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Chapter 4 Propagation in Built-up Areas 4.1 INTRODUCTION Having looked at how irregular terrain aects VHF and UHF radio wave propagation and the eects of multipath, we are now in a position to discuss propagation in built-up areas. This chapter will deal principally with propagation between base stations and mobiles located at street level; propagation into buildings and totally within buildings will be discussed later. Although losses due to buildings and other man-made obstacles are of major concern, terrain variations also play an important role in many cases. Within built-up areas, the shadowing eects of buildings and the channelling of radio waves along streets make it dicult to predict the median signal strength. Often the strongest paths are not the most obvious or direct ones and the signal strength in streets that are radial or approximately radial with respect to the direction of the base station often exceeds that in streets which are circumferential. Figure 4.1 is a recording of the signal envelope measured in a vehicle travelling along two city streets. For the ®rst 65 m the street is radial; the Rayleigh fading is clearly observed along with the increase in mean level at intersections. The vehicle then turned into a circumferential street, where the mean signal strength is a little lower and the fading pattern is somewhat dierent. In suburban areas there are fewer large buildings and the channelling eects are less apparent. However foliage eects, often negligible in city centres, can be quite important. Generally, the eects of trees are similar to those of buildings, introducing additional path losses and producing spatial fading. Estimation of the received mobile radio signal is a two-stage process which involves predicting the median signal level in a small region of the service area and describing the variability about that median value. Quantifying the extent to which the signal ¯uctuates within the area under consideration is also a problem in which there are two contributing factors. Short-term variations around the local mean value will be discussed in Chapter 5 and are commonly termed multipath, fast fading or Rayleigh fading. Longer-term variations in the local mean are caused by gross variations in the terrain pro®le between the mobile and the base station as the mobile moves from place to place and by changes in the local topography. They are often The Mobile Radio Propagation Channel. Second Edition. J. D. Parsons Copyright & 2000 John Wiley & Sons Ltd Print ISBN 0-471-98857-X Online ISBN 0-470-84152-4 termed slow fading and, as mentioned in Chapter 3, the characteristics can be described by a lognormal statistical distribution. 4.2 BUILT-UP AREAS: A CLASSIFICATION PROBLEM The propagation of radio waves in built-up areas is strongly in¯uenced by the nature of the environment, in particular the size and density of buildings. In propagation studies for mobile radio, a qualitative description of the environment is often employed using terms such as rural, suburban, urban and dense urban. Dense urban areas are generally de®ned as being dominated by tall buildings, oce blocks and other commercial buildings, whereas suburban areas comprise residential houses, gardens and parks. The term `rural' de®nes open farmland with sparse buildings, woodland and forests. These qualitative descriptions are open to dierent interpretations by dierent users; for example, an area described as urban in one city could be termed suburban in another. This leads to doubts as to whether prediction models based on measurements made in one city are generally applicable elsewhere. There is an obvious need to describe the environment quantitatively to surmount the unavoidable ambiguity embodied in the qualitative de®nitions which can arise from cultural dierences and subjective judgement. To illustrate the argument, Figure 4.2 shows building height histograms for two 500 m Ordnance Survey (OS) map squares in central London. In qualitative terms both areas would be classed as dense urban. It is obvious that the percentage of square A occupied by tall buildings is much greater than the percentage of square B, so a higher path loss value would be expected. In practice it is higher by 8± 10 dB [1]. 72 The Mobile Radio Propagation Channel Figure 4.1 Recording of signal strength in an urban area. 4.2.1 A classi®cation approach In situations of practical interest, the environment can be regarded as composed of many dierent mutually independent scatterer classes or types. Features such as buildings and trees are common and a town might appear as a random collection of buildings, each building being a scatterer. Likewise a forest appears as a random collection of trees. If the statistical properties of groups or clusters of individual scatterers are known, as well as the scatterer population per group, then it is possible to derive quantitative descriptions of the environment using the statistics [2]. Propagation in Built-up Areas 73 Figure 4.2 Building height histograms for central London: (a) Soho area, (b) Euston area. An environment classi®cation method can be based on this approach. Any given mobile radio service area can be viewed as a mixture of environments (e.g. a mixture of urban, suburban and rural localities). Following OS descriptions, the service area can be divided into squares of dimension 500 m6500 m. An individual square is then regarded as a sample of an ensemble of composite environments with the ensembles described by dierent terrain type and land cover. Although sample cells in an ensemble are not identical, they are suciently similar to allow a meaningful statistical description. When considering the eects of the environment, six factors are useful in classifying land usage: . Building density (percentage of area covered by buildings) . Building size (area covered by a building) . Building height . Building location . Vegetation density . Terrain undulations Using some or all of these factors, various researchers have devised classi®cations for the environments in which they carried out their experiments. 4.2.2 Classi®cation methods: a brief review Kozono and Watanabe [3] working in Tokyo in 1977 attempted a quantitative description of the urban environment as part of their investigation into the in¯uence of buildings on received mean ®eld strength. They proposed four parameters: . Area factor of occupied buildings, a . Extended area factor of occupied buildings, a H . Building volume over a sampled area, b . Building volume over an extended area, b H A sampled area, based on the Japanese community map, is a circle of radius 250 m. The extended area extends the sampled area towards the base station by a 500 m6500 m area along the straight line joining the base station to the sampled area. In their study into the in¯uence of buildings on the mean received signal strength, they concluded that although b often correlated better with the median received signal, a was more suitable since it is easier to extract from the maps. Ibrahim and Parsons [4], characterising the test areas for their experiments in inner London, introduced two parameters: land usage factor L and degree of urbanisation factor U. Land usage factor L is de®ned as the percentage of the 500 m6500 m test square that is covered by buildings, regardless of their height. This is essentially the same as the factor a used by Kozono and Watanabe. Good correlation was observed between the path loss value and L. Degree of urbanisation factor U is de®ned as the percentage of building site area, within the test square, occupied by buildings having four or more ¯oors. The decision to use four ¯oors as the reference was taken after plotting the cumulative frequency distribution of the building area against the number of ¯oors, for a large number 74 The Mobile Radio Propagation Channel of OS map squares. Comparison with the propagation loss from a base station to a mobile moving in the square revealed that the percentage of buildings having four or more ¯oors correlated best with the measured propagation data. The factor U may vary between zero and 100%; a value approaching zero indicates a suburb whereas a value approaching 100% indicates a highly developed urban area. British Telecom [5] proposed a ten-point land usage categorisation based on qualitative descriptions. This scale is shown in Table 4.1. These categories, though comprehensive, can be interpreted dierently by other service providers. Table 4.2 shows how the BT categories compare to those employed by other organisations [6±9]. The comparisons in Table 4.2 clearly indicate the fallibility of employing mainly qualitative descriptions in classifying land use within mobile radio service areas. In Germany, built-up areas are classi®ed under one category, whereas in Britain and Japan they come under three broad classes: suburban, urban and dense urban. Experiments have shown, however, that these three categories do not cause the same level of signal attenuation and it would therefore be inappropriate to compare results obtained in built-up areas in Germany with those collected in the UK. A more detailed description of land use in Germany would be required, and this would be Propagation in Built-up Areas 75 Table 4.1 British Telecom categories of land usage Category Description 0 Rivers, lakes and seas 1 Open rural areas, e.g. ®elds and heathlands with few trees 2 Rural areas similar to the above but with some wooded areas, e.g. parkland 3 Wooded or forested rural areas 4 Hilly or mountainous rural areas 5 Suburban areas, low-density dwellings and modern industrial estates 6 Suburban areas, higher-density dwellings, e.g. council estates 7 Urban areas with buildings of up to four storeys, but with some open space between 8 Higher-density urban areas in which some buildings have more than four storeys 9 Dense urban areas in which most of the buildings have more than four storeys and some can be classed as skyscrapers (this category is restricted to the centre of a few large cities) Table 4.2 Comparisons of BT and other land use categories BT (UK) Germany BBC (UK) Denmark Okumura (Japan) 0 4 ± ± Land or sea 1210,1,2± 2311,2± 3214± 4 2, 3 1 ± Undulating 5 1 2 3 Suburban 6 1 2 6 Suburban 7 1 3 7 Urban 8 1 3 8 Urban 9 1 4 9 Urban more expensive in terms of cost and time. The need for a more accurate and universal standard of categorisation is therefore very apparent, particularly now that the pan- European mobile radio system GSM is in widespread use and third-generation systems have been planned. Some years ago the derivation of land usage data involved costly and time- consuming manual procedures. Now it is possible to use geographic information systems (GIS) where digital database technology indexes items to a coordinate system for storage and retrieval [10]. Digitised maps are now generally available and for the future it seems most appropriate to adopt some standard categories of land use which relate to a GIS and which will be applicable worldwide. In association with a computer-based simulation, a more re®ned method of categorisation has been proposed [11]. From a digitised map it is possible to extract the following land usage parameters: . Building location (with respect to some reference point) . Building size, or base area . Total area occupied by buildings . Number of buildings in the area concerned . Terrain heights . Parks and/or gardens with trees and vegetation When this information is available it becomes possible to develop further parameters: . The building size distribution (BSD): a probability density function de®ned by a mean and standard deviation. The standard deviation is an indication of homogeneity. A small value indicates an area where the buildings are of a fairly uniform size; a large value implies a more diverse range. . Building area index (BAI): similar to a [3] or L [4]. . Building height distribution (BHD): a probability density function of the heights of all buildings within the area concerned. . Building location distribution: a probability density function describing the location of buildings with the area. . Vegetation index (VI): the percentage of the area covered by trees, etc. . Terrain undulation index: similar to Dh. Three classi®cations of environment are also proposed, with subclasses as appropriate: . Class 1 (rural) (A) Flat (B) Hilly (C) Mountainous . Class 2 (suburban) (A) Residential with some open spaces (B) Residential with little or no open space (C) High-rise residential 76 The Mobile Radio Propagation Channel . Class 3 (urban and dense urban) (A) Shopping area (B) Commercial area (C) Industrial area Digitised maps, in the form of computer tape, are supplied with software that enables the user to create an output ®le for plotting the map. Further software has been developed to extract the information needed to calculate the parameters for an appropriate area classi®cation. Based on the observed statistics of the extracted data, values have been proposed for the parameters associated with the subclasses in Class 2 and Class 3 environments (Table 4.3). 4.3 PROPAGATION PREDICTION TECHNIQUES Some of the techniques in Chapter 3 can be applied to propagation in urban areas, but Chapter 3 did not cover methods speci®cally developed for application in urban areas, i.e. methods primarily intended to predict losses due to buildings rather than losses due to terrain undulations. We now review a further selection of models but there is no suggestion that the two sets are mutually exclusive. Just as some of the `irregular terrain' methods have factors that can be used to account for buildings, some of the techniques described here are applicable in a wider range of scenarios than built-up areas. Before describing the better-known techniques, it is worth re-emphasising that there is no single method universally accepted as the best. Once again the accuracy of any particular method in any given situation will depend on the ®t between the parameters required by the model and those available for the area concerned. Generally, we are concerned with predicting the mean (or median) signal strength in a small area and, equally importantly, with the signal variability about that value as the mobile moves. 4.3.1 Young's measurements Young [12] did not develop a speci®c prediction method but he reported an important series of measurements in New York at frequencies between 150 and 3700 MHz. His ®ndings proved to be in¯uential and have been widely quoted. The Propagation in Built-up Areas 77 Table 4.3 Descriptive parameters for Class 2 and 3 environments Class BAI (%) BSD (m 2 ) BHD (no. of storeys) VI (%) m s s s m H s H 2A 12±20 95±115 55±70 2 1 5 2:5 2B 20±30 100±120 70±90 2±3 1 < 5 2C 5 12 5 500 > 90 5 414 2 3A 5 45 200±250 5 180 5 41 0 3B 30±40 150±200 5 160 3 1 0 3C 35±45 5 250 5 200 2±3 1 4 1 experimental results of some ®eld trials in which the signal from a base station was received at a vehicle moving in the city streets con®rmed that the path loss was much greater than predicted by the plane earth propagation equation. It was clear that the path loss increased with frequency and there was clear evidence of strong correlation between path losses at 150, 450 and 900 MHz. The sample size at 3700 MHz was not large enough to justify a similar conclusion. In fact, Young did not compare his measured results with the theoretical plane earth equation, but an investigation of some of his results (Figure 4.3) strongly suggests the existence of high correlation. In other words, Young's results show that an inverse fourth-power law relates the loss to distance from the transmitter, and in terms of the Egli model (Section 3.6.1) the relationship can be expressed as L 50  G b G m  h b h m d 2  2 b 4:1 In this case the clutter factor b represents losses due to buildings rather than terrain features, and Figure 4.3 shows that at 150 MHz in New York b is approximately 25 dB. From his experimental results, Young also plotted the path loss not exceeded at 1, 10, 50, 90 and 99% of locations within his test area and these are also shown in Figure 4.3. They reveal that the variability in the signal can be described by a lognormal distribution, although Young himself did not make such an assertion. Finally, Young observed that the losses at ranges greater than 10 miles (16 km) were 6±10 dB less than might have been expected from the trend at shorter ranges. He reasoned, convincingly, that this was because the measurements at longer ranges were representative of suburban New York, whereas those nearer the transmitter 78 The Mobile Radio Propagation Channel Figure 4.3 Measured path loss at 150 MHz in Manhattan and the Bronx and suburbs (after Young). represented losses in urban Bronx and Manhattan. In summary we can say that as early as 1952 it could have been inferred from Young's results that the propagation losses were proportional to the fourth power of the range between transmitter and receiver, that the mean signal strength in a given area was lognormally distributed, and that the losses depended on the extent of urban clutter. 4.3.2 Allsebrook's method A series of measurements in British cities at frequencies between 75 and 450 MHz were used by Allsebrook and Parsons [13] to produce a propagation prediction model. Two of the cities, Birmingham and Bath, were such that terrain features were negligible; the third, Bradford, had to be regarded as hilly. Figure 4.4 shows results at 167 MHz, from which it is apparent that the fourth- power range law provides a good ®t to the experimental data. Equation (4.1) Propagation in Built-up Areas 79 Figure 4.4 Median path loss between half-wave dipoles at 167.2 MHz. therefore provides a basis for prediction, with an appropriate value of b. Where terrain eects are negligible the ¯at city model can be used: L 50 dBL p  L B  g 4:2 where L p is the plane earth path loss, L B is the diraction loss due to buildings and g is an additional UHF correction factor intended for use if f c > 200 MHz. Eectively, in this model, b  L B  g. For a hilly city it was necessary to add terrain losses, and following extensive analysis of the experimental results it was proposed to determine the diraction loss using the Japanese method (Section 3.5.3) and to combine this with the other loss components in the manner suggested by Blomquist and Ladell. The hilly city model, which reduces to the ¯at city model if L D 3 0, is L 50 dBL F L p À L F  2  L 2 D  1=2  L B  g 4:3 It was shown that the diraction loss due to buildings could be estimated by considering the buildings close to the mobile using the geometry in Figure 4.5. The receiver is assumed to be located exactly at the centre of the street, which has an eective width W H . This assumption is not exactly true but it is simple. It obviates the need to know the direction of travel and on which side of the street the vehicle is located. Figure 4.6 shows calculations based on knife-edge diraction in an average street, compared with measured values of b. The calculations were based on the existence of coherent re¯ection on the base station side of the buildings, although it 80 The Mobile Radio Propagation Channel Figure 4.5 The geometry used by Allsebrook to calculate diraction loss. [...]... strength with distance: (- - - -) measured loss and (À À ) calculated free space loss À À À Propagation in Built-up Areas 103 follows the free space law over a distance of 100 m where a line-of-sight path exists Obstruction of the path by trees causes the signal strength to fall by about 12 dB Where there is no evident line-of-sight, or in urban areas where there is signi®cant scattering from buildings, the... for use at 900 MHz and operates in two modes, an area-to-area mode and a point-to-point mode In the ®rst case the prediction is based on three parameters: The median transmission loss at a range of 1 km, L0 The slope of the path loss curve, g dB/decade 96 The Mobile Radio Propagation Channel An adjustment factor, F0 Hence the median loss at a distance d is given by L50 …dB† ˆ L0 ‡ g log d ‡ F0 …4:31†... to be made in each of these situations, hence the term `point-to-point prediction' Figure 4.17(b) shows the di erence between the point-to-point model and predictions for a ¯at suburban area with g ˆ 38:6 dB/decade The trend is as expected: for positions C to G the value of he is greater than the physical height above local ground, so the predicted loss is smaller; for positions H and I the value of... antenna height in two types of hilly terrain 98 The Mobile Radio Propagation Channel Figure 4.17 In¯uence of terrain on e€ective antenna height for di erent positions (after Lee): (a) hilly terrain contour, (b) point-to-point prediction There have been other approaches to formulating a simple prediction equation suitable for use in built-up areas For example, McGeehan and Griths [31] started from... principal features of the environment, i.e terrain, buildings, foliage and land usage The various channel e€ects such as re¯ection, scattering, di raction and foliage loss, are taken into account and the outputs include signal strength, time dispersion, spatial dispersion and fading statistics Di raction losses can be calculated using either UTD or knife-edge approximations A feature of the model is that... constants are chosen such that Lb is equal to the free space path loss at d ˆ 20 m In the non-LOS case the basic transmission loss comprises the free space path loss LB (2.6), the multiple-screen di raction loss Lmsd and the rooftop-to-street di raction and scatter loss Lrts Thus 94 The Mobile Radio Propagation Channel Figure 4.15 De®ning the street orientation angle '  Lb ˆ LB ‡ Lrts ‡ Lmsd LB Lrts... by others [34] The EFIE approach includes both forward and back scattering and embodies a moment method using a novel set of complex basis 100 Table 4.7 The Mobile Radio Propagation Channel Comparison of prediction models in urban areas Frequency (MHz) Prediction from (5.23) Allsebrook's best-fit line Ibrahim's best-fit line 85.87 167.2 441.0 97:4 ‡ 38 log d 105 ‡ 38 log d 116 ‡ 38 log d 98 ‡ 38 log... prediction method for signal strength prediction The basis of the method is that the free space path loss between the points of interest is determined and added to the value of Amu … f, d ) obtained from Figure 4.7 Amu is the median attenuation, relative to free space in an 82 The Mobile Radio Propagation Channel Figure 4.7 Basic median path loss relative to free space in urban areas over quasi-smooth... deviation from Okumura's reference median curve at 450 MHz ± is given by 8 < 30 À 25 log a S …dB† ˆ 20 ‡ 0:19 log a À 15:6…log a†2 : 20 5% < a < 59% 1% < a 4 5% …4:12† a < 1% where a is the percentage of the area covered by buildings Figure 4.11 Deviation from median ®eld strength curve due to buildings surrounding the mobile terminal 88 4.3.4 The Mobile Radio Propagation Channel The Ibrahim and Parsons... London for (a) path length 100 m, (b) path length 190 m: (Ð) experimental PDF, (Â) experimental CDF, (.) best-®t Rayleigh PDF and CDF 104 The Mobile Radio Propagation Channel Figure 4.20 Signal attenuation in a shadow zone: d is distance from the wall the free space value, and in New York City this value increased to 46 dB The distribution of the excess loss was closely represented by lognormal statistics . often The Mobile Radio Propagation Channel. Second Edition. J. D. Parsons Copyright & 2000 John Wiley & Sons Ltd Print ISBN 0-4 7 1-9 8857-X Online ISBN 0-4 7 0-8 415 2-4 termed slow fading and, as. those of buildings, introducing additional path losses and producing spatial fading. Estimation of the received mobile radio signal is a two-stage process which involves predicting the median signal. in classifying land usage: . Building density (percentage of area covered by buildings) . Building size (area covered by a building) . Building height . Building location . Vegetation density .