Optical limits
8.3 EMPIRICAL PATH LOSS MODELS
The two basic propagation models (free space loss and plane earth loss) examined in Chapter 5 plus the more detailed obstruction loss models described in Chapter 6 account, in principle, for all of the major mechanisms which are encountered in macrocell prediction. However, to use such models would require detailed knowledge of the location, dimension and constitutive parameters of every tree, building and terrain feature in the area to be covered. This is far too complex to be practical and would anyway yield an unnecessary amount of detail, as the system designer is not usually interested in the particular locations being covered, but rather in the overall extent of the coverage area. One appropriate way of accounting for these complex effects is via an empirical model. To create such a model, an extensive set of actual path loss measurements is made, and an appropriate function is fitted to the measurements, with para-meters derived for the particular environment, frequency and antenna heights so as to minimise the error between the model and the measurements. Note that each measurement represents an average of a set of samples, the local mean, taken over a small area (around 10–50 m), in order to remove the effects of fast fading (Chapter 10), as originally suggested by [Clarke, 68]. See Chapter 19 for further details of measurement procedures. The model can then be used to design systems operated in similar environments to the original measurements. A real example of an empirical model fitted to measurements is shown in Figure 8.2. Methods of accounting for the very large spread of the measurements at a given distance are the subject of Chapter 9.
The simplest useful form for an empirical path loss model is as follows:
PR
PT¼1 L¼ k
rn or in decibels L¼ 10n log r þ K ð8:1Þ where PT and PR are the effective isotropic transmitted and predicted isotropic received powers as defined in Chapter 5, L is the path loss, r is the distance between the base station and the mobile and K¼ 10 log10 k and n are constants of the model. Parameter k can be considered as the reciprocal of the propagation loss that would be experienced at one metre range ðr ¼ 1 mÞ. Models of this form will be referred to as power law models. A more convenient form (in decibels) is
LðrÞ ¼ 10n logðr=rrefÞ þ LðrrefÞ ð8:2Þ where LðrrefÞ is the predicted loss at a reference distance rref.
hb
hm
r
h0
dm
Figure 8.1: Definition of parameters for macrocell propagation models
164 Antennas and Propagation for Wireless Communication Systems
Both the free space loss and the plane earth loss can be expressed in this form. The parameter n is known as the path loss exponent. It is found by measurement to depend on the system parameters, such as antenna heights and the environment. Chapter 1 showed that the path loss exponent is critical in establishing the coverage and capacity of a cellular system.
8.3.1 Clutter Factor Models
Measurements taken in urban and suburban areas usually find a path loss exponent close to 4, just as in the plane earth loss, but with a greater absolute loss value – i.e. larger K in Eq. (8.1).
This has led to some models being proposed which consist of the plane earth loss, plus (in decibels) an extra loss component called the clutter factor, as shown in Figure 8.3. The various models differ basically in the values which they assign to k and n for different frequencies and environments.
Example 8.1
Calculate the range of a macrocell system with a maximum acceptable path loss of 138 dB, assuming hm¼ 1:5 m; hb¼ 30 m; fc¼ 900 MHz and that path loss can be modelled for this frequency and environment using the plane earth loss plus a clutter factor of 20 dB.
100 200 300 400 2000 3000 5000
−110
−100
−90
−80
−70
−60
−50
−40
Distance from base station [m]
Received signal level [dBm]
500 600 700 800 1000
Figure 8.2: Empirical model of macrocell propagation: the dots are measurements taken in a suburban area and the line represents a best-fit empirical model (reproduced by permission of Red-M Services Ltd)
Macrocells 165
Solution
The plane earth model of Eq. (5.34) is now modified to the following empirical model:
Lemp¼ 40 log r 20 log hm 20 log hbþ K
where K is the clutter factor [decibels]. Equating this to the maximum acceptable path loss and rearranging for log r yields
log r¼Lempþ 20 log hmþ 20 log hb K 40
¼138þ 20 log 1:5 þ 20 log 30 20
40 3:78
Hence r¼ 103:78 6 km. This shows how the results of Examples 5.4 and 5.5 can be modified to predict more practical ranges.
A good example of a clutter factor model is the method by [Egli, 57], which is based upon a large number of measurements taken around American cities. Egli’s overall results were Figure 8.3: Clutter factor model. Note that the y-axis in this figure and in several to follow is the negative of the propagation loss in decibels. This serves to make clear the way in which the received power diminishes with distance.
166 Antennas and Propagation for Wireless Communication Systems
originally presented in nomograph form, but [Delisle, 85] has given an approximation to these results for easier computation:
L¼ 40 log R þ 20 log fc 20 log hbþ Lm ð8:3Þ where
Lm¼ 76:3 10 log hm for hm< 10 76:3 20 log hm for hm 10
ð8:4Þ
Note that this approximation involves a small discontinuity at hm¼ 10 m. Although plane earth loss is frequency independent, this model introduces an additional fc2received power dependence, which is more representative of the results of real measurements. For very large antenna heights, the loss predicted by (8.3) may be less than the free space value, in which case the free space value should be used.
The mobile antenna characteristic is approximately linear for antennas which clear the surrounding terrain features. Elsewhere there is a square law variation for heights in the range 2–10 m, as for the plane earth loss model. The transition value 10 m presumably corresponds to the mean building height, although no correction is made for other heights.
The average effect of polarisation is considered negligible.
8.3.2 The Okumura–Hata Model
This is a fully empirical prediction method [Okumura, 68], based entirely upon an extensive series of measurements made in and around Tokyo city between 200 MHz and 2 GHz. There is no attempt to base the predictions on a physical model such as the plane earth loss. Predictions are made via a series of graphs, the most important of which have since been approximated in a set of formulae by [Hata, 80]. The thoroughness of these two works taken together has made them the most widely quoted macrocell prediction model, often regarded as a standard against which to judge new approaches.
The urban values in the model presented below have been standardised for international use in [ITU, 529]
The method involves dividing the prediction area into a series of clutter and terrain categories, namely open, suburban and urban. These are summarised as follows:
Open area: Open space, no tall trees or buildings in path, plot of land cleared for 300–400 m ahead, e.g. farmland, rice fields, open fields.
Suburban area: Village or highway scattered with trees and houses, some obstacles near the mobile but not very congested.
Urban area: Built up city or large town with large buildings and houses with two or more storeys, or larger villages with close houses and tall, thickly grown trees.
Okumura takes urban areas as a reference and applies correction factors for conversion to the other classifications. This is a sensible choice, as such areas avoid the large variability present in suburban areas (Chapter 9) and yet include the effects of obstructions better than could be done with open areas. A series of terrain types is also defined for when such information is available. Quasi-smooth terrain is taken as the reference, and correction factors are added for the other types.
Macrocells 167
Okumura’s predictions of median path loss are usually calculated using Hata’s approx-imations as follows [Hata, 80]:
Urban areas LdB¼ A þ B log R E Suburban areas LdB¼ A þ B log R C Open areas LdB¼ A þ B log R D
ð8:5Þ
where
A¼ 69:55 þ 26:16 log fc 13:82 log hb
B¼ 44:9 6:55 log hb
C¼ 2 logðfð c=28ÞÞ2þ5:4
D¼ 4:78ðlog fcÞ2 18:33 log fcþ 40:94
E¼ 3:2ðlogð11:75hmÞÞ2 4:97 for large cities; fc 300 MHz E¼ 8:29ðlogð1:54hmÞÞ2 1:1 for large cities; fc< 300 MHz
E¼ ð1:1 log fc 0:7Þhm ð1:56 log fc 0:8Þ for medium to small cities ð8:6Þ
The model is valid only for 150 MHz fc 1500 MHz; 30 m hb 200 m;
1 m< hm< 10 m and R > 1 km. The path loss exponent is given by B=10, which is a little less than 4, decreasing with increasing base station antenna height.
Base station antenna height hbis defined as the height above the average ground level in the range 3–10 km from the base station; hbmay therefore vary slightly with the direction of the mobile from the base. The height gain factor varies between 6 dB per octave and 9 dB per octave as the height increases from 30 m to 1 km. Measurements also suggest this factor depends upon range.
Okumura found that mobile antenna height gain is 3 dB per octave up to hm¼ 3 m and 8 dB per octave beyond. It depends partially upon urban density, apparently as a result of the effect of building heights on the angle-of-arrival of wave energy at the mobile and the consequent shadow loss variation (Chapter 9). Urban areas are therefore subdivided into large cities and medium/small cities, where an area having an average building height in excess of 15 m is defined as a large city.
Other correction factors are included in Okumura’s original work for the effects of street orientation (if an area has a large proportion of streets which are either radial or tangential to the propagation direction) and a fine correction for rolling hilly terrain (used if a large proportion of streets are placed at either the peaks or valleys of the terrain undulations).
Application of the method involves first finding the basic median field strength in concentric circles around the base station, then amending them according to the terrain and clutter correction graphs.
Okumura’s predictions have been found useful in many cases [COST207, 89], particularly in suburban areas. However, other measurements have been in disagreement with these predictions; the reasons for error are often cited as the difference in the characteristics of the area under test with Tokyo. Other authors such as [Kozono, 77] have attempted to modify Okumura’s method to include a measure of building density, but such approaches have not found common acceptance.
168 Antennas and Propagation for Wireless Communication Systems
The Okumura–Hata model, together with related corrections, is probably the single most common model used in designing real systems. Several commercial prediction tools essentially rely on variations of this model, optimised for the particular environments they are catering for, as the basis of their predictions; see www.simonsaunders.com/apbook for a list of such tools.
8.3.3 The COST 231–Hata Model
The Okumura–Hata model for medium to small cities has been extended to cover the band 1500 MHz< fc< 2000 MHz [COST231, 99].
LdB¼ F þ B log R E þ G ð8:7Þ
where
F¼ 46:3 þ 33:9 log fc 13:82 log hb ð8:8Þ E is as defined in (8.6) for medium to small cities and
G¼ 0 dB medium-sized cities and suburban areas 3 dB metropolitan areas
ð8:9Þ
8.3.4 The Lee Model
The Lee model is a power law model, with parameters taken from measurements in a number of locations, together with a procedure for calculating an effective base station antenna height which takes account of the variations in the terrain [Lee, 82; Lee, 93]. It can be expressed in the simplified form
L¼ 10n log R 20 log hbðeffÞ P0 10 log hmþ 29 ð8:10Þ where n and P0are given by measurements as shown in Table 8.1 and hbðeffÞis the effective base station antenna height. The measurements were all made at 900 MHz, and correction factors must be applied for other frequencies, but these do not appear to have been specified in the open literature.
The effective base station height is determined by projecting the slope of the terrain in the near vicinity of the mobile to the base station location. Figure 8.4 shows how this effective height varies for four mobile locations on gently sloping terrain.
Table 8.1: Parameters for the Lee model
Environment n P0
Free space 2 45
Open area 4.35 49
Suburban 3.84 61.7
Urban Philadelphia 3.68 70
Newark 4.31 64
Tokyo 3.05 84
New York city 4.8 77
Macrocells 169
8.3.5 The Ibrahim and Parsons Model
This method [Ibrahim, 83] is based upon a series of field trials around London. The method is not intended as a fully general prediction model, but as a first step towards quantifying urban propagation loss. It integrates well with a previous method [Edwards, 69] for predicting terrain diffraction effects as the same 0.5 km square database is also used. Each square is assigned three parameters, H, U and L, defined as follows.
Terrain height H is defined as the actual height of a peak, basin, plateau or valley found in each square, or the arithmetic mean of the minimum and maximum heights found in the square if it does not contain any such features.
The degree of urbanisation factor U is defined as the percentage of building site area within the square which is occupied by buildings having four or more floors. For the 24 test squares in inner London which were analysed, U varied between 2 and 95%, suggesting that this parameter is sensitive enough for the purpose.
Land usage factor L is defined as the percentage of the test area actually occupied by any buildings.
These parameters were selected empirically as having good correlation with the data. Two models were proposed. The fully empirical method shows marginally lower prediction errors but relies on a complex formulation which bears no direct relationship to propagation principles.
The semi-empirical method, as with the Egli clutter factor method, is based upon the plane earth loss together with a clutter factor b, expressed as a function of fc, L, H and U. Only the semi-empirical method will be examined here, as it has been quoted in later work by the same author [Parsons, 92], and as it forms a better basis for future development. The model is given as
LT ¼ 40 log r 20 logðhmhbÞ þ b where b¼ 20 þfc
40þ 0:18L 0:34H þ K and K¼ 0:094U 5:9
ð8:11Þ hA
hB hC hD
A B
C D
Figure 8.4: Determination of effective base station antenna height for Lee’s model
170 Antennas and Propagation for Wireless Communication Systems
Data is extracted from the databases compiled by local authorities in the United Kingdom.
Although information on U is available only in highly urbanised city centres, K is set to zero elsewhere. Considerably lower accuracy may be expected in such areas as K¼ 0 would correspond to U¼ 63%. RMS errors calculated from the original data on which the model is based vary from 2.0 to 5.8 dB as frequency is increased from 168 to 900 MHz. A comparison is also shown for some independent data, but error statistics are not given.
The model is of limited use in suburban areas as U will normally be zero, giving no measure of building height distribution.
8.3.6 Environment Categories
In an empirical model, it is crucial to correctly classify the environment in which the system is operating. The models assume the characteristics of the environment to be predicted are sufficiently similar to those where the original measurements were taken that the propagation loss at a given distance will be similar. Good results will therefore be obtained only if the correct classification is chosen. The categories of environment should also be sufficiently numerous that the properties of different locations classed within the same category are not too variable. The decision as to which category an environment fits into is usually purely subjective and may vary between individuals and countries. For example, the Okumura–Hata model uses four categories: large cities, medium-small cities, suburban areas and open areas.
Although the original measurements were made in Tokyo, the model relies on other parts of the world having characteristics which are somehow similar to those in Tokyo. Although this is an extremely questionable assumption, it is nevertheless true that the model has been applied to many successful system designs.
Many more detailed schemes exist for qualitative classification of land usage; Table 8.2 shows one example. Schemes often correspond to sources of data, such as satellite remote-sensing data which classifies land according to the degree of scattering experienced at various wavelengths. This at least avoids the need for ambiguous judgements to be made. Similarly, the Ibrahim and Parsons model uses a clear numerical approach to classification. Never-theless, there is no guarantee that there is any one-to-one mapping between the propagation characteristics and such measures of land usage. In order to find more appropriate parameters,
Table 8.2: British Telecom land usage categories [Huish, 88]
Category Description
0 Rivers, lakes and seas
1 Open rural areas, e.g. fields 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 centres of a few large cities
Macrocells 171
the growing tendency in macrocellular propagation is towards models which have a physical basis, and these are examined in the next section.