CELLULAR RADIO NETWORK PLANNING
A. Gamst and E.-G. Zinn
Philips GmbH Forschungslaboratorium
Hamburg, F.R.G.
R. Beck and R. Simon
Philips Kommunikations Industrie
Nurnberg 1, F.R.G.
ABSTRACT
The fundamental ideas behind a cellular network design
program are described. By the aid of a generalization of the
notion of a radio cell it becomes possible to integrate hitherto
isolated areas of investigation. The elementary design steps
makig u th prgra ar ouline toethr wth hei inerwith new
outbe used
theirneted to design
upThe
Theprogram
program may be
connections.
works, analyze or extend existing ones, and study system var-
tioners to question the value of theoretical models entirely
(see e.g. [15]). Their critique has usually been connected with
item two in our list, i.e. nonhomogeneous propagation condi-
a bee deveop ed,foslo
strengthspredctionrprogram
ramsmasrement [20land, the of
sthe
bedion
pok
them based on Okumura's measurements [20] and the forrnuderived from them by Hata [13]. It appears, however, that
las
togdet
inetemare
makntiong
teeporm aermie ahrioae ol.O h
tork de
hodebe earathat
therehand,rit
cellular network
clear that cellar
other
hand, it should
design
ians
esin sraegis
and ad
design
strategies iin agenralnohomgenou
There
are
addifield
with
strength
prediction.
not
stop
ageneralnonhomogeneousdoes
ssetting.
et iant
tional steps, as will be outlined below, and they have to be
.
INTRODUCTION
The regular hexagonal cell lattice, equipped with the familiar doubly periodic frequency reuse pattem, has dominated
network concepts for cellular mobile radio systems for quite
some time [6], [1], [19], [17]. System alternatives such as dynamic frequency assignment [21] [16], sectorized cells [18],
[19], reuse partitioning [12], and others have usually referred
to this simple model, and popular presentations have almost
inevitably visualized cellular networks as hexagonal structures. The explanation for this phenomenon seems to be obvious, since such an arrangement of base stations and frequencies provides at the same time the most economic
covenng of the plane by congruent circles and the densest
packing of co-channel cells packing of co-channel cells [8].
There are, however, at least four good reasons why attention should also be paid to other types of cell systems:
* Traffic density cannot be assumed constant throughout.
* Radio wave propagation is practically never homogeneous and isotropic.
* Base station sites sometimes cannot be chosen arbitrarily.
* The traffic region usually has a boundary.
To overcome the first of these problems, the so-called cell
splitting method has been devised [7], [19], [23]. Its building
blocks are, however, still hexagonal cells (and their rudiments). So far it is not known whether networks generated
with this method enjoy any of the above-mentioned virtues of
the homogeneous lattice in a suitably generalized sense.
Except for this single invention no general theoretical
model has been published that can deal with any of the items
listed above. This unsatisfactory state of affairs has led practi-
18].da.
tied together with the preceding propagation calculations.
The present contribution describes the program package
GRAND (Generalized RAdio Network Design), a software
tool for the study of various aspects of nonhomogeneous networks (see also [22] for a different point of view). Using a
generalization of the concept of a radio cell, it provides a
framework for integrating the three basic design tasks.
0 generation of base station configurations,
cellular analysis(including propagation, coverage,
traffic, and interference),
0 frequency assignment
into a coherent rocess. A highl dialogue-oriented structure
a
ented
Anipulyofdialgem
gllo
network
radio strk
elementary
for flexible manipulation
~allows forexible
data.
STRUCTURE OF GRAND
t
In general terms, GRAND enables its user to generate radio
network data (output) for a given set of customer data
and system parameters (input). Furthermore, GRAND supports the verification of system requirements and the evaluation of cost functions.
The customer data specify the
* traffic density,
0 geographic height, and
* morphostructure (surface classification, such as urban,
suburban, etc.)
within the traffic region under consideration.
Among the system parameters there are at least the
* frequency band,
A similar version of this paper was presented at the 1985 Vehicular Technology Conference.
0885-8985/86/0200-0008 $1.00 ©1986 IEEE
8
IEEE AES Magazine, February 1986
* number of available frequencies,
* required S/N ratio,
* required co-channel S/I ratio,
* traffic table (giving the grade of service).
Optional parameters are antenna characteristics, fixed base
stations, adjacent channel S/I, and other restrictions. The radio network data supplied by GRAND include
* the number of base stations
* for each base station, its
- location,
- antenna height,
- transmitter power,
- antenna type,
- lobe direction,
-assigned frequencies.
Thus, if e.g. directional antennas are employed, there may
be base stations differing from each other only with aspect to
their lobe directions and assigned frequencies.
The three fundamental system requirements verified by
GRAND are
* coverage
* grade of service
* non-interference.
More precisely, the user will be informed whether the radio
network constructed by the aid of GRAND already provides
sufficient coverage, and whether it is possible to find a feasible frequency assignment, on the basis of the grade of service
as prescribed by the traffic table and the degree of non-interference as prescribed by the S/I system parameters, with no
more than the number of available frequencies.
To achieve these goals, the program cycles through the ten
separate steps shown in Figure 1. Each of these steps is characterized entirely by its input and output data structures. This
characterization of steps as black boxes makes it possible to
replace any concrete realization of such a step by another one,
e.g. the Okumura-Hata formulas by a simple r-4 propagation
law, without affecting the overall structure of the program.
Each individual step is usually a closed operation without
neorkata,
* replace computations in the next step by reading data
from a file,
etc. If radio network data have been changed, the program returns to the dialogue point immediately before the step where
these data are used first. The ensuing computations are carried
out anew only within regions where changes have taken
place.
It must be emphasized that the integration of steps as
shown in Figure 1 would not have been possible with the traditional concept of the radio cell as a classical plane point set.
Instead of this, the program adopts the view that a cell is a
fuzzy set[24], i.e. there is no strict distinction between 'inside'
and 'outside', but rather a continuous transition. In our case,
the degree of membership is defined via selection rates (see
below), which implies that the new concept can model much
more accurately what happens under field conditions. As a
consequence, however, of this approach there will exist no
strictly defined cell boundaries.
DESCRIPTION OF PROGRAM STEPS
Initialization
This step merely serves to generate parameters driving the
next step. For example, if a regular hexagonal cell system is
desired, the cell radius will be estimated here. The step has
been separated from the following to enable the user to ma-
nipulate such parameters at the dialogue point between them.
Base Station Configuration
In this step a set of base stations with attributes
0 location
* antenna height
* transmitter power
0 antenna type
IBaselnitializatin
0 lobe direction
is generated. The present version of GRAND offers homogeneous hexagonal configurations and a cell splitting method
Base Station Configuration
zizz
irizzi
IPropagation Ana
Propagation
sIs
Analysis
I(with
splittingautomatic
based onnetwork
traffic desities).
thesewill
data-driven
later from
versions
generatorsApart
Radio Coverage
Selection Rates
ItaffIc per Cell
Channels per Cell
Interference Analysis
any possibility of interaction by the user. Between the steps
there are dialogue points where the user may choose among
different activities,
displayavi e.g.ti
on,
* miplat raio
* manipulate radio network data,
* perform next step,
|
also include the more flexible dialogue-driven approach of the
Ssoap bubble' or potential method, a description of which will
be published in the near future. All data generated in this step
can be manipulated by the user at the subsequent dialogue
point.
Propagation Analysis
The purpose of this step is to predict the median field
*~~~~strength of signals transmitted from the base stations within
I Local Frequency Demand
Frequency
Assignment
rqec
Asgm n
|the traffic region. The standard version of this step emnploys
Figure 1. Program steps in GRAND
be replaced by measurements (if available).
lI
IEEE AES Magazine, Februlary 1986
extended
of the Okumura-Hata
model
[2]. For aversion
quick evaluation
of trends, apropagation
simple r-4 law
can
~ bean~ [3] chosen.
Furthermore, calculated field strength values may
9
Radio Coverage
Radio coverage is assumed to be achieved if the required
S/N ratio (or a minimal field strength value) is exceeded with
a specilied probability. On a global scale, this must hold for a
sufficiently large portion of either the traffic region or the subscriber population. In step 4, the said excess probabilities are
calculated from the median field strength values and the shadowing parameter o [20] connected with the local morphostructure. At the ensuing dialogue point, the local coverage
information thus obtained may be displayed and judged by the
user, and global coverage information can be derived.
Selection Rates
This step generates the radio cells in the generalized sense
introduced in section 2. For each field point in the traffic region, the strength of signals received from all nearby base stations are compared, and for each of these potential servers the
selection rate [14], i.e. the probability that a mobile visiting
this point will be served by him, is calculated. This probability will depend of course on the median field strength levels
and the local shadowing parameter u, but it is also strongly
influenced by the hand-off algorithm used in the system [25].
Traffic Per Cell
Once the radio cells have been defined in termsofeselecon
rates, the traffic to be served by a base station iS easily computed as the integral of the product of traffic density times selection rate over the neighborhood of the base station.
Channels Per Cell
For each base station itS associated traffic iS now compared
to the value ofsan s ternally dfir traficytable s aeresu
r the
of f
c c
this
sary
tof crison,
serve
esnerce
traffic is obtained. Thsthedgrads(e. blofcsvic
sarye
can
betosrved
forced this
to conform
to certain standards (e.g. blocking
rates) in each cell by using an appropriate traffic table.
Interference Analysis
This step serves to compute, for each pair of radio cells,
the degree of mutual interference. As in the coverage calculations, there are (at least) two possible ways of defining the
interference level in a cell given in terms of selection rates.
The first definition determines the average of the probability
of exceeding a specified S/I threshold, weighted with the selection rate, over the cell area, i.e. the region where the selection rate is nonnegligible. This might be called an area-oriented definition. The second definition uses the selection rate
times the traffic density as its weight function, i.e. a trafficoriented approach. Classical definitions often refer to excess
rates on the cell boundaries, which makes no sense in our
context.
The averaged probabilities thus obtained are then compared
to a given threshold value (which is another system parame-
Local Frequency Demand
The final two steps serve to compute the number of frequencies which are needed to assign to each cell the number
of frequency channels determined in step 7 while observing
the reuse constraints collected in the compatibility matrix. In
principle, this combinatorial problem cannot be solved exactly
due to its inherent computational complexity [11]. Therefore
the task is split into two subtasks, viz. generating lower and
upper bounds on the frequency demand. In the ninth step, the
lower bounds are computed by analyzing the demand on a local basis, i.e. in the neighborhood of each cell. The basis estimate is the clique number [5], which can easily be computed
with well-known algorithms [4]. The clique number represents
the
maximal local frequency
demand due toteco-channel
srit;fretmtsi
oncinwt
osritcon-e
sth
forthcomingo[9].
the forthcoming [9].
Frequency Assignment
In this final step, an arbitrary number of frequency assignments may be generated, each giving rise to an upper bound
on the frequency demand. The procedure stops when either
the largest lower bound from step 9 has been reached (in
which case an optimal frequency assignment has been found)
or when the user decides so. Experience with this type of approach
has the
shown
that in between
most cases
will occur,
convergence
difference
lower bound
and
and if not,
largest
smletuprbndwlingealeisgifct[1]
smallest upper bound will in general be insignificant [10].
CONCLUSION
The program package described in this paper aims at integrating
network models,
generation
realistic
field
andoffrequency
To achieve
strengththe
prediction,
assignment.
this, a generalized (but natural) new definition of the radio
cell has been introduced linking these so far unconnected aspects
together. A hmodular
ipoeeto
ol structure facilitates the continuous
of the tool.
improvement
GRAND may be viewed as a 'shooting' approach to find a
radio network satisfying the system requirements mentioned in
section 2. The program will support the user in verifying
whether this has been achieved, but the mechanism driving to
a solution must be supplied by the user himself. This holds all
the more if the network to be constructed has to be optimal in
terms of a particular cost function.
In other words, GRAND is not an automatic cellular network generator. Efficient design strategies still have to be
found. It is one of the purposes of GRAND to offer an environment for testing them.
ACKNOWLEDGEMENT
This work was supported in part by the Bundesministerium
fur Forschung und Technologie under grant no. TK 01769.
REFERENCES
ter). Inacceptable interference is assumed if the threshold is
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so-called compatibility matrix [10]. The entries of this matrix
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[21 Beck, R.: Wellenausbreitung fur 900 MHz-Mobilfunksysteme. Vorhersage uhnd MNessungg;NTG-Fachtagung 'Bewegliche Funkdienste',
interference is detected between two cells, the corresponding
[3] Beck, R.; Schmidt, W.: Funknetzplanung fur Mobile Automatische Tele-
adjacent channel interference, the matrix entry must be in-
[4] Bron, C.; Kerbosch, J.: Finding All Cliques of an Undirected Graph
(Algorithm 457); Conmm. ACM, vol. 16 (1973), 575-577.
exceeded. The occurrence of this event is registrated in the
use the same frequencies. If, however, too much co-channel
entry in the matrix will be set to 1. If there is even too much
creased to 2.
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phone System; Rev. ECL, vol. 16 (1968), 357-373.
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fossee TEK
ETcn
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[5] Christofides, N.: Graph Theory. An Algorithmic Approach: London etc.:
Academic Press 1975.
[6] Fastert, H.W.: Die mathematischen Grundlagen der theoretischen Sendernetzplanung; Rundfunktechn. Mitt., vol. 4 (1960), 48-56.
[7] Frenkiel, R.H.: Cellular Radiotelephone System Structured for Flexible
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.:.Emirialormlaorropgatonoin
[13] Hata, M.:13HataM
Empirical
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[18] Lorenz, R.W.: Kleinzonennetze fur den Mobilfunk, Nachrichtentechn.
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[19] MacDonald, V.H.: The Cellular Concept; BSTJ, vol. 58 (1979), 15-41.
[20] Okumura, Y.; Ohmori, E.; Kawano, T.; Fukuda, K.: Field Strength and
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[25] Zimmerman, G.: Efficiency of Hand-Off Strategies in Cellular Mobile
Automatic Telephone Systems Using Signal Strength Measurements; internal report (in German), to be published.
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