Computation of ideal location for 3G communication towers in urban

areas on web based 3D environment

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Ajaze Parvez Khan

This paper focuses on finding ideal location of 3G mobile

towers for a given coverage which will cover maximum

population. Factors of particular importance include coverage

and capacity issues in the planning process for cellular 3G

networks. A novel algorithm based on weighted K-Means has

been developed for obtaining optimal location of

telecommunication towers in Dehradun municipal area.

Probable optimal sites for tower installation are obtained on 3D

in a web environment. Results are compared with industry

software and the advantage is analyzed.

Abstract

Introduction

Furthermore, area coverage and population catered/capacity planning should be

performed in tandem since capacity requirement and traffic distribution influence

the coverage. Due to changing needs in telecom sector, high costs and the scarcity

of installation sites, new algorithms are required to obtain ideal locations for tower

installation.

In recent years, web-based three-dimensional (3D) GIS for visualizing geospatial

data have attracted many researchers. Hence implementation for establishing

optimal 3G network is carried out using web services.

Since elevation factor is vital for tower installation the results are presented on 3D

utilizing Digital Elevation Model (DEM). Availability of network planning application

on 3D networked environment for users will save cost, time and effort which is too

high when done through 2D parameters like paper maps and sketches obtained

from field surveys.

Due to immense growth of telecommunications technologies, systems, and

services upcoming wireless technologies come with enhancements like high-

speed transmission, advanced multimedia access and global roaming

requiring effective network planning.

The wireless technology, 3G radio network, also referred to as

Universal Mobile Telecommunication System (UMTS) [6], is extensively

adopted to fulfil user requirement for innovative services such as enhanced

and multimedia messaging through high-speed data channels.

The network planning strategy of 3G networks [1], [2], [3], [4] and latest 4G is

very different from strategies for planning previous generation networks,

since all carriers in the network use the same frequency range, frequency

planning is not required.

Network planning involves establishment of ‘cells’ which is the area served

by a base station through its transmission. The cells may be characterized as

macro-cells, micro-cells and pico-cells depending upon its sizes that may

vary from 10m to 30km. Cell planning addresses the problem of logical

placement of the base stations and specifying their system parameters so

that an optimal system performance is achieved characterised mainly by:

Coverage: The radio signal coverage must be guaranteed and holes/call

drop in the coverage area should be avoided.

Capacity: In each cell, a sufficient number of channels must be available

in order to meet its traffic demand for new calls and handoffs.

Concept and problem statement

Distance of each subscriber with the towers should be such that the

subscriber always gets minimum signal to call, moreover maximum

subscribers should be serviced for a given region. Consequently cell

planning can be modelled as a clustering problem where the set of

properties being: maximum capacity for a given coverage for fixed base

stations [12].

In this paper we restrict to the planning of macrocells for 3rd generation

technology. Satellite image and DEM together corresponds to

topography, whereas, traffic demand is specified through ward wise

population density inputs. For experimental purpose the census data

employed is of year 2001.

Input: 3G parameters(Number of towers [K], Coverage, Capacity, and Population

Density File)

Obtaining ideal locations for given 3G towers

Generation of 3D model of terrain and overlay of satellite image

Algorithm: To operate on parameters entered by user and the dataset from Server.

Display of Tower location onto the 3D model

Process

Algorithm - Modified Weighted K-Means Clustering

Standard K-Means algorithm [5], [13] form clusters such that

the objective function, which is based on the Euclidean

Distance between points is minimized.

The objective function for K-Means is, k n

J = || xi(j) – cj || ² ….. (1)

j=1 i=1

Where || xi(j) – cj || ² is a chosen distance measure between a

data point xi(j) and the cluster centre cj, which is an indicator of

the distance of the n data points from their k respective cluster

centres.where,J : K-Means objective function to be minimizedcj : Centroid of the jth cluster k : Number of clustersN : Total data pointsxi

(j) : ith data point

An important parameter for K-Means algorithm is the initialization

vectors which are taken in this case as centroid of the wards (in

descending order of population density) of Dehradun city. The

population density of each ward (i.e. ratio of population of a ward to

the area of that ward) is taken as weights for weighted K-Means

estimation which ensures the participation of population in cluster

formation. The objective function for weighted K-Means algorithm then

become,

k nJ = || xi

(j) Wi – cj Wi || ² ….. (2)

j=1 i=1

where, J : Weighted K-Means objective functionWi : Population Density of ith ward

The algorithm in its present form may not achieve the required

solution and performance because of the irregular nature of

ward boundaries and prerequisite to service maximum number

of subscribers. To circumvent this problem, a cost function has

been introduced to K-Means so that it can take into account the

traffic demand and cell size. The thought is to minimize the cost

function to include the effects of cluster size and the number of

points in a cluster [14].

Cost function is given by, kC = (yi − a) ² ….. (3) i=1

where, C:Cost function for populating a cluster with more number of points than expected

a:Expected number of points in a cluster (i.e. ratio of total points to number of clusters) yi: Number of points in the ith cluster k: Number of clusters

This introduced cost function ensures that for every iteration, population at each

cluster will be more or less similar and perceive nearly equal contribution.

Thus, final modified objective function becomes, E = k1 J + k2 C ….. (4)

where,E : Final objective function to be minimized J : From Equation (2)C : From Equation (3)

k1, k2 are the normalizing factors.

In order to analyze the potential of proposed approach for ideal location

of given cellular mobile towers, a prototype web program has been

implemented in Java, Java3D [15].

Fused image of IRS-1D and digital elevation model of Dehradun region are

called as web services for topography generation. The ward wise

population density data is used for calculating the capacity/traffic

demand. The snapshot of the web based solution is presented in Figure-2.

The figure shows the best suitable position of installation of tower

thereby determining cell locations.

Results

The results, i.e., ideal location of towers obtained from the algorithm

implemented on web based 3D environment for these four locations are

compared with widely used and employed industry network planning Keima

OvertureTM software [16]. Similar inputs were given to the industry software

OvertureTM and the corresponding results are shown in Figure-4.

For four locations a coverage of 0.5 km the population (capacity) served by the

industry software is 27275 and for the same range the population served by the

approach discussed in this paper is 29234.The increment of 6.7% indicates a

considerable increase in the number of people being served due to the

placement of tower by the approach discussed in this paper.

In this paper the predicament of obtaining ideal tower locations for

urban wireless networks was examined and a web based 3D approach

to solve the problem was presented. We have modelled the location

planning problem as a clustering problem and then by applying

application based modifications to the weighted K-Means clustering

technique, determined suitable locations for specified 3G towers.

Applying the web based program based on above inputs and ideas, the

results of ideal locations for telecom towers for Dehradun municipal

area was demonstrated.

The results were compared with the industry software OvertureTM and

output indicated a considerable increase in the capacity i.e. the

customers being served.

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