Elevation Dependent Shadowing Model for Mobile Communications via High Altitude Platforms in Built-Up Areas

1078 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL 56, NO 4, APRIL 2008 Elevation Dependent Shadowing Model for Mobile Communications via High Altitude Platforms in Built-Up Areas Jaroslav Holis, Student Member, IEEE, and Pavel Pechac, Senior Member, IEEE Abstract—An empirical propagation prediction model is described for mobile communications from high altitude platforms (HAPs) in different types of built-up areas The model introduced here is defined as a function of the angle of elevation The target frequencies are selected from the to GHz frequency band prospective for 3G and 4G mobile systems, namely at 2.0, 3.5, and 5.5 GHz This new HAP model recognizes two cases—line of sight (LOS) and non-line of sight (NLOS) between a HAP and a user at street level The simulation of the urban environment is based on a statistical approach Additional shadowing path loss is calculated using the uniform theory of diffraction for NLOS conditions Normal distribution of the additional shadowing path loss was distinguishable from the simulation results The shadowing path loss is defined as a function of the elevation angle The results of the empirical model developed for idealized conditions are verified by measurements taken from a remote-controlled airship in different types of urban environment Close correlation was achieved between the theoretical model and the experimental data The HAP elevation dependent shadowing model is easy to implement and can be used for realistic planning and simulations of mobile networks provided via HAPs in built-up areas Index Terms—Empirical model, high altitude platforms (HAPs), propagation, wireless communications I INTRODUCTION VER THE past 10 years, mobile communications have changed the world more than any other area of technical achievement One weak point of mobile networks is the relative ease with which they can be disabled by disasters or terrorist attacks High altitude platforms (HAPs) could provide a possible alternative to the terrestrial provision of mobile services A suitable HAP is usually considered to be a quasi-stationary unmanned vehicle in the stratosphere at an altitude of between 17 and 22 km [1], [2] It should maintain its position within a sphere having a radius of 0.5 km [3] HAPs can combine the benefits of both terrestrial and satellite communications In particular, they have low free space loss (FSL) when compared to satellites and limited shadowing at high elevation angles, i.e they provide good coverage when compared to terrestrial net- O Manuscript received July 13, 2007; revised November 28, 2008 This work was supported in part by the Czech Ministry of Education, Youth and Sports within the framework of the OC092-COST Action 297 and MSM 6840770014 projects The authors are with the Department of Electromagnetic Field, Faculty of Electrical Engineering, Czech Technical University in Prague, Praha 6, Czech Republic (e-mail: holisj1@fel.cvut.cz; pechac@fel.cvut.cz) Digital Object Identifier 10.1109/TAP.2008.919209 works in dense urban areas A major advantage of using HAPs is the low cost of deployment and, especially in the event of a disaster, their rapid deployment The principal mobile network prospective for HAPs involves 3G systems and, potentially, mobile WiMAX systems The frequency spectrum for 3G mobile networks (around GHz) is also allocated to HAPs [3], [4] 3G networks could be replaced by a Mobile WiMAX operating in a frequency spectrum between and GHz [5] A simple FSL propagation model is unsuitable for system simulations of mobile systems provided via HAPs in urban areas An empirical roadside shadowing model (ERS) [6] or an empirical model for satellites [7] is used in order to achieve a more realistic approach to HAP propagation prediction A stochastic modeling approach, which can be utilized for coverage planning of HAP communication systems, is introduced in [8], [9] A propagation model for building blockage for satellite mobile systems was presented in [10] The results of deterministic wideband channel modeling of a satellite propagation channel with building blockage are shown in [11], [12] The impact of vegetation on the propagation environment for terrestrial and satellite mobile communications is demonstrated in [13] Complex information about propagation effects in megacells is provided in [14] An overview of channel modeling for HAPs is given in [15] An elevation dependent propagation model is required for HAP scenarios This type of model plays a key role in realistic studies of mobile services from HAPs The propagation model presented in this paper is based on simulations using a randomly generated urban environment The theoretical approach is partly verified by measurements using a remote-controlled airship The aim of this paper is to introduce an HAP shadowing model defined as a function of the elevation angle in different types of built-up areas and the prospective for HAP mobile applications in the 2–6 GHz frequency band, namely at 2.0, 3.5, and 5.5 GHz This model can be used for radio network planning of mobile systems provided from HAPs A comprehensive modeling approach based on stochastically generated urban environment and ray tracing was introduced in [16] The fade distribution can be derived as an output according to specific parameters that describe the scenario The statistical distribution of building heights, street widths, the azimuth, etc as well as the position of the mobile antenna within the scenario can be defined The model presented in this paper is based on the same principles, but for the urban environment the statistical ITU-R Rec P.1410 model [17] was utilized in order to 0018-926X/$25.00 © 2008 IEEE HOLIS AND PECHAC: ELEVATION DEPENDENT SHADOWING MODEL FOR MOBILE COMMUNICATIONS VIA HAPS describe different types of built-up areas in general terms Moreover, using very extensive simulations for all possible locations of a mobile terminal within the modeled area, the line of sight (LOS) probability as well as the shadowing were derived as a closed form expression By this method path loss can be calculated as a function of the elevation angle and can be directly used in system level simulations Section II presents the propagation modeling approach The simulation results and the shadowing model are described in Section III The propagation model defines the probability of LOS connections at street level and predicts an additional loss due to the shadowing effects of buildings using a statistical distribution Finally, Section IV deals with the verification of the model by measurements 1079 TABLE I PARAMETERS OF THE ITU-R P.1410 MODEL FOR SELECTED ENVIRONMENTS II PROPAGATION MODELING APPROACH A Statistical Model of Building Deployment As was mentioned above, a randomly generated urban environment was utilized to simulate different types of built-up areas The statistical ITU-R Rec P.1410 model [17] was adopted for building deployments The application of the statistical model was motivated by its universality and simplicity The great advantage of this model is that the city can be modeled without specific information concerning building shapes and distribution The statistical model requires only three empirical parameters describing the built-up area The ratio of land area , the mean number covered by buildings to total land area , and a parameter determining of buildings per unit area The parameter a ranges from building height distribution 0.1 to 0.8 and ranges from 750 to 100, respectively The parameter parameterizes the Rayleigh statistic distribution of building heights where represents the most frequent building height The Probability Density Function (PDF) for the building heights based on the Rayleigh distribution is (1) where denotes a probability that the building height is equal to in the given urban environment The ratio of land area can easily be obtained from 2D city covered by buildings is the plans The mean number of buildings per unit area least important parameter since it has a minor impact on the final simulation results presented below The buildings are generated with random heights based on the Rayleigh distribution This guarantees a realistic approach when analyzing the shadowing conditions in terms of statistics, although the buildings are deployed in an unrealistic regular grid Four different types of environment were selected for the scenarios presented here: 1) Suburban area; 2) Urban area; 3) Dense urban area; 4) Urban high-rise area Table I summarizes the parameters of the statistical ITU-R Rec P 1410 model used to define the different types of en- Fig Geometry of basic LOS and NLOS scenarios vironment ranging from suburban districts to city centers with skyscrapers It must be noted that in some references the building height density is shown to be closer to log-normal distribution than to Rayleigh [8], [14] Information on distributions of building heights can be found in [14], [17]–[19] B Simulation Method An urban area with dimensions of by km was considered for the analysis The resolution of building deployment for the simulations was equal to m This fine resolution, a sufficiently large area, and the extent of the simulation ensure that the results are not dependent on a specific case of randomly generated building heights The simulations were divided into two cases First the LOS probability in the streets was analyzed as a function of an elevation angle for different types of built-up area Second, additional path loss due to shadowing effect of buildings was analyzed using the uniform theory of diffraction (UTD) [20], [21] Building rooftops were modeled as dielectric wedges with a relative permittivity equal to Rooftop diffraction loss was calculated for both vertical and horizontal polarizations separately A more detailed ray tracing could not be applied here since the statistical model of the environment does not include realistic street structures with street canyons etc Fig illustrates the basic geometry covering both LOS and NLOS cases The calculations were made for azimuth angles ranging from 0–360 with a variation of degrees Initially, buildings were randomly generated using the statistical model Then the position of the HAP was determined for each point on the streets in 1080 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL 56, NO 4, APRIL 2008 TABLE II PARAMETERS FOR LOS PROBABILITY CALCULATION (2) Fig LOS probability in the streets as a function of the elevation angle for selected environments a fine grid for a given elevation and azimuth angles in order to simulate and analyze an extremely large number of scenarios The LOS probability for a specific elevation angle was calculated as the median value of the data obtained for all azimuth angles In this way the results are independent of the azimuth angle, since in the real world buildings are not usually located in a regular structure The simulations were performed for the full range of elevation angles from to 89 degrees III NEW PROPAGATION MODEL The simulation results can be divided into two cases First, the probability of LOS connections in the streets is shown In this case the free space loss (FSL) model can be used for the mean path loss calculation Second, the additional path loss due to the shadowing effect of buildings must be considered for the NLOS connections The realistic elevation angles for HAP mobile applications in cities range from 60 to 90 (the HAP is situated at the zenith above the mobile terminal), but lower elevation angles are not excluded as they might be important, for example, in terms of interference or for other studies For instance, the distance between the user and a sub-platform point on the ground (the point vertically below the HAP) is about 211 km for an altitude of 22 km (including the impact of the earth curvature) and about 168 km for an altitude of 17 km, if the elevation angle is equal to degrees A LOS Probability in the streets as a function of The LOS probability the elevation angle was obtained for the four environments, see Fig A simple function was then found to approximate the simulated data in Fig (2) where is the probability of LOS in percent, is an elevation angle in degrees and , , , , are the empirical parameters given in Table II for the four typical environments Parameters for an environment described by the arbitrary values of , , of the ITU-R Rec P 1410 statistical model could also easily be derived from the simulation results To a certain extent the results can be compared with the roadside building-shadowing model [6], which uses the same Rayleigh distribution of building heights It gives the probability of the link blockage as a function of the elevation angle, the azimuth angle relative to street direction, and the distance of the mobile terminal from the buildings, while the model (2) is based on statistics covering all possible positions of the mobile terminal within the area Appropriate parameters for both models were selected to define several test cases which would enable tentative comparisons As expected, both models give similar results B Additional Shadowing Loss This section presents the impact of the shadowing effects of buildings on NLOS connections The great benefit of HAP stations as compared to satellites is that the relatively short path length also enables NLOS links between the mobile station and the HAP station Fig illustrates an example of the simulation results—a normalized histogram for additional rooftop diffraction loss at 2.0 GHz for vertical polarization at an elevation angle of 70 Normal distribution can be distinguished from the figure The PDF for the normal distribution fitted to the simulated data is also shown in Fig The PDF of the normal distribution can be written as (3) where is normalized probability, the mean value in dB, and the standard deviation in dB For the specific case in Fig and The cumulative distribution functions (CDF) for the simulated data are depicted in Fig for elevation angles equal to 85 , 80 , 70 , and 50 The dashed curve in Fig illustrates the CDF of normal distribution fitted to the simulated data The close conformity between the simulated data and the normal distribution is distinguishable from this figure The CDF for elevations between 10 and 50 are very similar to the 50 curve so they have not been included in Fig in order to maintain clarity Parameters of the normal distribution (mean value and standard deviation ) for elevation angles ranging from to 89 are depicted in Fig All the parameters are given as an averaged HOLIS AND PECHAC: ELEVATION DEPENDENT SHADOWING MODEL FOR MOBILE COMMUNICATIONS VIA HAPS Fig Normalized histogram and PDF of shadowing loss at 2.0 GHz for vertical polarization in a dense urban area Fig CDF for shadowing loss at 2.0 GHz for vertical polarization and for a range of elevation angles (solid line—simulation results, dashed line—CDF of fitted normal distribution) value of vertical and horizontal polarizations in the following stages The results presented in Figs 3–6 were obtained for a dense urban area Simulations for the other selected environments were also performed The same results for additional rooftop diffraction loss were achieved, because the incidence angle (see Fig 1) depends much more on the elevation angle, which was calculated for each point in the streets independently, than on building heights This means that the same range of angles was included in the UTD wedge diffraction geometry regardless of the environment; the impact of street widths was also insignificant The type of built-up area only determines the number of NLOS points in the scenario, i.e., the LOS probability Table III presents the normal distribution parameters for an elevation angle of 70 and a carrier frequency equal to 1081 Fig Normal distribution parameters for shadowing loss as a function of the elevation angle for vertical and horizontal polarization Fig Normal distribution parameters as a function of an elevation angle for carrier frequencies equal to 2.0, 3.5, and 5.5 GHz TABLE III PARAMETERS OF NORMAL DISTRIBUTION FOR SELECTED ENVIRONMENTS ,2 ) (F = GHZ = 70 2.0 GHz for the four environments Only the suburban area has slightly different parameters, but these are insignificant The simulations and analysis were also performed for the 3.5 and 5.5 GHz frequencies It is obvious that a higher shadowing loss was recorded at these higher frequencies Fig presents the parameters of normal distribution for three different carrier frequencies and elevation angles ranging from to 89 1082 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL 56, NO 4, APRIL 2008 TABLE IV PARAMETERS OF (4) FOR F : = GHZ, ALL ENVIRONMENTS TABLE V : PARAMETERS OF (4) FOR F = GHZ, ALL ENVIRONMENTS TABLE VI PARAMETERS OF (4) FOR F : = 5 GHZ, ALL ENVIRONMENTS Fig CDF of a shadowing model for a dense urban environment at a frequency of 3.5 GHz The data were divided into two intervals of the elevation and , in order to angle, approximate the simulation results by a simple function A simple fractional rational function was derived as a best fit to simulation results to approximate the mean value as well as standard deviation of the normal distribution (4) where is the elevation angle in degrees and , , are empirical parameters The approximations are shown in Fig by continuous lines The empirical parameters are summarized in Table IV for a carrier frequency of 2.0 GHz, in Table V for 3.5 GHz, and in Table VI for a carrier frequency equal to 5.5 GHz The parameters for other frequencies in the to GHz range can be obtained as an interpolation of the parameters presented in Table IV–VI Parameters from Tables II–VI should be used with the given decimal points precision in all calculations C HAP Shadowing Model Finally, the path loss in a built-up area can be expressed in dB as LOS NLOS where shadowing in dB as a function of the elevation angle It is calculated using the normal distribution parameterized by (4) and appropriate empirical parameters from Table IV–VI Because of the need for realistic system-level modeling of mobile systems, and in dB are added as a locarandom components tion variability utilizing the log-normal distribution with a zero mean Based on the measurement results presented in Section IV below and in relation to [14], the standard deviation of the location variability is from to dB for LOS connections and from to 12 dB for NLOS connections As an example of the practical implementation of (5), (6) shows the generation of random shadowing loss at GHz for an elevation angle of LOS NLOS (7) where is the distance in km, is the elevation angle in degrees and the generates random numbers using the normal distribution with the mean and the standard deviis a standard Matlab function ation in dB Another option is to express the elevation dependent shadowing model in the form of CDF as (5) is the free space loss which can be calculated as (6) is the distance between the transmitter and the rewhere ceiver in km and frequency in GHz represents a random (8) and represent the shadwhere is probability in percent, owing loss in dB, is the mean value in dB and the standard deviation in dB of the normal distribution by (4), and is the line of sight probability at street level by (2) As an example of the type of calculation Fig shows a CDF calculated from (8) for 3.5 GHz, for a dense urban environment as defined in Table I, and elevations of 80 , 70 , 50 , and 60 HOLIS AND PECHAC: ELEVATION DEPENDENT SHADOWING MODEL FOR MOBILE COMMUNICATIONS VIA HAPS 1083 Fig Path loss at 2.0 GHz measured in front of a flat roof building during an airship flyover Fig Path loss measured in front of saddle roof building during an airship flyover The carrier frequency was equal to 5.5 GHz IV VERIFICATION BY MEASUREMENT is accompanied by a significant fading The close conformity between the measurements and the model can be distinguished in Fig Fig illustrates a comparison between the model and the measurement at a frequency of 5.5 GHz in front of a saddle roof building A high level of conformity was observed here as well The measurements proved that the shadowing path loss estimation based on the UTD presented in Section II was adequate These methods of built-up area modeling and the calculation of additional shadowing loss using wedge diffraction may appear over-optimistic Different roof profiles occur in real cities and the model only employs the shortest path between a HAP station and a mobile terminal That is why measurements were taken from a low altitude remote-controlled airship [22] (9 m long) in order to verify the feasibility of the simplifications employed The signal was transmitted from 26 dBm CW generators at frequencies of 2.0, 3.5, and 5.5 GHz using patch antennas situated at the bottom part of the airship gondola A spiral broadband antenna was used to receive the transmitted signal at street level Several trials were performed in the city of Prague The receiver station was located in real conditions in front of a building on the street The airship flew across the rooftops at an altitude of about 150 m The low altitude enabled a wide range of elevation angles (20 –90 ) to be obtained Measurements were taken using different types of buildings and roofs Two samples per second were recorded at each frequency and the measured data were compared with the calculations The position of the airship was logged using the Global Positioning System (GPS) The 3D track of the airship was restored using the GPS data Possible measurement errors caused by the roll and pitch of the airship (changing the radiation patterns of the transmitting antennas) were below about dB The measured path loss was compared with the theoretical model described in Section II-B Additional shadowing loss was calculated as rooftop diffraction loss for a given geometry using the UTD wedge diffraction For the UTD calculations the buildings were modeled as idealized blocks, in the same way as in the simulations described above Fig presents the measured path loss from a trial in front of a flat roof building at 2.0 GHz The modeled mean path loss is depicted by a dashed curve The shadowing effect of the building V CONCLUSION The narrowband elevation dependent shadowing model was introduced for mobile systems provided from high altitude platforms in built-up areas in the 2–6 GHz frequency band prospective for 3G and 4G mobile systems The probability of LOS connections between the HAP and a mobile station and the additional shadowing path loss for NLOS connections is presented as a function of the elevation angle The probability of LOS is defined for four different types of built-up area, from suburban to high-rise urban Simple empirical formulas derived from extensive simulation results enable easy implementation of this model as a radio network planning tool for system-level simulations and 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pp 1448–1461, Nov 1974 [21] R J Luebbers, “Finite conductivity uniform GTD versus knife edge diffraction in prediction of propagation path loss,” IEEE Trans Antennas Propag., vol 32, no 1, Jan 1984 [22] [Online] Available: www.airshipclub.com Jaroslav Holis (S’05) received the M.Sc degree in radio electronics from the Czech Technical University in Prague, Czech Republic, in 2005, where he is currently working toward the Ph.D degree His research interests are focused on physical layer of 3G and 4G mobile systems and on radiowave propagation Pavel Pechac (M’94–SM’03) received the M.Sc degree and the Ph.D degree in radio electronics from the Czech Technical University in Prague, Czech Republic, in 1993 and 1999, respectively He is currently a Professor in the Department of Electromagnetic Field, Czech Technical University in Prague His research interests are in the field of radiowave propagation and wireless systems  ... elevation dependent shadowing model was introduced for mobile systems provided from high altitude platforms in built-up areas in the 2–6 GHz frequency band prospective for 3G and 4G mobile systems The... PECHAC: ELEVATION DEPENDENT SHADOWING MODEL FOR MOBILE COMMUNICATIONS VIA HAPS Fig Normalized histogram and PDF of shadowing loss at 2.0 GHz for vertical polarization in a dense urban area Fig CDF for. .. PECHAC: ELEVATION DEPENDENT SHADOWING MODEL FOR MOBILE COMMUNICATIONS VIA HAPS describe different types of built-up areas in general terms Moreover, using very extensive simulations for all possible