • 6, 1994 • 0, 91 • 0, +2 .41, 141 • 7, 189, D C4, Fri 02Apr04, 12 06 20, 342 3, 16 .4, 1945 .0, 91 • 8, +2 .36, 141 • 5, 189, D C4, Fri 02Apr04, 12 06 30, 343 6, 17 • 3, 1897 • 0, 92 • 7, +2 .31, 141 • 3, 189, D C4, Fri 02Apr04, 12 06 40, 345 0, 18 • 2, 1850 .1, 93 • 5, +2 .26, 141 • 1, 190, D C4, Fri 02Apr04, 12 06 50, 346 5, 19 • 1, 1804 .2, 94 • 4, +2 .20, 140 • 9, 190, D C4, Fr i 02Apr04, 12 07 00, 348 1, 20 • 1, 1759 • 7, 95 • 3, +2 • 14, 140 • 6, 191, D C4, Fr i 02Apr04, 12 07 10, 349 8, 21 • 0, 1716 • 4, 96 • 3, +2 .07, 140 • 4, 191, D 76 Appendix B S a t T r a c k T L E D a t a U p d a t e a n d P r e d i c t i o n R u n n i n g P r o c e d u r e B . l Updating the Orbcomm . T L E file Step-l Download the orbit file from Orbcomm website. Step-2 Open this orbit file and delete all the blank lines in the data. Step-2 Rename it as tle.dat and store it in directory \SatTrack\tle\. Step-3 If the Satlist for Orbcomm satellite is not available, create a satellite list file in directory \SatTrack\data\. Otherwise, go to step 4. Step-4 Delete the Orbcomm data currently in file tlex.dat. Step-5 Go to directory and run Maketlex. This step will update the tlex.dat file with new satellite orbit data in tle.dat. Step-6 Open tlex.dat and delete the extra catriage returns from the data. After finishing above steps, you can run Sattrack for prediction now. B.2 Running Simulation for A l l The Orbcomm Satellite Step-l Make sure file satlist — ocm.dat is avaiable in directory \SatTrack\data\. If not, creat one according to other satlist data examples. Step-2 Go to direcoty \SatTrack\data\, and edit the batch.dat file with your required simulation settings. Appendix B. SatTrack TLE Data Update and Prediction Running Procedure 77 Step-3 Go to direcory \SatTrack\run\, and run Makepassesx. Choos 'ocm' when you are asked to input satlist name. Step-4 Run 'passesx'. All the prediction data are in directory \SatTrack\pred\. 78 Appendix C D a t a C o l l e c t i o n C o d e This code is developed to execute on O R B C O M M subcriber communicator. The code is developed under DOS environment utilizing S D K in C programming language. #include "kme_lib.h" #pragma _section B=USER #pragma .section B unsigned char signal_level[11000]; void main(void) { in t count=0; i n t no_of_pnts=10000; TIME_INF0 *time; //set a pointer to time structure defined i n SDK signal_level[count]=255; //write FF i n the f i r s t byte count=count+l; signal_level[count]=238; //write EE i n the second byte, as indicator of beginning count=count+l; while (count < no_of_pnts) •C signal_level[count+1]= get_sat_no(); //get s a t e l l i t e ID signal_level[count+2]= get_adcon_data(0); //get RSSI Appendix C. Data Collection Code 79 go_wait(10UL,SEC_UT); //wait a short period count=count+2; > signal_level[count]=255; //write FF i n the byte before l a s t count=count+l; signal_level[count]=238; //write EE i n the l a s t byte, as indic a t o r of end count=count+l; exit_user_apl(); } 

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