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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 T H E S I S 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 THE REQUIREMENTS FOR THE D E G R E E OF M A S T E R OF A P P L I E D SCIENCE in The Faculty of Graduate Studies (Electrical and Computer Engineering)  T H E UNIVERSITY OF BRITISH C O L U M B I A A p r i l 2005 © Jueren (Steven) M a , 2005  11  A b s t r a c t  While land mobile satellite systems operating at V H F (150 M H z ) should provide better coverage in urban and suburban environments than similar systems that operate at L-band (1 G H z 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 d B 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  Abstract  ii  Contents  iii  L i s t of T a b l e s  vi  L i s t of F i g u r e s  1  Acknowledgements  1  2  vii  ix  Introduction  1  1.1  Statement of Problem  1  1.2  Background and Motivation  1  1.3  Objective and Approach  2  1.4  Thesis Outline  5  Literature Review  6  2.1  Introduction  6  2.2  Satellite Signal Blockage Modeling  7  2.2.1  Empirical Model - E R S 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  2.3  11  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  2.4  3  14  Methodology  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  3.4  4  Limitations of Previous Work  iv  Field Measurement  24  3.3.1  Data Collection K i t - Hardware and Software  25  3.3.2  Problems with Data Collection Kit and Further Development . . . .  26  3.3.3  Measurement Location Selection  29  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  System Coverage in Suburban and U r b a n Environments  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  4.4  4.5  Influence of Degree of Buildup  49  4.3.1  Theoretical Background  49  4.3.2  Simulation Results and Analysis  49  Variation of Coverage with Latitude  56  4.4.1  Theoretical Background  56  4.4.2  Simulation Results and Analysis  57  Influence of Terminal Antenna Pattern  59  4.5.1  59  Theoretical Background  4.5.2  5  Contents  v  Simulation Results and Analysis  60  Conclusions and Recommendation  69  5.1  Conclusions  69  5.2  Recommendations for Future Work  70  References  72  A  S a m p l e of S a t T r a c k O u t p u t  75  B  SatTrack T L E D a t a Update and Prediction R u n n i n g Procedure  C  . . . .  76  B.l  Updating the Orbcomm . T L E file  76  B.2  Running Simulation for A l l The Orbcomm Satellite  76  D a t a Collection 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  3.2  Satellite to subscriber link budget at minimum elevation angle and edge cov-  27  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 . . .  3.1  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  LNA  26  3.9  SC  26  3.10 RSSI in sports field without L N A  3  ,  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 validation  4.1  Complementary cumulative distribution function of RSSI at 138 M H z and 1.3 G H z  4.2  38  45  Complementary cumulative distribution function of excess path loss at 138 M H z and 1.3 G H z  46  List of Figures  viii  4.3  Mean duration of connection vs. RSSI threshold at 138 M H z and 1.3 G H z .  47  4.4  Time share vs. RSSI threshold at 138 M H z and 1.3 G H z  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  . .  4.11 Mean and standard deviation of free space path loss  63 64  4.12 Complementary cumulative distribution function of elevation angle at different latitude  65  4.13 Complementary cumulative distribution function of excess path loss at different latitude  66  4.14 Complementary cumulative distribution function of signal strength at different latitude  67  4.15 Complementary cumulative distribution function of signal strength with different 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 L i m (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.  A n d 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. O h , 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 M a . They are always standing behind me and supporting me hundred percent, whenever and wherever. Kristy, my love and best friend, thank you for everything!  i  Chapter  1  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 allows us to assess the effects of building blockage, terminal antenna radiation pattern and carrier frequency on system coverage. Our comparisons of system performance with different 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 satellites deployed in low earth orbit ( L E O ) 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 frequency bands at V H F and U H F . To date, O R B C O M M (Downlink: 137-138 M H z , Uplink: 149-150 M H z ) is the only such system to be deployed. Development of the O R B C O M M L M S S 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 ( 7 0 ° 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. includes oil, mining, and other companies that use O R B C O M M ' s remotely control their facilities in distant areas. trucking companies that use O R B C O M M ' s fleets.  One group  service to monitor and  The other includes transportation and  service to track and locate their cargoes and  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 G H z 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 provided 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 (latitude 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 physicalstatistical 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 recommendations for further work.  6  C h a p t e r  2  L i t e r a t u r e  2.1  R e v i e w  Introduction  Propagation and channel modeling for earth-space communications to and from low earth orbit ( L E O ) 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 propagation 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. limitations of previous research results are identified.  Finally, in Section 2.4, the  Chapter  2.2  2.  Literature  Review  7  Satellite 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 first  1  and/or the second order statistics of the satellite signal. 2  2.2.1  Empirical Model - E R S Model  The Empirical Road Side (ERS) model, which is described in I T U - R Recommendation P.681-3 [2], is a typical empirical L M S S 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 M H z ) and L(1.5 GHz) bands in Maryland (USA) [3] [4]. A t 1.5 G H z , the model is expressed as:  L(p, 9) = -(3.44 + 0.09750 - O.OO20 ) • ln(p) - 0.4436 + 34.76 2  (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, 2 0 ° < 9 < 6 0 ° and 1% < p < 20%, the loss can be calculated as: L{p, / ) = LUL) • exp{1.5 • {(J-)  05  where L(fi)  - (jf }} 5  (2.2)  is the loss at 1.5 G H z (Equation 2.1) and fr, — 1.5 G H z .  • For 20% < p < 80%, the loss can be expressed as:  L(p,0,/)=L(2O%,0,/).^^  (2.3)  'First order statistics: Signal envelope statistics which can facilitate fade margin design. Second order statistics: Level Crossing Rate (LCR) and Average Fade Duration (AFD) which can facilitate modem and error correction scheme design. 2  Chapter  2.  Literature Review  8  • For 7° < 8 < 2 0 ° , the fade distribution is assumed to have the same value as at 6 = 20°,  L(p,6,f)  2.2.2  = L(p, 20°, f)  (2.4)  Statistical M o d e l - L o o M o d e l  In Canada, measurements collected in the U H F (870 M H z ) 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^)  where r and 0 are channel envelope and phase respectively,  (2.5)  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:  =bjmJ  p{r)  ^r - V ]  0  ^  e  x  p  [  — 2 *  h  -  dz  (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. B y collecting data in different European cities, they were able to acquire measurements at different elevation angles, such as 13° at Stockholm, 1 8 ° 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.  T h e 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  (2.7)  p(r) = (1 - A) • p ood(r) + A • Pbad(r) g  in which A is the time share of shadowing. p od{ ) r  g0  l  s  a  Rician process,  Pgood(r) = c • exp(-c(r + 1)) • I (2c-Jr) 0  .  (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)*JO 1 r Pra leigh(r|r ) = — exp( )  (2.9)  0  y  P  l  o  g  (2.10)  0  ™  l  (  r  o  )  = T ^ h T T o  •*  •  e  x  p  2^  [  Here p is the mean power level expressed in dB and a  ]  (  2  -  n  )  is the variance of the mean power  1  level due to shadowing. The determination of the time share of shadowing A is closely related to the two transition probabilities, namely the good-to-bad probability P b and the bad-to-good probability g  Pb . If Pgb and Pbg are given, the mean duration that the channel stays in the good state g  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 ( > n) = PIgg  (2.17)  Pb(> n) = P "  (2.18)  g  b  b  When applying this model for different satellite elevations, different types of environments 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 proposed 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 attributes of the physical and statistical modeling approaches.  It utilizes  electromagnetic  theory, e.g., the Uniform Theory of Diffraction ( U T D ) , 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 specific location. Since signal propagation study for L M S S 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 G H z ) , 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  12  2. Literature Review  C . Oestges [16] [17] formulated the prediction problem as  F(x) = J f(x\ri)-T ( )d N  where:  V  (2.19)  ri  (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 <J (rj) is the average power of 2  the multipath components. T h e 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. d  m  - perpendicular distance from the mobile to the building face, the corresponding p.d.f. isT {d ). Dm  m  hi, - building height, the corresponding p.d.f. TH {hb) follows a log-normal distribution. b  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)=  / Jo Jo • T (9)  / / / Jo Jo Jo  • T {w)  e  T\ R  • T (d )  w  Dm  ^{r\h d w4>B)-T {4>)  HbDmW  • T {h )  m  Hb  b  b  m  9  (2.21)  • d{h )d(w)d(d )d4>d9 b  m  in which  TR\H D Wie{ \ bd W(f)d) r h  B  m  m  r  =  • exp[-  r  2  +  c {h ,d ,w,(f>,9) 2  b  b  rc(h ,d ,w,<j),6) b  m  a {h ,d ,w,(j),e) 2  b  The parameters a {h ,d ,w,(j),9) b  m  b  program [16]. In the L O S case, a (h ,d ,w,(j>,9) 2  b  m  (2.22)  )  m  and c(h ,d ,w,<fi,9)  2  m  2a (h ,d ,w,4>,6) 2  m  m  are estimated by a ray-tracing  is the power of the direct components. If  non-LOS, it is the power of the diffracted rays from the nearest roof edges. c(h , d , w, 4>, 9) b  m  is the power contributed by all other reflected and diffracted rays. Given the measured statistical distributions of physical parameters, the physical parameter generator produces simulation environmental settings. U T D based ray-tracing is then used to calculate the time series signal envelope.  2.3  Building Penetration by V H F Satellite Signals  At V H F band, there is very limited (published) research results concerning building penetration 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 M H z in 1992. It gave a quantitative comparison between the signal strength of outdoor reception (LOS) and that of indoor reception (Non-LOS). T h e 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 The transmitter of NOAA-9 failed on December 18, 1997 and it was permanently deactivated on February 13, 1998. 3  Chapter  2.  14  Literature Review  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 M H z with a half wavelength dipole antenna. The receiver unit is an Anritsu ( M L 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) Hp) = 20 • l o g ( p ) + 4 2  10  [dB]  (2.23)  [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. D i f f e r e n t F r e q u e n c y 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 e x p l i c i t assessment o n the influence of t e r m i n a l a n t e n n a r a d i a t i o n pattern 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 e x p l i c i t s t u d y o n t h e v a r i a t i o n of p e r f o r m a n c e d u e to t h e v a r i a t i o n o f user l o c a t i o n (latitude a n d longitude) j o i n t l y w i t h b u i l d i n g 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 distribution 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 location, 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 L M S S based upon L E O satellites.  16  C h a p t e r  3  Methodology  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 earthspace propagation simulation tool. As stated in Section 2.2.4, this approach can offer adequate 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. T h e 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 geometry 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 analyzing 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 3.2.1  Simulation Model Signal Flow of Propagation Prediction M o d e l  The physical-statistical approach utilizes electromagnetic theory to analyze randomly generated 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. T h e functionality of each module is as follows: C o n s t e l l a t i o n S i m u l a t i o n P a r a m e t e r s are the input parameters of the SatTrack program. 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 i m u l a t i o n 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  Constellation Simulation Parameter Location, Simuationl Time, Time;Stei  3.  Building.Height at Random Location  Link Budget Data (El'RP, ,..)  3E  18  Antenna Pattern  Simulation Geometry  3-D Geometry Data at Random Location  Time Series Constellation Data Elevation, Azimuth, Range  Methodology  I  31  NEC-BSC (UTD Based Numerical Electromagnetic Program)  31  me.Series Prediction Data  Post Data Processing Module  IE Statistics  Figure 3.1: Flow diagram of propagation simulation tool  power under blockage. We justify this a reasonable simplification that also allows us to compare 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 depth  W  Building depth  Figure 3.2: Simulation geometry  B u i l d i n g H e i g h t at a R a n d o m L o c a t i o n 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 H  b  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 separately 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 T h e 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 predictions. 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. P o s t D a t a P r o c e s s i n g 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\. A t 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 t , there exists a series of shadowing loss predictions {h,l2---,ln} n  predictions  {n,?"2, . . . , r „ } .  and signal strength  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  SatTrack  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 U N I X 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.  T h e 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 new / tools / softwareArchi ve. php#linux  from:  http://www.amsat.org/amsat-  Chapter 3. Methodology  21  * 1 second step 1 day + 5 seconds step 1 day 0 10 seconds step 1day o 5 seconds 5 days  ...8 $  1  a> 4 TO c  £  & ?  CD  $  ££  y  Q) o  ..  $ .  A  . . .  $  0^  10  20  30  40  i  50  60  70  80  90  Elevation angle (Degree) 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.  22  Methodology  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 d B m , and -PNoBiockage(#, 4>) i the received power without blockage s  in d B m . At the same frequency and through the same propagation path, both cases have the same free space propagation loss and atmospheric attenuation. T h e difference,  L^  (9,4>)  xcess  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(^) /') ~ NoB\ocL.ge(® > <t>) <  where PfjnderBlockage^' 0) -^NoBiockage(^' I f  p  i S  *  n  e  P  UnderBl>ckage(^^)  o  a  l s  w  e  n  t  n  e  r e c e  i  v e  P  [ ]  (3-2)  dB  d power at satellite under blockage in d B m , and  r  received without blockage in d B m .  d  ^ffi&go(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 ^> MJnderBlockagel"' Q) — "77 -MVormalizationFactor pSatellite (fi j~\ nNormalizcd/fl i\ _ NoBlockageV ' rNoBlockagcl '' 9) ~ p -'NormalizationFactor  f r  . ,N  / \ -°>  17  6  6  q  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  ( - )  ^NoBlockageC^i 0) = 4 7 T • C/NOBlockage  (3-8)  ^UndcrBiockagc(^' 0)  =  3  ^UnderBlockage  7  (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(#, 4 ) = GlJnderBlockage 1  _  ^NoBlockage  [dB]  (3-H)  Interpolation 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.  24  Methodology  180  180  Figure 3.4: Hemispherical radiation pattern Figure 3.5: Radiation approximating L P R antenna  pattern  of  A/4  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) = 2 c o s ( | • 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 P C ' 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  3.3.1  3.  Methodology  25  D a t a C o l l e c t i o n K i t - H a r d w a r e a n d 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 M H z ) The connection digram is shown as Figure 3.6. The laptop computer is connected with S C through RS232 serial port.  Subs'criber, 'Cbm'municatSr" LPR-Antenna  Mihi-Gircuits Low Noise-Amplifier,  ;'<Qfb R'ferfpr'm Data  Figure 3.6: Connection diagram  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 K i t (SDK) for the S C .  • ORBPerform ORBPerform supports global messaging via the O R B C O M M satellite system, and  Chapter 3. Methodology  Figure 3.7: L P R antenna  Figure 3.8: L N A  26  Figure 3.9: SC  runs performance test and V H F G P S antenna test functions.  It accesses the O R -  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 collection purpose, we utilize the Antenna  Test function to log the RSSI, Satellite ID and  Time information [23]. • Customized D a t a Collection Program Utilizing the S D K for S C , we developed a data collection program which allows the S C 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 S C . The source code for this program is given in Appendix-C.  3.3.2  Problems with D a t a Collection K i t and Further  Development  P r o b l e m 1 - Interference The first data collection was conducted in the roof of the MacLeod building ( M C L D ) 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 S C and the satellite. no useful data was collected.  As a result,  We carefully checked all the parameter settings on S C and  hardware connections and were assured that all are correct. We did data collection again, however S C 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 antenna with the same coaxial cable we used for data collection. The resolution bandwidth ( R B W ) 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 K H z ) [20].  The video bandwidth ( V B W ) 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 Amateur Radio ORBCOMM DL Land Mobile 117.975-137 M H z 138-144 M H z 137-138 M H z 144-148 M H z Clean, noise floor  Clean, noise floor  Strong signal from paging  Interference appears  is about -92 d B m  is about -92 d B m  service. Such as  occasionally. Spike  139.21 MHz(-65.16 dBm)  is at 140.21 M H z  140.21 MHz(-46.22 dBm)  (-84.77 dBm)  Detailed channel plan is unknown.  that there exist strong signals from the land mobile band (paging service) that are more than 40 d B 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 S C through desensitization of the receiver and intermodulation interference. O n the roof of the Macleod building, the paging signal can reach the antenna of S C through a distance of just several hundred metres of essentially 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 Weak Signal Strength In Vancouver, the elevation angles of O R B C O M M satellites are lower, and the ranges between satellite and S C 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 . T h e SC's sensitivity is specified as -118 d B m 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 S C 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. T h e low RSSI also limits the range of signal strength that can be used to characterize the influence of building blockage.  -80 — *  Measurement Satellite in view  -90 h  -100  ....  : W ;  E m w w or  %  It -110  Ll  -120  A  -130  -140  300  400  500  800  Time (x10 seconds) 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 ( L N A ) was added to the S C front end. The L N A used is ZFL-1000LN from Mini-Circuits (see Figure 3.8). Its working bandwidth is 0.1 - 1000 M H z . W i t h 15 Volt D C power supply, it can provide about 23 d B gain over 137-138 M H z frequency band. T h e noise figure is specified as 2.9 d B . For detailed specification, please go to www.minicircuits.com/model and search for ZFL-1000LN. A preselector was proposed to reduce interference. T h e filter proposed is a 2C137-1.5-  Chapter  3.  Methodology  29  4 A A from Lark Engineering. It can provide more than 40 dB attenuation at frequency higher than 139 M H z , 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. W i t h 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 km Satellite Altitude 825 User Elevation Angle  5 4800  Bps  Downlink Frequency  137.5  MHz  Transmit E I R P  12.0  dBw  Spreading Loss  -140.1  dBm  Atmospheric Losses  2  dB  Polarization Losses  4.1 (SC 2 d B axial ratio, subscriber linear)  dB  Multipath Fade Losses  5.0  dB  Satellite Pointing Loss  0.3 (5 degree off-nadir pointing)  dB  -4.2  dBm'  Area of an Isotrope Power at User Antenna Subscriber antenna G / T  dBw  -28.6 23  dB dB  Received  77.4  dB  36.8 (4.8 kbps)  dBH  40.6  dB  0  Data rate Received Idea  E /No b  E /N b  0  2  dB/K  -2.9  C/N  2  -143.7  L N A noise figure  L N A gain  3.3.3  Deg  User Data Rate  10.3 ( K T  5  BER)  dB  Measurement Location Selection  Three sets of valid measurements were collected at three locations in Vancouver. See T a . ble 3.3 for details. A n abstraction of the Richmond Centre and Marpole Residence sites are shown in Figures 3.11 and 3.12, respectively.  They are used in computer simulation to validate the  propagation simulation tool against measurement.  Chapter  Location  3.  Methodology  30  Table 3,3: Field measurement location description Date Time Terrain  Typical  Building Height Forestry Building(UBC)  Aug-30, 2004  15:27 - 17:00  Light Urban  20 m  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  Marpole  Figure 3.11: Richmond centre geometry  3.4 3.4.1  Validation of Computer  Figure 3.12: Marpole residence geometry  Simulation  Model  Purpose of Validation  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 3.4.2  31  Feature Selective Validation  The F S V technique was developed by the need for error determination in the validation of numerical models against experimental data. T h e 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. T h e components used are the Amplitude Difference Measure ( A D M ) , which compares the amplitudes and 'trends' of the two data sets, and the Feature Difference Measure ( F D M ) , which compares the rapid changing features.  T h e A D M and F D M are  them combined to form a Global Difference Measure ( G D M ) . T h e 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.  ADM = Y  ^  l o w l  ~  I l o w 2  \  (3.12)  Jmin fmax  FDM = 2 £  [FDi{f)  + FD (f) 2  + FD (f)} 3  (3.13)  fmin  FDi(f)  =  FD (f) 2  =  FDz(f)  =  \f  I  —f UFDl _ f  \f  h l 9 h l  (3.14) I (3.15)  h l 9  apD2 -1" I high2\ &FD3  \l"  ^  h % 9 h l  ^  in which —  1  fmax  1, Jmax  Jmin  •£  +  IW/)ll  (3-17)  f  J min frnax  a Di F  =  •£ 0Ci(/)l Jmax  Jmin  + lC (/)ll 2  (3-18)  j. fmax  « F 0 2 = ~z Jmax  _, Jmin  •£  + l4 fc2(/)0  (3-19)  [\Cghdf)\ + \C h2(f)\}  (3-20)  [\Ihighl(f) \  fl  f  Jmin  CXFD3 =  , Jmax  7  l  2 f  Jmin  • £ f  Jmin  9  Chapter 3. Methodology  32  howl and Ii 2 are the low pass component of data sets 1 and 2. T h e subscript low refers OW  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 ( G D M ) is then obtained as either a single figure of merit or as a point-by-point result: JmaT.  GDM =  \/(ADM(/))  2  + (FDM(/))  2  (3.21)  fmin or  GDM(/) = v/(ADM(/)) + (FDM(/)) 2  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 3.4.3  33  Validation Results  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 measurement 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.  T h e 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  Figure 3.13: Time series excess path loss (long duration)  35  Chapter 3. Methodology  36  * +  measurement simulation  *:  *  **  *  •*• *  +  * * *%  * *  * * * * * * * * *  3.5  3.502  * * *  3.504  * *  3.506 3.508 Time (second)  * #^  :  *  3.51  3.512  Figure 3.14: Time series excess path loss (short duration)  3.514  x 104  Chapter  20 I  3.  Methodology  37  Magnitude Response 1  1  1  1  1  1  1  r  -120 -  0  0.05  0.1  0.15  0.2  0.25 0.3 Frequency (Hz)  0.35  0.4  0.45  Figure 3.15: Magnitude response of low pass filter for A D M calculation  0.5  Chapter 3. Methodology  10  u  i  * — '' ..+  : H  38  1 * +  1  * +  +  . .  1 Measurement . Simulation  * ...» ........  h  It + Hh SI  ro £ 10  TO in vi o  * 1  *  +  i *  10  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 validation  39  Chapter  4  System Coverage i n Suburban and U r b a n 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 prediction 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 M H z 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 4.2.1  Influence of Wavelength Theoretical Background  Signal from a satellite can be received by user terminal through line of sight (LOS) propagation, 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 N  N  r  £  t o t a l  = E A 0  l o s  e-  jkiri  j=l where ^4i Ai Aj and A os  m  N  t  + Yl REiAie-  j k o r o  d  + £ TEjA - ^ j=l  + £  jk  je  TE A e-^ m  are spreading factors for L O S , reflected, transmitted and diffracted  rays; R, T and D are reflection, transmission and diffraction coefficients; r  n  along the n  th  ray; k  is the wave number of the n  th  n  (4.1)  Tm  m  m=l  ray. E  n  is the distance  is the incident field immediately  adjacent to the corresponding transmission, reflection or diffraction point. N  r  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  L O S 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  ({cotr  +  T  + ( 2  ^  ^-  W  cotr-^-^]F[ { c o t r  +  ^  +  2  f  7  ^  2  7  ^  i  r^-(0-0O]}  ^ V [ 2 ^ ( 0  + c o t [ ^ ± ^ ] F [  -  +  0 O ]  r ^ - ( 0  +  0')]})  where n=1.5 for 9 0 ° 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:  s,h  D  =  _ e  s i n (  i) .^  1 cos(^) - c o s ( ^ )  (4.4) 1 c o s ( £ ) - COSi • <t>+4>' >  It can be seen that as wavelength A increases, the diffraction coefficients increase and so does £ ^  when incident field E  %  o t a l  is the same.  Chapter  4.2.2  4.  System Coverage in Suburban and Urban Environments  42  Simulation Results and Analysis  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 G H z 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 M H z signals experience less excess path loss hence stronger RSSI than 1.3 G H z signals, which conforms with the theoretical prediction. The mean RSSI values for 138 M H z signals in light urban and heavy urban environments are -120.7 d B m and -123.7 d B m respectively, while the mean RSSI values for 1.3 G H z signals are -126.4 d B m and -130.8 d B m in light urban and heavy urban environments. Operating at 150 M H z rather than 1.3 G H z gives a 5-7 d B 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 d B and 5.3 d B in light urban and heavy urban environments, while those for 1.3 G H z signal are 7.5 d B and 7.3 d B respectively. 138 M H z signal is less sensitive to changes in the environment.  The  In link budget design,  coverage probability and shadowing margin are found according to the standard deviation of RSSI. The 138 M H z 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 threshold from -145 d B m to -95 d B m when there is a satellite in view. If the sensitivity of the S C is -125 d B m , 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 G H z 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 S C 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 d B m to -95 d B m for both 138 M H z and 1.3 G H z . If the sensitivity of S C is -125 d B m , at 138 M H z , SC is able to have one minute's signal constantly stronger than S C 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 S C 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 d B m RSSI level, 138 M H z signal has about 45 — 50% more coverage avaialble time. For the mean connection duration, at -125 d B m threshold, 138 M H z  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 G H z signal is very unlike to provide service at the -125 d B m RSSI level.  Chapter  4.  100ft  System Coverage in Suburban and Urban Environments  45  4 $. Z  -*-A-o-0-  "A \  A  \  \  \  y  \  1.3GHz Light Urban 138MHz Light Urban 1.3GHz Heavy Urban 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 G H z  Chapter  4.  System  Coverage  in Suburban  and  Urban  Environments  46  10  to ID ID  A  O CO X) TO  c  ^  .  ;  >  ..:. \  TO  ^  *...;... \  0) 2  :  \  10" ' 1  03 in O  A  j.  \  \  .A.  TO CL  ID  to a> o X CD  TO  _  2  S 10 ' CD D> TO  -*-A-o-0-  1.3GHz Light Urban 138MHz Light Urban 1.3GHz Heavy Urban 138MHz Heavy Urban  4::  "c  x  cu \  a.  10  15  j & 20  u 25  \ i_ 30  35  Excess Path Loss (dB) Figure 4.2: Complementary cumulative distribution function of excess path loss at 138 M H z and 1.3 G H z  Chapter  4.  System  Coverage  n '—r  in  Suburban  and  Urban  "1—I—r I  I  I  I  I I "  I  I  I  I  i  i  i i i i i  i i i i  *  -140  -135  1.3GHz Light Urban 138MHz Light Urban 1.3GHz Heavy Urban 138MHz Heavy Urban  i i i i i \ i i i i i i i i i  i  -145  -*-A-o-0-  47  Environments  -130  i  -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 G H z  Chapter 4. System Coverage in Suburban and Urban Environments  9 CO CO CO  Ab  o CO  c CO .c  4•C— D' -C O) X _co  W (Z>  a:  \ .\  70  \ \. .  i .. \  \ \  .... J ...  CD  E io  CD O) CO  '  CD c o CD Q-  \ \ A \  i i \  \ \  \  i  * .. \  <  > (  1  CO  sz  © I  9,  40  1 . 3 G H z Light Urban 1 3 8 M H z Light Urban 1 . 3 G H z H e a v y Urban 1 3 8 M H z H e a v y Urban  \  <*> i i.  \ *t \  \  50  i..  *  .  \ A  \  9  .  \  i  \ \  60  -*-A-o-0-  A  i \  80  A;  \ . \  c?  90  A  *  48  t  * \  1  4  » .i  A  \  \ \  30  \  6 <\*  : A  20  A  V ^\  10  A \  -145  -140  -135  -130  -125  -120  A  A  -115  -110  -105  -100  RSSI (dBm) Figure 4.4: Time share vs. RSSI threshold at 138 M H z and 1.3 G H z  -95  Chapter  4.3 4.3.1  4.  System  Coverage  in Suburban  and  Urban  Environments  49  Influence of Degree of Buildup 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  S i m u l a t i o n 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 connected to each other. Street width is about 10-15 metres.  Chapter  4.  System  Coverage  in Suburban  and  Urban  50  Environments  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 H  b  6 (10,15,20,25,30,35,40,45,50,55) metres.  Excess path loss vs. Mean building height H  b  Figure 4.5 gives mean excess path loss with different mean building heights H . Generally, b  more excess path loss is found with higher H . For H b  b  > 20m, there is a 1 dB increment in  excess path loss for every 10-metre increment in H . The standard deviation of excess path b  loss is shown in Figure 4.6. It is apparent that excess path loss at The standard deviation at  H =10 b  H =10 b  m is 3 dB less than that at  H =20 b  m is smaller than standard deviation at higher  H. b  m. 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 H  b  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 H , and Figure 4.6 gives the b  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 H  b  >20 m, there exists a linear relationship  between RSSI in d B m 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  P ancouver(h ) V  b  Chapter  4.  System  Coverage  in Suburban  and  Urban  Environments  51  and mean building height h as Equation 4.5. 0  Vancouver(^b) = -0.10923 • h - 119.36 b  [dBm]  In Equation 4.5, RSSI -FVancouver(hb) is mean RSSI in d B m and H  0  (4.5)  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  ! 3  Mean excess path loss  It  r **  *  35  40  K  10  *  *  *  0  5  10  15  20  25  30  45  Mean building height (m) Figure 4.5: Mean excess path loss at difference degree of buildup  50  55  Chapter  4. System Coverage in Suburban and Urban  +  *  Hh  r  *|e  H• V  *I  t *  Environments  53  *  o of excess path loss  +  oof RSSI  V  *  V  h  b  }t  2r 1 -  QI 10  :  I  15  I  I  20  25  ;  I  •  I  ;  -  I  I  I  30 35 40 Mean building height (m)  45  50  55  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  *  t  54  Mea n RSSI  *  *  K-  >It  *It  it it »  10  15  20  25  30 35 40 Mean building height (m)  45  Figure 4.7: Mean excess path loss at difference degree of buildup  50  55  Chapter  4.  System  Coverage  in  Suburban  and  Urban  Environments  Figure 4.8: Linear curve fitting for mean RSSI in urban area, Vancouver  55  Chapter  4.4  4. System Coverage in Suburban and Urban Environments  Variation of Coverage with  4.4.1  56  Latitude  Theoretical Background  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. O n 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 d B and 144.7 d B respectively. The difference between them is about 12 d B .  ^FreeSpacc = 10 • l o g  10  {^f  [dB]  r :  Distance between user and satellite  [metre]  A :  Wavelength  [metre]  (4.6)  B y 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 4 0 ° has least free space path loss and latitude 6 0 ° has the biggest.  At latitude 1 0 ° , 2 0 ° , 3 0 ° and 5 0 ° , free space path loss have  Chapter  4.  System  Coverage  in Suburban  and  Urban  Environments  57  very comparable statistical properties.  E l e v a t i o n A n g l e D i s t r i b u t i o n vs. L a t i t u d e In previous chapters, we have shown that elevation angle of satellite is important in determining 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 ° , 1 0 ° , 2 0 ° , 3 0 ° , 4 0 ° , 5 0 ° , 6 0 ° , 7 0 ° and 8 0 ° . At latitudes 0 ° , 1 0 ° , 2 0 ° , 3 0 ° , and 4 0 ° , the elevation angle can be up to 90 degrees. As latitude goes higher than 4 5 ° , the elevation angle for satellites in the four 45-degree inclination orbit planes can not reach 9 0 ° . A t latitude 4 0 ° , elevation distribution shows the biggest portion of high elevation angles. The complementary cumulative distribution function of 0 ° , 1 0 ° , 2 0 ° , 3 0 ° and 5 0 ° are very close, except the very high elevation angle part that is not available for latitude 5 0 ° . A t latitude 7 0 ° , 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  Simulation Results and Analysis  Simulations are completed under light urban environment (H^ = 20m) at latitudes 10°, 2 0 ° , 3 0 ° , 4 0 ° , 5 0 ° and 6 0 ° . For higher latitude such as 7 0 ° and 8 0 ° , 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 ° , 1 0 ° , 2 0 ° , 3 0 ° , 4 0 ° and 5 0 ° are very close. Even though latitude 4 0 ° 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. A t latitude 4 0 ° , the best signal strength is available while latitude 6 0 ° 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 5 0 ° , the free space loss statistics are very similar. A t 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° 2 0 ° 3 0 ° and 5 0 ° are very small. A n d the difference among the excess path loss statistics at different latitude are even smaller. Therefore, 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  4.5  4. System  Coverage  in Suburban  and Urban  Environments  59  Influence of T e r m i n a l A n t e n n a P a t t e r n  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 P (d)  can be expressed as:  r  P (d) r  = -PFreeSpace(d) • g (0, <p)  (4.7)  r  where -Ppi-cc-Space^) is the power at receiver before into antenna through free space propagation; g are the antenna gain factor as a function of three parameters at the receiving sites: r  (i) G (0,4>) the pattern of radiation received by the mobile unit's antenna, (ii) a is a loss r  factor depending on the angle of signal arrival, and (iii) p (8, <f>) P . D . F . of incoming waves' r  angle of arrival. Then g can be expressed as: r  (4.8)  where k\ is a constant for normalization. For land mobile satellite communication link with L O S propagation, p (9, 4>) is mainly r  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 G (9,(p) r  has more gain at higher elevation angles, from Equation 4.7 and Equation 4.8  the overall gain factor g  r  would be bigger and received power at antenna output P (d) r  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 p (9,<{>).. r  Chapter  4.5.2  4.  System  Coverage  in Suburban  and  Urban  Environments  60  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 G {8) T  = 2cos#, 8 G [0,7r/2].  The vertical plane of a A/4 monopole is in Figure 3.5 and mathematically expressed as G {8) r  = 2 c o s ( | • cosf?)/sinf?,  restrial wireless application.  8 £ [0,7r/2]. A/4 monopole antenna is widely used in terSimulations are conducted under suburban light urban and  heavy urban environment. By doing this comparison, we can quantitatively assess the performance 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 d B m  -120.7 d B m  -123.7 d B m  A/4 monopole  -122.9 d B m  -124.4 d B m  -125.2 d B m  5.5  3.7  1.5  S T D @ Suburban  S T D @ Light Urban  S T D @ Heavy Urban  4.23 dB  5.12 dB  5.33 d B  8.2 dB  8.4 d B  8.14 dB  A low-profile A/4  monopole  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 d B . 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. O n 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. most case is more than 45 degree.  This angle in  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. A n d 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 signals reaching the antenna through both L O S 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  Figure 4.9: Geometry of elevation angles at different latitude  62  Chapter  4.  System  Coverage  in Suburban  and  Urban  63  Environments  -*-  Latitude=10  -o- Latitude=20  -0- Latitude=30 —H-  \  •v  iL  -  \ •  -v- Latitude=60  \  N  \  A  Latitude=40  -A- Latitude=50  \ \ N  \  A .V. \ \  \ \ -k.  N  N  A 5 \ . \  \  s  ..A . A  .. . X \  <  A  N  \  v N  V  \M9  .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  _  40  50  60  40  50  60  Latitude (degree)  10  20  30 Latitude (degree)  Figure 4.11: Mean and standard deviation of free space path loss  Chapter  4. System Coverage in Suburban and Urban  Elevation angle (Degree)  Environments  65  Elevation angle (Degree)  Figure 4.12: Complementary cumulative distribution function of elevation angle at different latitude  Chapter  1  SS CO  4.  v = 4^  in Suburban  and  Urban  !  \  0.7  CO  Environments  -*-o-0—t-  ? X ....  0.8  CD  £  Coverage  0.9  'o co  "ro  System  66  Latitude=10 Latitude=20 Latitude=30 Latitude=40  -A-  I atitnHe=5n  -v-  Latitude=60  \&v \*  «  CO CO  •2  "\  0.5  "co CU  o X  Z  CO  \Y  0.3  ^\ \i  sz  •  S» CO  ,H  0.2  >  \ \ ^ \ Hv  •*—•  c  g  0.1  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 different antenna pattern  69  Chapter 5 C o n c l u s i o n s  5.1  a n d  R e c o m m e n d a t i o n  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 d B . • 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 d B . In heavy urban environments, this reduces to only 1.5 d B . We also examined the variation of coverage as a user in a light urban environment changes latitude. T h e 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 4 0 ° , about 15% of free space path loss is above 142 d B . At latitude 6 0 ° , 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° 2 0 ° 3 0 ° and 5 0 ° 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 O R B C O M M 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 antenna 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.  Case Specific S i m u l a t i o n w i t h D E M  Data  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 ( D E M ) 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 Symposium  on Electromagnetic  Proc.  2004  International  vol. 3, Aug. 2004, pp. 1011-1016.  Compatibility,  [2] "Propagation data required for the design of earth-space land mobile telecommunication systems ( I T U - 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 roadsode 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. L i n , "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 G H z using A C T S , " in  Proc.  IEE  ICAP'95,  [7] J . Butterworth, "Propagation measurement  vol. 2, A p r . 1995, pp. 381-386.  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," Communication Research Center, Ottawa, Ontario Canada, Tech. Note 723, Aug. 1984.  73  References  [9] C . Loo, "A statistical model for a land mobile satellite link," nol,  IEEE  Trans.  Veh.  Tech-  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.  A h n , and K . - C . Whang, "A channel model for nongeo-  stationary orbiting satellite system," in ference  Proc.  IEEE  47th  Vehicular  Technology  Con-  vol. 1, May 1997, pp. 41-45.  (VTC'97),  [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.  vol. 40, pp. 375-386, May 1991.  Technol,  [13] Y . Karasawa, K . Kimura, and K . Minamisono, "Analysis of availability improvement in L M S S 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 systems,"  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, A p r . 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," Antennas  and  Propagation,  IEE  Proc.  on  Microwaves,  vol. 2, pp. 554-558, Oct. 2002.  [17] C . Oestges and D . Vanhoenacker-Janvier, "Physical-statistical prediction of performance for land mobile satellite communication systems," Antennas  and  Propagation,  IEE  vol. 146, pp. 362-368, Oct. 1999.  Proc.  on  Microwaves,  74  References  [18] R. Zabela and C . Bostian, "Measurements of building penetration by low orbit satellite signals at  -  VHF,"  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 M H z , " in Radio  [20]  and  Personal  ORBCOMM  System  Communications,  Overview  Proc. Dec.  - (Doc.  NO.  Sixth 1991,  International pp.  Conference  on  Mobile  83-86.  A80TD0008),  E ed.,  O R B C O M M Global,  L . P . , Dulles, Virginia, U S A , Feb. 1999. [21] M . Bester,  [22]  Numerical liminary  The  Satellite  Electromagnetics Draft),  Tracking  Code  Program  SatTrack  - Basic  Scattering  (V3.1),  Code  Mar. 1995.  V4-2  User's  Manual  (Pre-  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, U S A , Feb. 2000. [24] C . A . Balanis,  Advanced  Engineering  Electromagnetics.  Wiley, 1989, pp. 782-783.  75  Appendix A Sample of SatTrack Output S a t e l l i t e Name, Date, Time, Azimuth [deg], Elev[deg], Range[km], Sun Ang[deg], Doppler [kHz], Loss[dB], Phs, V A6, F r i 02Apr04, 12 00 00, 302 8, 5. 9, 2661. 8, 84. 6, + 1.49, 144. 2, 60, D A6, F r i 02Apr04, 12 00 10, 304 2, 6. 3, 2631. 8, 84. 7, +1. 44, 144. 1, 60, D A6, F r i 02Apr04, 12 00 20, 305 6, 6. 6, 2602.9, 84. 8, +1. 37, 144. 0, 61, D A6, F r i 02Apr04, 12 00 30, 307 0, 6. 9, 2575.3, 85. 0, +1. 31, 144. 0, 61, D A6, F r i 02Apr04, 12 00 40, 308 4, 7. 2, 2549.0, 85. 1, +1. 25, 143. 9, 62, D A6, F r i 02Apr04, 12 00 50, 309 9. 7. 5, 2524. 1, 85. 2, +1. 18, 143. 8, 62, D  C4, F r i 02Apr04, 12 05 50, 338 7, 13 • 9, 2094 • 7, 89 • 4, +2 .49, 142 • 2, 188, D C4, F r i 02Apr04, 12 06 00, 339 8, 14 .7, 2043 • 9, 90 • 2, +2 .45, 141 .9, 188, D C4, F r i 02Apr04, 12 06 10, 341 0, 15 • 6, 1994 • 0, 91 • 0, +2 .41, 141 • 7, 189, D C4, F r i 02Apr04, 12 06 20, 342 3, 16 .4, 1945 .0, 91 • 8, +2 .36, 141 • 5, 189, D C4, F r i 02Apr04, 12 06 30, 343 6, 17 • 3, 1897 • 0, 92 • 7, +2 .31, 141 • 3, 189, D C4, F r i 02Apr04, 12 06 40, 345 0, 18 • 2, 1850 .1, 93 • 5, +2 .26, 141 • 1, 190, D C4, F r i 02Apr04, 12 06 50, 346 5, 19 • 1, 1804 .2, 94 • 4, +2 .20, 140 • 9, 190, D C4, F r i 02Apr04, 12 07 00, 348 1, 20 • 1, 1759 • 7, 95 • 3, +2 • 14, 140 • 6, 191, D C4, F r 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  P r e d i c t i o n  B.l  L  E D a t a  U p d a t e  R u n n i n g  a n d  P r o c e d u r e  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  Otherwise, go to step 4.  \SatTrack\data\.  Step-4 Delete the Orbcomm data currently in file  tlex.dat.  Step-5 Go to directory and run Maketlex. This step will update the satellite orbit data in  Step-6 Open  tlex.dat  tlex.dat  file with new  tle.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 G o to direcoty  \SatTrack\data\,  simulation settings.  and edit the  batch.dat  file with your required  Appendix  Step-3  B.  SatTrack  Go to direcory  TLE  \SatTrack\run\,  Data  Update  and  Prediction  Running  Procedure  77  and run Makepassesx. Choos 'ocm' when you are  asked to input satlist name.  Step-4 R u n 'passesx'. A l l 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 D O S environment utilizing S D K in C programming language.  #include  "kme_lib.h"  #pragma _ s e c t i o n B=USER #pragma . s e c t i o n B  unsigned char s i g n a l _ l e v e l [ 1 1 0 0 0 ] ;  void  main(void)  { i n t count=0; int  no_of_pnts=10000;  TIME_INF0 *time; / / s e t a p o i n t e r t o time s t r u c t u r e d e f i n e d i n SDK  signal_level[count]=255;  / / w r i t e FF i n the f i r s t  byte  count=count+l; signal_level[count]=238;  / / w r i t e EE i n the second byte, as i n d i c a t o r of b e g i n n i n g  count=count+l;  while (count < no_of_pnts)  •C signal_level[count+1]=  g e t _ s a t _ n o ( ) ; //get s a t e l l i t e ID  signal_level[count+2]=  get_adcon_data(0);  //get RSSI  Appendix  go_wait(10UL,SEC_UT); //wait  79  C . Data Collection Code  a short p e r i o d  count=count+2; >  signal_level[count]=255;  / / w r i t e FF i n the byte b e f o r e  last  count=count+l; signal_level[count]=238; count=count+l;  exit_user_apl(); }  / / w r i t e EE i n the l a s t byte, as i n d i c a t o r of end  

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