# Experimental Investigation of Land Mobile Prediction Methods and Modeling of Radio Planning  Tool Parameters along Indian Rail Road Rural Zones

Authors: M. V. S. N. Prasad; P. K. Dalela; M. Chaitanya
Journal: International Journal of Antennas and Propagation
 (2008)

---

## Abstract

Mobile communication networks in rural zones were not given enough importance and emphasis unlike their urban counter parts due to the unattractive revenues and economic considerations for the cellular operators.  In order to identify the suitable prediction methods for Indian rail road rural zones, train-based measurements were conducted in the northern and western rural zones along rail roads. These were carried out by recording the carriers emitted by the trackside base stations inside the moving train. The observed signal levels converted into path losses were compared initially with various conventional prediction methods. The observed results were also compared with the predicted results of radio planning tool utilizing digital terrain data. The constants of the model incorporated in the radio planning tool were tuned separately for north Indian and west Indian base stations based on the observed results. The suitability of the models has been evaluated in terms of standard statistical parameters.


---

## Body

## 1. Introduction

The deployment of future mobile communication systems requires accurate planning tools for
efficient performance. A knowledge of suitable prediction method helps to
optimize the signal levels and reduces interference to neighboring users. A single prediction might not be suitable over India since it is a vast country with diverse topographic and environmental features. Some of the urban regions have non uniform building structure and some of the rural regions have difficult terrains. To identify the prediction
method suitable for a given region, a good number of experimental campaigns are
necessary and evaluation of various prediction methods against these observed
results gives an insight into the suitability of these methods and the
possibility of transporting these methods to similar regions of the country. In
rural environments where there is varying tree density and terrain is
irregular, it is difficult to have links with long distances, good data rate,
and line-of-sight mode [1]. It is better to investigate with the help of
experimental observations the extent of
line-of-sight (los) and non-los zones and the associated problems.
Organizations like Universal Services Obligation Fund Administration are
funding projects in rural India
for shared infrastructure for establishing cellular mobile telephone services.
In this context, identifying methods relevant to rural zones and modifying the
existing prediction methods in terms of tuning the coefficients are the need of
the hour.Medeisis and Kajackas [2] presented the
comparison of Hata model with measurements in urban and rural zones at Lituania
at 160, 450, 900, and 1800 MHz bands. In the urban region, the
standard deviation of Okumara-Hata method was in the range of 5–7 dB. Best
precision of Okumara-Hata model in urban areas was achieved in the 900 MHz
range. In the rural areas, measured results deviated significantly from the
model. Here the standard deviation increased up to 15 dB and more. Only in the
160 MHz band model and experiments agreed  very well. Blaunstein and Ben-Shimol [3]
studied theoretically and experimentally the frequency dependence of path loss for various terrestrial macrocell and microcell environments. They have
deduced various algorithms for link budget designs suggesting modifications
like additional diffraction coefficient, effective height parameter. Miyashita
et al. [4] conducted field measurements in eleven areas in
Japan including urban, rural, mountainous, and water areas and investigated diffraction,
Okumura-Hata and two ray models. The models are selected depending on the
terrain between transmitter and receiver. They have deduced correction factors
based on the features of each path loss model and authors claim that proposed
method gives a good level of accuracy compared to that of conventional
models.


## 2. Experimental Details

Keeping the above objective in view, in the
present study a comparison of different prediction methods used for land mobile
radio in rural/suburban areas has been carried out utilizing UHF train mobile
radio measurements conducted in the northern and western regions of the
country. The track side base stations utilized in the study are (1) Meerut, (2) Muzzafarnagar,
(3) Saharanpur located in northern India (1) Vangani, (2) Neral, (3) Karjat, (4) Pangoli
(5) Talegaon, and (6) Pune situated in western India. The base stations in northern India
are located at 40 m above ground level and those in western India are situated
at 26, 25, 60, 46, 115, and 45 m above ground level. The effective radiated
powers of the base stations which are Meerut, Muzzafarnagar, Saharanpur, Vangani, Pangoli, and
Pune are +37 dBm, whereas those of Neral, Karjat, and
Talegaon are +39.5 dBm. The checking of transmitter
power, frequency, VSWR, and supply voltage was done regularly to monitor the
deviations, and the values of the parameters reported in the paper are correct
to the best of our knowledge.At the time of measurements, Indian railways were allocated frequency
range from 314 to 322.6 MHz. Train radio network occupies a bandwidth of2×850 kHz, in the range from 314 to 314.85 MHz and 321.0 to 321.85 MHz. Base
stations located along the track continuously transmit the carrier at 320 MHz.
In this carrier band, the bandwidth is 850 kHz. A test coach is equipped with a
calibrated receiver and computerized data logger. Also a chart recorder was
used to record the carrier levels. To relate path loss values with locations, a
counter of wheel rotations was necessary to drive the paper chart in a fixed
linear relation to the train speed as well as having the option of storing
samples at selectable intervals. The receiver sensitivity will be better than
0.5 μ v
(−93 dBm) for 20 dB S/N ratio measured at an RF signal modulated
with 1000 Hz at 60% of the peak deviation into the duplexer antenna input. The dynamic adjacent
channel selectivity will be 70 dB or better. A low profile omnidirectional
antenna was used for reception at the roof of the coach. Slight directing
antenna radiation patterns have been used for reduction of radiation in
unwanted direction and obtain moderate antenna gain of 5 dB. Slightly directional antennas have
been employed when the track has minimum number of turns. Track is a broad gauge type with
width of 1.676 m. Highly directional antennas have been employed when the track
is straight and omnidirectional antennas are employed when the track is zigzag
and with a large number of turns. However, in the present case of slightly directional antennas,
the beam width corresponded to 88° in horizontal direction and 30° in
vertical direction.Antennas are vertically polarized, 50 ohms impedance. VSWR is less than 1.5 : 1
across the frequency band with antenna gain of 5 dB, branching and feeder loss of
transmitter cable is 3 dB, receiving antenna gain is 0 dB and omnidirectional,
and branching and feeder loss of receiver cable is 1 dB.


### 2.1. Data Processing

The signal is measured at a distance of
every meter and is averaged over a distance of hundred meters. Here 90%
coverage implies an uncovered track of 10 m track. This averaged signal is
converted into path loss values and its standard deviation, maximum value, and
minimum values were deduced. The coverage is calculated by division of the
number of samples exceeding threshold by the total number of samples. This
experiment was primarily conducted by Indian railways mainly for estimating
their coverage area of base stations located along the railway track for
designing future train mobile communication systems. We got associated with
them for scientific purpose of getting good data for evaluating models. They
adopted the methodology of measuring signal level at every meter and averaging
over one hundred meters for their own planning purposes.Coverage objectives for mobile radio
systems utilized by railways are prescribed in technical regulations of
International Union of Railways (UIC code leaflet no.751-3) [5]. According to this, satisfactory
coverage of an entire line is achieved if a minimum reception signal can be
attained over 95% of  the track distance
and for 95% of the time. To estimate this coverage (which is not part of this
paper) 100 m has been chosen as the statistic determined over portion of the
path. A train traveling at 60 km/hr will pass this gap within 6 seconds.  This will keep the communication link up
right. In the present study, the interest is to investigate the median path
loss and not the fast fading.The region extending from Meerut to Saharanpur can be
classified as open area with intermittent trees and small villages and towns in
between. The region extending from Vangani, Neral, and so forth in western India is also open
with agricultural lands and the region around the base station is of
suburban in nature. Overall both the northern and western regions represent
typical rural Indian scenario. Microwave towers located by the track side were
utilized as base stations. In the northern region, the terrain is flat and open
whereas in the western region where measurements were conducted the terrain is
rough with some hilly (ghat) sections in between. Photographs of environments,
clutter where measurements were conducted are shown in Figures1–4 and are
described under the radio planning tool section.  These are given to get a feel of the
environment.Figure 1
Clutter map of north Indian base stations.Figure 2
Terrain variation of north Indian base stations.Figure 3
Clutter map of west Indian base stations.Figure 4
Terrain variation of west Indian base stations.


## 2.1. Data Processing

The signal is measured at a distance of
every meter and is averaged over a distance of hundred meters. Here 90%
coverage implies an uncovered track of 10 m track. This averaged signal is
converted into path loss values and its standard deviation, maximum value, and
minimum values were deduced. The coverage is calculated by division of the
number of samples exceeding threshold by the total number of samples. This
experiment was primarily conducted by Indian railways mainly for estimating
their coverage area of base stations located along the railway track for
designing future train mobile communication systems. We got associated with
them for scientific purpose of getting good data for evaluating models. They
adopted the methodology of measuring signal level at every meter and averaging
over one hundred meters for their own planning purposes.Coverage objectives for mobile radio
systems utilized by railways are prescribed in technical regulations of
International Union of Railways (UIC code leaflet no.751-3) [5]. According to this, satisfactory
coverage of an entire line is achieved if a minimum reception signal can be
attained over 95% of  the track distance
and for 95% of the time. To estimate this coverage (which is not part of this
paper) 100 m has been chosen as the statistic determined over portion of the
path. A train traveling at 60 km/hr will pass this gap within 6 seconds.  This will keep the communication link up
right. In the present study, the interest is to investigate the median path
loss and not the fast fading.The region extending from Meerut to Saharanpur can be
classified as open area with intermittent trees and small villages and towns in
between. The region extending from Vangani, Neral, and so forth in western India is also open
with agricultural lands and the region around the base station is of
suburban in nature. Overall both the northern and western regions represent
typical rural Indian scenario. Microwave towers located by the track side were
utilized as base stations. In the northern region, the terrain is flat and open
whereas in the western region where measurements were conducted the terrain is
rough with some hilly (ghat) sections in between. Photographs of environments,
clutter where measurements were conducted are shown in Figures1–4 and are
described under the radio planning tool section.  These are given to get a feel of the
environment.Figure 1
Clutter map of north Indian base stations.Figure 2
Terrain variation of north Indian base stations.Figure 3
Clutter map of west Indian base stations.Figure 4
Terrain variation of west Indian base stations.


## 3. Results

The prediction methods utilized in the
present study are (1) Hata [6], (2) Blomquist and Ladell [7], (3) Egli [8],
and (4) irregular terrain model [ITM] [9]. The details of prediction methods
are not included here and can be obtained from respective references. In this
study, the observed signal levels are converted into path losses and compared
with the theoretically computed path losses. Instead of describing the
comparison of individual base station results with the above prediction methods,
statistical indicators like standard deviation and mean error for each
prediction method have been deduced and are given in Table 1. These indicators help to evaluate the suitability of the
prediction methods.Table 1
Mean errors (me) and standard deviations (sd) of all the prediction methods (dB).
Base stationHataEgliBlomquistITMmesdmeSdmesdmesdMeerut−1.363.23−12.973.85−4.104.441.724.10Muzafarnagar−2.575.55−10.9719.33−2.2018.03.6817.02Saharanpur−1.813.12−14.474.52−5.445.471.235.42Vangani−9.1011.550.2621.0014.7619.215.224.68Neral9.995.011.284.615.984.3717.555.3Karjat4.964.527.387.7121.027.2323.438.64Pangoli−16.1711.83−27.6311.52−16.3312.86−13.3312.28Talegaon−3.29.92−3.128.67−2.668.87−2.269.13Pune0.315.74−10.656.173.536.317.545.95


### 3.1. Standard Deviations of Prediction Methods

Mean errors and standard
deviations of the prediction errors for all the methods have been deduced and
shown in Table1.The general practice in
radio wave propagation modeling is that when a given model is exhibiting a
highest standard deviation, it is considered not suitable and when it is having
a low standard deviation, it is considered suitable in that region. From
Table1, the following observations have been made. In
the northern India, Hata's method showed less standard deviation for all the three paths. Hata's
method also exhibited less mean error
compared with Egli, Blomquist and Ladell, and ITM methods. The negative sign
before mean error shows that the
given model is over estimating the path loss. In the case of west Indian paths,
Hata's method followed by ITM method gave less standard deviations. Hata's
method exhibited higher mean error and higher standard deviations for west
Indian paths. This could be due to the difference in terrains between northern
and western Indian regions. In the northern region, the terrain was flat, open
whereas in west India,
it was rough in nature as in the case of Pangoli and Talegaon base stations. In
the case of Pune base station where suburban conditions prevailed, all the
methods predicted standard deviation within 5–7 dB. Similarly,
in the case of Neral base station where there is smooth variation of terrain
all the prediction methods yielded a deviation between 4–5 dB. In the case of
Pangoli and Talegaon high standard deviations were observed due to the varying
and rough nature of terrain.


### 3.2. Investigation by Diffraction Mechanism

Rec-526 of ITU-R [10] is used to investigate
propagation by diffraction and knife edge diffraction. Since no mountains and
hills are there along the path knife edge diffraction has not been utilized.
Only spherical diffraction has been used. No corrections are used for
vegetation. The terrain is flat with rolling plains along the track and not
much vegetation is seen. We made an
attempt to compute the losses due to diffraction using the above Rec-526 for
various paths beyond the line-of-sight distances. The losses obtained and their
comparison with measured losses are given in Table 2. In the
table, distances are in km and losses are in dB.Table 2
Computed and observed diffraction losses for north and west Indian paths (dB).
North Indian pathsMeerutMuzzafarnagarSaharanpurDis (km)Obs lossDiff lossDis (km)Obs lossDiff lossDis (km)Obs lossDiff loss(dB)(dB)(dB)(dB)(dB)(dB)32.5129.5144.632.5126.5144.632.5130144.635130146.835127.5146.835135146.837.5133148.937.5128148.937.5135148.940135150.540136150.540139150.542.5140151.942.5141151.942.5140151.945143.5153.845142153.845141.5153.847.514415747.514415747.514215750145158.450143.5158.4West Indian PathsPangoliTalegoanPuneDist. (km)Obs. lossdiff. lossDist. (km)Obs. lossdiff. lossDist. (km)Obs. lossdiff. loss(dB)(dB)(dB)(dB)(dB)(dB)31122142.53014012731.513614232118142.632.515612833145143.333141143.334139143.935135144.536126145.63811514740110148.54211415044107150.345127151.5Here diffraction losses
are computed for distances beyond line-of-sight zones. For each path a radio
horizon distance has been computed and beyond this distance diffraction losses
have been deduced. A perusal of the above table indicates that in the case of
north Indian paths, Meerut base station
exhibited deviation of 10–15 dB,
Muzzafarnagar exhibiting 10–18 dB, and Saharanpur
base station
showing a deviation of 12–15 dB. These
large deviations show that signal propagation at these distances is not
supported by diffraction mechanism. In the case of west Indian paths Pangoli
and Talegaon base stations exhibited large deviations of the order of 20 dB and
above. Only Pune base station where only two experimentally measured values are
available beyond the los zone, some kind of agreement is seen between
diffraction computed and measured path losses. Probably only in this case
beyond 30 km propagation occurred through diffraction mechanism.


### 3.3. Investigations with Radio Planning Tool

Using the digital terrain data with a resolution of 50 m and Aircom's asset tool [11] clutter,
terrain and environmental photographs, signal coverage plots for different base
stations have been generated. Clutter maps, terrain maps for entire northern
and western base stations are shown in Figures 1–4. Figure 1 shows
the clutter map of north Indian base stations Meerut, Muzaffarnagar, and Saharanpur
base stations along the track. Meerut base station is surrounded by low density urban environment. As one moves along
the track from Meerut to Muzaffarnagar base station, agricultural lands and very low density
vegetation are seen. At intermittent distances rural villages are located. From
Muzzafarnagar base station to Saharanpur
base station also agricultural lands along with rural features are seen. All
the base stations are surrounded by low density urban environment up to small
distances. Figure 2 depicts the terrain variation of these north Indian base
stations. Meerut base station is situated at a
distance of 225 m above mean sea level, Muzaffarnagar situated at a height of
243 m above mean sea level, and Saharanpur
located at a height of 275 m above mean seal level. As one progresses, the
terrain is rising and there is a gradual upward slope.Figure3 shows the clutter map of west Indian base
stations Vangani, Neral, Karjat, Pangoli, Talegaon, and Pune. From Vangani to
Neral, open regions with low density vegetation are seen along the track.
Similar environment between Neral to Karjat is seen. From Karjat to Pangoli
open area with low height vegetation is seen. This is not tall enough to block
out radio waves and apply foliage losses. The region between Pangoli and
Talegaon is filled with moderate agricultural lands and partly open regions.
From Pangoli onwards the railway track enters the hilly terrain. The region between Talegaon and Pune is
also open and agricultural region. Near Pune base station low density urban
environment is seen. Figure 4 shows the terrain variation of all these west
Indian base stations. Vangani is situated at a height of 40 m above mean sea
level, Neral at 40 m, Karjat at 80 m, and Pangoli at 600 m. There is a steep
upward slope towards Pangoli. At Talegaon, the mean sea level height  is 600 m and at Pune the corresponding value
is 560 m.


#### 3.3.1. Propagation Model

The propagation model employed in this tool [11] is illustrated as follows.(1)Pathloss=K1+K2log(d)+K4(Hms)+K4log(Hms)+K5log(Heff)+K6log(Heff)log(d)+K7(diffn)+ClutterLoss, where d is distance between base to
mobile station (Km); Hms is height of the mobile station above ground (meter).
This figure may be specified either globally or for individual clutter
categories. Heff is effective base station antenna height (meter). Diffn is
diffraction loss calculated using Epstein, Peterson, Deygout, or Bullington
equivalent knife edge methods. K1 and K2 are
intercept and slope. These factors correspond to a constant offset (in dBm) and
a multiplying factor for the log of the distance between the base station and
mobile. K4 is mobile antenna height factor. It is a correction factor used to take into account the
effective mobile antenna height. K4 is Okumura-Hata multiplying factor for Hms.
K5 is effective antenna height gain. This is the multiplying factor for the log
of the effective antenna height K6. This is the Okumura-Hata type multiplying factor
for log(Heff)log(d). K7 takes into account diffraction effects. A choice of diffraction methods
is available.C_loss is clutter specifications such as heights and separation that
are also taken into account in the calculation process. Clutter losses shown in Table3 have been obtained based on our past experiences of conducting radio
measurements in Indian rural zones. Low density urban = 1 dB, village = 1 dB,
high density vegetation = 4 dB, low density vegetation = 1 dB, agriculture = 0 dB,
open area = −5 dB, quasiopen = −5 dB, and water = −15 dB.
The parameters of the tool at various frequencies are shown in Table 3.Table 3
Parameters of the Aircom tool at various frequencies.
Parameter450 MHz900 MHz1800 MHz2000 MHzMobile Rx height(m)1.51.51.51.5Earth radius(Km)8493849384938493K1142.3150.6160.9162.5K244.944.944.944.9K4−2.6−2.55−2.55−2.55K4−8000K5−11.7−13.82−13.82−13.82K6−4.3−6.55−6.55−6.55K70.40.70.80.8From Table3,
it is seen that K2 is independent of
frequency. Keeping K2 to K7 values
corresponding to 450 MHz (this being nearest to 320 MHz in the present study),
an attempt has been made to tune the value of K1 with the help of measured
values. Various values of K1 corresponding to different paths are deduced and
signal coverage plots for various base stations have been generated. In these
entire figures, tool generated values are confined to distances of 35 km. This
is due to the inherent limit set in the tool by the manufacturer when it is
being used in GSM mode. Comparison of tool predicted losses for various values
of K1 with measured losses is shown in Figures 5, 6, and 7 for north
Indian base stations and in Figures 8, 9, 10, 11, and 12 for west
Indian base stations. The figure for Pangoli base station is not shown due to
restriction of number of figures. The advantage of these tuned values over
these regions is that these can be used by the future operators of mobile
communications for this and similar regions.Figure 5
Comparison of tool predicted losses with observed losses versus track distance for Meerut  base station.Figure 6
Comparison of tool predicted losses with observed losses versus track distance for
Muzaffarnagar base station.Figure 7
Comparison of tool predicted losses with observed losses versus track distance for Saharanpur  base station.Figure 8
Comparison of tool predicted losses with observed losses versus track distance for Vangani base station.Figure 9
Comparison of tool predicted losses with observed losses versus track distance for Neral base station.Figure 10
Comparison of tool predicted losses with observed losses versus track distance for
Karjat base station.Figure 11
Comparison of tool predicted losses with observed losses versus track distance for
Talegaon base station.Figure 12
Comparison of tool predicted losses with observed losses versus track distance for
Pune base station.


#### 3.3.2. Comparison of Tool Predicted Values with Observed Losses

In the figures, measured data have
been processed in accordance with the resolution of the prediction tool. Figures5, 6, and 7 show the
comparison of tool predicted values of north Indian base stations Meerut,
Muzaffarnagar, and Saharanpur with measured values as a function
of track distance. The tool has been utilized to optimize the value of K1. The
agreement between measured and tool predicted values is reasonable. The tool
predicted values of path loss for K1 = 115 show good coincidence with average
values of observed path loss over northern India
and this is considered as an
optimally tuned value. This is basically due to similarity in type of
environment and terrain for all the base stations.Figures8, 9, 10, 11, and 12 depict the
comparison of tool predicted values with measured values of path loss for west
Indian base stations of Vangani, Neral, Karjat, Talegaon, and Pune base
stations. In these figures, the
tool has been utilized to optimize the K1 ranging from 110 to 130 depending on
the individual base station. The evaluation of the suitability of tool mean
prediction error and standard deviations for various K1 values is shown in Table 4.Table 4
Standard deviations and mean errors of the predictions of radio planning tool (dB).
Stationme110sd110me115sd115me120sd120me125sd125me130sd130Meerut0.725.32Muzzafarnagar−1.236.92Saharanpur−2.044.42Vangani6.0110.66−8.9910.66Neral−2.0320.87Karjat6.927.62−3.087.54Pangoli−13.1117.37−23.1117.37Talegaon0.727.93Pune−1.327.62−6.157.32Standard Deviations of the Predicted Errors from the Tool
The standard deviations and
mean error of the prediction errors of the tool are deduced and shown in Table4.
In Table4, me is mean
error, sd is standard deviation; 110, 115, and so forth indicate the values of
K1. In the case of north Indian stations for K1 = 115, Saharanpur
exhibits the lowest value of
standard deviation 4.4 dB and the deviation over north varies from 4.4 to 6.9.
In the case of west Indian paths, the standard deviation varies from 7.3 to
20.87, with Pune
base station exhibiting the lowest value of standard deviation. Vangani base station was
tuned for K1 values of 115 and 130, Karjat for 115 and 125, and Pune for 115.
The two values of K1 seen are due to the slightly change in environment type on
the two sides of each base station.
Similarly base stations, Neral tuned for K1 value of 120, Pangoli for
110 and 120, and Talegaon for 120. When the environment is of rolling plains with open
type like typical north Indian
and in some parts of west India, K1 value of 115 seems to be the optimally tuned value. For
rough terrain, K1 value of 125 or 130 can be a better value. High standard
deviations reported for Pangoli and other base stations could be due to the
rough nature of the terrain. If the standard deviation of Hata's method in
Table 1 is compared with standard deviation of tool, it seems that Hata's method
still gives marginally better values except for Vangani base station.


## 3.1. Standard Deviations of Prediction Methods

Mean errors and standard
deviations of the prediction errors for all the methods have been deduced and
shown in Table1.The general practice in
radio wave propagation modeling is that when a given model is exhibiting a
highest standard deviation, it is considered not suitable and when it is having
a low standard deviation, it is considered suitable in that region. From
Table1, the following observations have been made. In
the northern India, Hata's method showed less standard deviation for all the three paths. Hata's
method also exhibited less mean error
compared with Egli, Blomquist and Ladell, and ITM methods. The negative sign
before mean error shows that the
given model is over estimating the path loss. In the case of west Indian paths,
Hata's method followed by ITM method gave less standard deviations. Hata's
method exhibited higher mean error and higher standard deviations for west
Indian paths. This could be due to the difference in terrains between northern
and western Indian regions. In the northern region, the terrain was flat, open
whereas in west India,
it was rough in nature as in the case of Pangoli and Talegaon base stations. In
the case of Pune base station where suburban conditions prevailed, all the
methods predicted standard deviation within 5–7 dB. Similarly,
in the case of Neral base station where there is smooth variation of terrain
all the prediction methods yielded a deviation between 4–5 dB. In the case of
Pangoli and Talegaon high standard deviations were observed due to the varying
and rough nature of terrain.


## 3.2. Investigation by Diffraction Mechanism

Rec-526 of ITU-R [10] is used to investigate
propagation by diffraction and knife edge diffraction. Since no mountains and
hills are there along the path knife edge diffraction has not been utilized.
Only spherical diffraction has been used. No corrections are used for
vegetation. The terrain is flat with rolling plains along the track and not
much vegetation is seen. We made an
attempt to compute the losses due to diffraction using the above Rec-526 for
various paths beyond the line-of-sight distances. The losses obtained and their
comparison with measured losses are given in Table 2. In the
table, distances are in km and losses are in dB.Table 2
Computed and observed diffraction losses for north and west Indian paths (dB).
North Indian pathsMeerutMuzzafarnagarSaharanpurDis (km)Obs lossDiff lossDis (km)Obs lossDiff lossDis (km)Obs lossDiff loss(dB)(dB)(dB)(dB)(dB)(dB)32.5129.5144.632.5126.5144.632.5130144.635130146.835127.5146.835135146.837.5133148.937.5128148.937.5135148.940135150.540136150.540139150.542.5140151.942.5141151.942.5140151.945143.5153.845142153.845141.5153.847.514415747.514415747.514215750145158.450143.5158.4West Indian PathsPangoliTalegoanPuneDist. (km)Obs. lossdiff. lossDist. (km)Obs. lossdiff. lossDist. (km)Obs. lossdiff. loss(dB)(dB)(dB)(dB)(dB)(dB)31122142.53014012731.513614232118142.632.515612833145143.333141143.334139143.935135144.536126145.63811514740110148.54211415044107150.345127151.5Here diffraction losses
are computed for distances beyond line-of-sight zones. For each path a radio
horizon distance has been computed and beyond this distance diffraction losses
have been deduced. A perusal of the above table indicates that in the case of
north Indian paths, Meerut base station
exhibited deviation of 10–15 dB,
Muzzafarnagar exhibiting 10–18 dB, and Saharanpur
base station
showing a deviation of 12–15 dB. These
large deviations show that signal propagation at these distances is not
supported by diffraction mechanism. In the case of west Indian paths Pangoli
and Talegaon base stations exhibited large deviations of the order of 20 dB and
above. Only Pune base station where only two experimentally measured values are
available beyond the los zone, some kind of agreement is seen between
diffraction computed and measured path losses. Probably only in this case
beyond 30 km propagation occurred through diffraction mechanism.


## 3.3. Investigations with Radio Planning Tool

Using the digital terrain data with a resolution of 50 m and Aircom's asset tool [11] clutter,
terrain and environmental photographs, signal coverage plots for different base
stations have been generated. Clutter maps, terrain maps for entire northern
and western base stations are shown in Figures 1–4. Figure 1 shows
the clutter map of north Indian base stations Meerut, Muzaffarnagar, and Saharanpur
base stations along the track. Meerut base station is surrounded by low density urban environment. As one moves along
the track from Meerut to Muzaffarnagar base station, agricultural lands and very low density
vegetation are seen. At intermittent distances rural villages are located. From
Muzzafarnagar base station to Saharanpur
base station also agricultural lands along with rural features are seen. All
the base stations are surrounded by low density urban environment up to small
distances. Figure 2 depicts the terrain variation of these north Indian base
stations. Meerut base station is situated at a
distance of 225 m above mean sea level, Muzaffarnagar situated at a height of
243 m above mean sea level, and Saharanpur
located at a height of 275 m above mean seal level. As one progresses, the
terrain is rising and there is a gradual upward slope.Figure3 shows the clutter map of west Indian base
stations Vangani, Neral, Karjat, Pangoli, Talegaon, and Pune. From Vangani to
Neral, open regions with low density vegetation are seen along the track.
Similar environment between Neral to Karjat is seen. From Karjat to Pangoli
open area with low height vegetation is seen. This is not tall enough to block
out radio waves and apply foliage losses. The region between Pangoli and
Talegaon is filled with moderate agricultural lands and partly open regions.
From Pangoli onwards the railway track enters the hilly terrain. The region between Talegaon and Pune is
also open and agricultural region. Near Pune base station low density urban
environment is seen. Figure 4 shows the terrain variation of all these west
Indian base stations. Vangani is situated at a height of 40 m above mean sea
level, Neral at 40 m, Karjat at 80 m, and Pangoli at 600 m. There is a steep
upward slope towards Pangoli. At Talegaon, the mean sea level height  is 600 m and at Pune the corresponding value
is 560 m.


### 3.3.1. Propagation Model

The propagation model employed in this tool [11] is illustrated as follows.(1)Pathloss=K1+K2log(d)+K4(Hms)+K4log(Hms)+K5log(Heff)+K6log(Heff)log(d)+K7(diffn)+ClutterLoss, where d is distance between base to
mobile station (Km); Hms is height of the mobile station above ground (meter).
This figure may be specified either globally or for individual clutter
categories. Heff is effective base station antenna height (meter). Diffn is
diffraction loss calculated using Epstein, Peterson, Deygout, or Bullington
equivalent knife edge methods. K1 and K2 are
intercept and slope. These factors correspond to a constant offset (in dBm) and
a multiplying factor for the log of the distance between the base station and
mobile. K4 is mobile antenna height factor. It is a correction factor used to take into account the
effective mobile antenna height. K4 is Okumura-Hata multiplying factor for Hms.
K5 is effective antenna height gain. This is the multiplying factor for the log
of the effective antenna height K6. This is the Okumura-Hata type multiplying factor
for log(Heff)log(d). K7 takes into account diffraction effects. A choice of diffraction methods
is available.C_loss is clutter specifications such as heights and separation that
are also taken into account in the calculation process. Clutter losses shown in Table3 have been obtained based on our past experiences of conducting radio
measurements in Indian rural zones. Low density urban = 1 dB, village = 1 dB,
high density vegetation = 4 dB, low density vegetation = 1 dB, agriculture = 0 dB,
open area = −5 dB, quasiopen = −5 dB, and water = −15 dB.
The parameters of the tool at various frequencies are shown in Table 3.Table 3
Parameters of the Aircom tool at various frequencies.
Parameter450 MHz900 MHz1800 MHz2000 MHzMobile Rx height(m)1.51.51.51.5Earth radius(Km)8493849384938493K1142.3150.6160.9162.5K244.944.944.944.9K4−2.6−2.55−2.55−2.55K4−8000K5−11.7−13.82−13.82−13.82K6−4.3−6.55−6.55−6.55K70.40.70.80.8From Table3,
it is seen that K2 is independent of
frequency. Keeping K2 to K7 values
corresponding to 450 MHz (this being nearest to 320 MHz in the present study),
an attempt has been made to tune the value of K1 with the help of measured
values. Various values of K1 corresponding to different paths are deduced and
signal coverage plots for various base stations have been generated. In these
entire figures, tool generated values are confined to distances of 35 km. This
is due to the inherent limit set in the tool by the manufacturer when it is
being used in GSM mode. Comparison of tool predicted losses for various values
of K1 with measured losses is shown in Figures 5, 6, and 7 for north
Indian base stations and in Figures 8, 9, 10, 11, and 12 for west
Indian base stations. The figure for Pangoli base station is not shown due to
restriction of number of figures. The advantage of these tuned values over
these regions is that these can be used by the future operators of mobile
communications for this and similar regions.Figure 5
Comparison of tool predicted losses with observed losses versus track distance for Meerut  base station.Figure 6
Comparison of tool predicted losses with observed losses versus track distance for
Muzaffarnagar base station.Figure 7
Comparison of tool predicted losses with observed losses versus track distance for Saharanpur  base station.Figure 8
Comparison of tool predicted losses with observed losses versus track distance for Vangani base station.Figure 9
Comparison of tool predicted losses with observed losses versus track distance for Neral base station.Figure 10
Comparison of tool predicted losses with observed losses versus track distance for
Karjat base station.Figure 11
Comparison of tool predicted losses with observed losses versus track distance for
Talegaon base station.Figure 12
Comparison of tool predicted losses with observed losses versus track distance for
Pune base station.


### 3.3.2. Comparison of Tool Predicted Values with Observed Losses

In the figures, measured data have
been processed in accordance with the resolution of the prediction tool. Figures5, 6, and 7 show the
comparison of tool predicted values of north Indian base stations Meerut,
Muzaffarnagar, and Saharanpur with measured values as a function
of track distance. The tool has been utilized to optimize the value of K1. The
agreement between measured and tool predicted values is reasonable. The tool
predicted values of path loss for K1 = 115 show good coincidence with average
values of observed path loss over northern India
and this is considered as an
optimally tuned value. This is basically due to similarity in type of
environment and terrain for all the base stations.Figures8, 9, 10, 11, and 12 depict the
comparison of tool predicted values with measured values of path loss for west
Indian base stations of Vangani, Neral, Karjat, Talegaon, and Pune base
stations. In these figures, the
tool has been utilized to optimize the K1 ranging from 110 to 130 depending on
the individual base station. The evaluation of the suitability of tool mean
prediction error and standard deviations for various K1 values is shown in Table 4.Table 4
Standard deviations and mean errors of the predictions of radio planning tool (dB).
Stationme110sd110me115sd115me120sd120me125sd125me130sd130Meerut0.725.32Muzzafarnagar−1.236.92Saharanpur−2.044.42Vangani6.0110.66−8.9910.66Neral−2.0320.87Karjat6.927.62−3.087.54Pangoli−13.1117.37−23.1117.37Talegaon0.727.93Pune−1.327.62−6.157.32Standard Deviations of the Predicted Errors from the Tool
The standard deviations and
mean error of the prediction errors of the tool are deduced and shown in Table4.
In Table4, me is mean
error, sd is standard deviation; 110, 115, and so forth indicate the values of
K1. In the case of north Indian stations for K1 = 115, Saharanpur
exhibits the lowest value of
standard deviation 4.4 dB and the deviation over north varies from 4.4 to 6.9.
In the case of west Indian paths, the standard deviation varies from 7.3 to
20.87, with Pune
base station exhibiting the lowest value of standard deviation. Vangani base station was
tuned for K1 values of 115 and 130, Karjat for 115 and 125, and Pune for 115.
The two values of K1 seen are due to the slightly change in environment type on
the two sides of each base station.
Similarly base stations, Neral tuned for K1 value of 120, Pangoli for
110 and 120, and Talegaon for 120. When the environment is of rolling plains with open
type like typical north Indian
and in some parts of west India, K1 value of 115 seems to be the optimally tuned value. For
rough terrain, K1 value of 125 or 130 can be a better value. High standard
deviations reported for Pangoli and other base stations could be due to the
rough nature of the terrain. If the standard deviation of Hata's method in
Table 1 is compared with standard deviation of tool, it seems that Hata's method
still gives marginally better values except for Vangani base station.


## 3.3.1. Propagation Model

The propagation model employed in this tool [11] is illustrated as follows.(1)Pathloss=K1+K2log(d)+K4(Hms)+K4log(Hms)+K5log(Heff)+K6log(Heff)log(d)+K7(diffn)+ClutterLoss, where d is distance between base to
mobile station (Km); Hms is height of the mobile station above ground (meter).
This figure may be specified either globally or for individual clutter
categories. Heff is effective base station antenna height (meter). Diffn is
diffraction loss calculated using Epstein, Peterson, Deygout, or Bullington
equivalent knife edge methods. K1 and K2 are
intercept and slope. These factors correspond to a constant offset (in dBm) and
a multiplying factor for the log of the distance between the base station and
mobile. K4 is mobile antenna height factor. It is a correction factor used to take into account the
effective mobile antenna height. K4 is Okumura-Hata multiplying factor for Hms.
K5 is effective antenna height gain. This is the multiplying factor for the log
of the effective antenna height K6. This is the Okumura-Hata type multiplying factor
for log(Heff)log(d). K7 takes into account diffraction effects. A choice of diffraction methods
is available.C_loss is clutter specifications such as heights and separation that
are also taken into account in the calculation process. Clutter losses shown in Table3 have been obtained based on our past experiences of conducting radio
measurements in Indian rural zones. Low density urban = 1 dB, village = 1 dB,
high density vegetation = 4 dB, low density vegetation = 1 dB, agriculture = 0 dB,
open area = −5 dB, quasiopen = −5 dB, and water = −15 dB.
The parameters of the tool at various frequencies are shown in Table 3.Table 3
Parameters of the Aircom tool at various frequencies.
Parameter450 MHz900 MHz1800 MHz2000 MHzMobile Rx height(m)1.51.51.51.5Earth radius(Km)8493849384938493K1142.3150.6160.9162.5K244.944.944.944.9K4−2.6−2.55−2.55−2.55K4−8000K5−11.7−13.82−13.82−13.82K6−4.3−6.55−6.55−6.55K70.40.70.80.8From Table3,
it is seen that K2 is independent of
frequency. Keeping K2 to K7 values
corresponding to 450 MHz (this being nearest to 320 MHz in the present study),
an attempt has been made to tune the value of K1 with the help of measured
values. Various values of K1 corresponding to different paths are deduced and
signal coverage plots for various base stations have been generated. In these
entire figures, tool generated values are confined to distances of 35 km. This
is due to the inherent limit set in the tool by the manufacturer when it is
being used in GSM mode. Comparison of tool predicted losses for various values
of K1 with measured losses is shown in Figures 5, 6, and 7 for north
Indian base stations and in Figures 8, 9, 10, 11, and 12 for west
Indian base stations. The figure for Pangoli base station is not shown due to
restriction of number of figures. The advantage of these tuned values over
these regions is that these can be used by the future operators of mobile
communications for this and similar regions.Figure 5
Comparison of tool predicted losses with observed losses versus track distance for Meerut  base station.Figure 6
Comparison of tool predicted losses with observed losses versus track distance for
Muzaffarnagar base station.Figure 7
Comparison of tool predicted losses with observed losses versus track distance for Saharanpur  base station.Figure 8
Comparison of tool predicted losses with observed losses versus track distance for Vangani base station.Figure 9
Comparison of tool predicted losses with observed losses versus track distance for Neral base station.Figure 10
Comparison of tool predicted losses with observed losses versus track distance for
Karjat base station.Figure 11
Comparison of tool predicted losses with observed losses versus track distance for
Talegaon base station.Figure 12
Comparison of tool predicted losses with observed losses versus track distance for
Pune base station.


## 3.3.2. Comparison of Tool Predicted Values with Observed Losses

In the figures, measured data have
been processed in accordance with the resolution of the prediction tool. Figures5, 6, and 7 show the
comparison of tool predicted values of north Indian base stations Meerut,
Muzaffarnagar, and Saharanpur with measured values as a function
of track distance. The tool has been utilized to optimize the value of K1. The
agreement between measured and tool predicted values is reasonable. The tool
predicted values of path loss for K1 = 115 show good coincidence with average
values of observed path loss over northern India
and this is considered as an
optimally tuned value. This is basically due to similarity in type of
environment and terrain for all the base stations.Figures8, 9, 10, 11, and 12 depict the
comparison of tool predicted values with measured values of path loss for west
Indian base stations of Vangani, Neral, Karjat, Talegaon, and Pune base
stations. In these figures, the
tool has been utilized to optimize the K1 ranging from 110 to 130 depending on
the individual base station. The evaluation of the suitability of tool mean
prediction error and standard deviations for various K1 values is shown in Table 4.Table 4
Standard deviations and mean errors of the predictions of radio planning tool (dB).
Stationme110sd110me115sd115me120sd120me125sd125me130sd130Meerut0.725.32Muzzafarnagar−1.236.92Saharanpur−2.044.42Vangani6.0110.66−8.9910.66Neral−2.0320.87Karjat6.927.62−3.087.54Pangoli−13.1117.37−23.1117.37Talegaon0.727.93Pune−1.327.62−6.157.32Standard Deviations of the Predicted Errors from the Tool
The standard deviations and
mean error of the prediction errors of the tool are deduced and shown in Table4.
In Table4, me is mean
error, sd is standard deviation; 110, 115, and so forth indicate the values of
K1. In the case of north Indian stations for K1 = 115, Saharanpur
exhibits the lowest value of
standard deviation 4.4 dB and the deviation over north varies from 4.4 to 6.9.
In the case of west Indian paths, the standard deviation varies from 7.3 to
20.87, with Pune
base station exhibiting the lowest value of standard deviation. Vangani base station was
tuned for K1 values of 115 and 130, Karjat for 115 and 125, and Pune for 115.
The two values of K1 seen are due to the slightly change in environment type on
the two sides of each base station.
Similarly base stations, Neral tuned for K1 value of 120, Pangoli for
110 and 120, and Talegaon for 120. When the environment is of rolling plains with open
type like typical north Indian
and in some parts of west India, K1 value of 115 seems to be the optimally tuned value. For
rough terrain, K1 value of 125 or 130 can be a better value. High standard
deviations reported for Pangoli and other base stations could be due to the
rough nature of the terrain. If the standard deviation of Hata's method in
Table 1 is compared with standard deviation of tool, it seems that Hata's method
still gives marginally better values except for Vangani base station.


## 4. Conclusions

The main advantage of this comparison
with various prediction methods whether empirical or semiempirical is the
possible extrapolation of these measurements to similar environments in the
related band of frequencies and different base station antenna heights. In the
rural/suburban areas,
the cellular operators are generally under the notion that same prediction
technique holds good in every region of the country. The study showed that
different prediction methods yielded different degrees of prediction errors in
northern and western zones. In the northern region, Hata's method  showed a standard deviation
of 3–5 dB, Blomquist
and Ladell's method showed 4-5 dB except for Muzzafarnagar base station where
large standard deviation is seen.  ITM
and Egli's methods showed small standard deviations for Meerut and Saharanpur
paths and showed high value of deviation for Muzzafarnagar path. In the western
region, Hata's method showed 5–11 dB, ITM showed
6–24 dB, Blomquist and Ladell's method showed 5–19 dB, Egli's
method showed 5–21 dB. It seems that prediction models
along rural railroads give good agreement when the base station antenna is
located on the top of the building or hill giving fairly good los distance to
many points. In the non-los zone, accuracy becomes less and correction terms
become necessary [12]. It seems that Rec-526 of ITU-R
method for spherical diffraction is unable to explain the observed losses in
the diffraction region satisfactorily.Using Aircom's asset radio planning tool and
digital terrain data, coverage plots for all the base stations have been
generated and the intercept factor K1 has been tuned. Photographs of clutter
and environment have also been presented. In the case of north Indian base stations, a uniform value of K1 = 115 is
found to be suitable in explaining the observed results due to the smooth
nature of terrain. In the case of west Indian paths, due to the rough nature of
terrain two or three values of K1 ranging from 115 to 130 are required in
explaining the observed results. The highlight of the present study is deducing
optimally tuned values of K1 using Aircom's asset radio planning tool. These
results can be utilized for designing future communication systems and for wave
propagation modeling over developing countries in similar environment type
regions. In the future study, the modified parameters of the radio planning
tool  can be utilized to predict the
signal levels/path losses in these regions and can be compared to future datasets which will be generated in this region at various frequencies.



---

*Source: 285763-2008-07-06.xml*