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Computation of ideal location for 3 g communication towers in urban areas on web based 3d environment
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Computation of ideal location for 3 g communication towers in urban areas on web based 3d environment


Computation of ideal location for 3G communication towers in urban areas on web based 3D environment

Computation of ideal location for 3G communication towers in urban areas on web based 3D environment

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  • 1. Computation of ideal location for 3G communication towers in urban areas on web based 3D environment ----------- Ajaze Parvez Khan
  • 2. 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
  • 3. 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.
  • 4. 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.
  • 5. 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
  • 6. 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.
  • 7. 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
  • 8. 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 minimized cj : Centroid of the jth cluster k : Number of clusters N : Total data points xi (j) : ith data point
  • 9. 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 n J =   || xi (j) Wi – cj Wi || ² ….. (2) j=1 i=1
  • 10. where, J : Weighted K-Means objective function Wi : 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].
  • 11. Cost function is given by, k C =  (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.
  • 12. 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
  • 13. 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.
  • 14. 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.
  • 15. 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.
  • 16. References •ITU-"ITU Radiocommunication Assembly approves new developments for its 3G standards". Press release. • (Accessed June 2009). •Kaiser Shamim M., Islam Ezharul Md., Jamil Raihan Md.,” Capacity and Coverage Calculation Model for the UMTS”, Journal of Computer Science,Vol 1, No 1, pp 05-10, June 2007. •Venugopal V., “The Coverage-Capacity Tradeoff in Cellular CDMA Systems”, IEEE Transactions on Vehicular Technology. Vol.48, No.5, 1999. •Liang Guo, Jie Zhang and Carsten Maple,”Coverage and Capacity Calculations for 3G Mobile Network Planning”, ISBN: 1-9025-6009-4, PGNet 2003. •Alsabti K., Ranka S.,Singh V., “An efficient K-means clustering algorithm.”, Proc. of High Performance Data Mining, 1998. • Network Capacity Planning.htm, (accessed October 2010). •Jaana L, Achim W, Tomas N, "Radio Network Planning and Optimization for UMTS", Wiley, 2002. •Lin. H., Gong, J. and Wang F., 1999. Web-based three-dimensional geo-referenced visualization. Computers & Geosciences, vol. 25, pp. 1177- 1185. •OGC® Web Map Server Interface Implementation Specification Revision 1.0.0, OGC® Project Document 00-028, Open Geospatial Consortium, 2000. •Daniel Selman,” Java 3D Programming Manning Publications”, (Accessed October 2010). •Nawrocki, M.J., and Wieckowski, T.W.: „Optimal site and antenna location for UMTS output results of 3G network simulation software‟. Proc. Int. Conf. on Microwaves, Radar and Wireless Communications, Gdansk, Poland, May 2002, Vol. 3, pp. 890–893 •Huang X, Behr U, Wiesback W, “Automatic base station placement and dimensioning for mobile network planning”, Proc. Of IEEE (VTC2000- fall), pp 1544-1549, Boston, USA, September 24-28, 2000. •Alsabti K., Ranka S.,Singh V., “An efficient K-means clustering algorithm.”, Proc. of High Performance Data Mining, 1998. •Ramamurthy Harish, Karandikar Abhay, “B-HIVE”, Deptt of Elec. Engg.IIT Mumbai, Powai, India.2006 •Java 3D API,, (accessed October 2011). •, (accessed March 2011).