[ieee 2010 2nd international conference on computer technology and development (icctd) - cairo,...

2010 2nd International Conference on Computer Technology and Development (ICCTD 2010)

Optimized Algorithm for Cellular Network Planning Based on Terrain and Demand Analysis

Ali Kamar, Syed Junaid Nawaz, Mohammad Patwary, Mohamed Abdel-Maguid, and Saif-Ur-Rehman Qureshi

Faculty of Computing, Engineering, and Technology

Staffordshire University

Stafford, United Kingdom, STl8 OAD

[email protected], [email protected], [email protected],

[email protected], and [email protected]

Abstract-This paper proposes a new algorithm to solve cellular layout design problem (CLDP) taking terrain variation and traffic demand into consideration. Based on the nature of the terrain, user distribution, user demand, and growth rate, the distribution of the cells is specified. The proposed algorithm has been simulated for the area with flat terrain and compared with other existing algorithms. It is found that it reduces the cost of physical resources by 11.8 % compared with the existing solutions. It is also found that the cost of the network increases as the terrain variance increases. However, the proposed algorithm keeps the cost to its minimum values by considering repeaters to meet the traffic demand within the blind spots.

Index Tenns-Land Mobile Radio Cellular Systems, Cellular Network Planning Algorithm, CLDP

I. INTRODUCTION

Due to the increasing demand for mobile radio services, cellular network planning has nowadays become one of the

most important fields of research. The competition among the network operators forces them to reduce their operating cost, and always search for improved reliable and flexible design tools for optimal planning of future mobile radio networks. The major challenge in designing a cellular mobile network is to offer the required coverage at the minimum cost. Given the bandwidth constraint, spatial tele-traffic distribution, and terrain of a specific area, it is required to trade off between subscriber satisfaction within the network and reducing the cost of network infrastructure and resources. The physical resources of the network infrastructure are one of the major concerns along with radio resources in designing a new network or expanding an existing one. This problem is known as Cellular Layout Design Problem (CLDP) [1]. It belongs to the family of Non-Polynomial (NP) Complete problems. Therefore, its existing solutions are based on heuristic approaches. However, the optimal solution cannot be always guaranteed. Several approaches have been proposed in different research works to solve this problem. In [1], Branch and Bound algorithm has been proposed. The network area to be covered is divided into a large number of square grids of equal area called elements and containing a certain traffic demand each. These square elements are classified into sets of non-adjacent squares. This technique reduces the computational size of the algorithm, thereby making it possible to solve large-scale CLDP. In

[2], an optimization framework based on simulated annealing scheme is used for base-station selection and configuration for realistic-sized networks. The full planning steps of the cellular networks has been provided. Four neighborhood structures (move generators) are used in case of blind spots or increase in traffic demand to improve solutions for coverage and capacity, which are Hole Filler, Small Cell Removal, Cell Splitter, and Traffic Filler. In L3 J, Genetic Algorithm (GA) has been considered to provide optimal radio average within the cellular network. A group of possible solutions is specified, and the algorithm evolves to an optimum solution using the fitness function. In L4J. Tabu search is considered for the two-layer network design problem by specifying the Tabu structure and then searching for the available neighborhood structures. In [51, the work is done using Genetic Algorithm together with Tabu search, taking into consideration of wether designing a new network or extending an existing one. However, interference has been neglected. GA and Tabu search are also used in [6] and compared according to their performance. In [7], a solution was proposed using Greedy Algorithm (GR), which starts in a decreasing order from the areas containing the highest traffic demand and offering the required traffic to them. Analysis for both Genetic and Greedy algorithms has been considered in [8], and a new algorithm called The combination algorithm for total optimization (CAT) is proposed based on the combination of both of them. CAT was improved later in [9] by using a new approach based on the introduction of control nodes which represent the capacity and coverage requirements in this area. In [10], a new cell planning method is proposed based on active contour theory to optimize the cell locations and coverage area of the cell. Other research studies were specific to third generation (3G) mobile networks such as [11], in which an algorithm is proposed to solve the problem of deploying 3G UMTS networks in areas that are already containing 2G GSM networks. In [12], the authors proposed the required planning and implementation for the evolution from 2G (GSM) to 3G (WCDMA-based UMTS) and analyzes the process of radio network planning stating all its steps. In [13], the authors introduce the usage of a multi-objective evolutionary algorithm for WCDMA network planning based on determined weight and sub-regional search. In [14], radio network planning with smart antennas for UMTS WCDMA

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Fig. I. (a) A 3D image of the network planning area (b) The three square grids into which the planning area should be partitioned: The terrain grid represents

the height above the sea level for each unit square of side' t', the demand grid represents the traffic demand for each square of side' d', and the cell planning grid represents the squares of side' a' from which we are going to choose the best positions for the base stations (BS's).

is presented. In [15], a study for genetic algorithms to solve the Antenna Placement Problem (APP) in UMTS networks is presented.

One of the key features of the 3G cellular network is to provide multirate data services to the user or user group, which also varies from one geographical area to another. However, none of the existing solutions has considered the terrain variation and multi-service demand of the network in their optimization solution.

This paper proposes an algorithm in the 3G cellular network planning for an area containing a various traffic demand, and with taking the terrain into consideration. The work proceeded in this paper is targeted to minimize the required physical resources that can provide the sufficient traffic capability of the network according to variable demand from the subscriber/user together with the assured coverage all over the geographical area to be covered. The rest of the paper is organized in the following order: Section II formulates the problem mathematically with a system model. The proposed solution is listed in section III. Section IV shows the simulated results for different cases, and finally conclusion is drawn in section V.

II. SYSTEM MODEL: PROBLEM FORMULATION

The terrain shown in Fig lea) has been considered with length l and width w. All the used parameters are defined in Table I. From Fig l(b), it is shown that the area is represented by three matrices: The bottom layer is with the greatest number of squares of smallest area, which represents the terrain matrix. This matrix specifies the height above the sea level of each square unit area of side t. Subsequently, the number of squares it contains is nXt = lit horizontally, and nYt = wit vertically; T (Xt, Yt). This matrix provides the knowledge of the variation of the terrain clearly, which is useful for searching for the maximum terrain height within the area of interest.

The matrix in the middle in Fig I (b) represents the traffic demand of each part of the area. Unlike the terrain, which is specified for very small areas to give more accurate measurements in a realistic environment, the demand is measured over larger square areas of side d, to obtain significant values. The number of squares in the demand matrix is nXd = lid horizontally, and nYd = wid vertically denoted as set D (Xd, Yd).

The top layer matrix in Fig I (b) represents the cell planning for the area. The area is divided into a square grid with the largest area. The side of each square is denoted by a. Thus the number of these squares is ni = l I a in the horizontal direction, and nj = 11)1 a in the vertical direction. Then the total number of squares to be considered is (ni x nj). This grid is divided into sets (or categories) of non-adjacent squares or maximal independent sets (MIS) as called in [1] (labeled "A", "B", "C", and "D" in Fig l(b), where the area is classified into four sets). The demand is added in the square elements of each set to find its total demand. From these sets, the proposed algorithm has to choose the one with the highest total demand, and then allocate the physical resources for the grid elements within the chosen set. Each base station (BS) is expected to cover the area formed by the square element, where it is located with a certain degree of overlap with the neighboring squares. This area will be denoted as a cell.

The aim of this layer classification is to provide parallel statistics of the demand and the terrain in each grid. In the proposed solution, the demand matrix D (Xd, Yd) is expanded into a new matrix Dext (Xed, Yed) with the same dimensions of the terrain matrix T (Xt, ytl to make both matrices equal in dimensions. By doing so, it is possible to know the average demand in each terrain unit area (t2). The demand matrix is considered to be expanded by a factor f = dlt in both vertical and horizontal directions. Subsequently, each element in the demand matrix is replaced by a matrix of dimensions (f x j),

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Fig. 2. (a) A top view image of the area of interest (b) The cell planning grid represents the squares (c) The user distribution over each cell after extending the demand matrix (d) final distribution of physical resources (BS's and repeaters) in a specific scenario: '*' represents a BS, and '0' represents a repeater.

Symbol I w

dmin dmax Pth PLm'in Ptmax Tih T",ax(i,j) (J"2 (J"� (i,j) t

d a

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TABLE 1 REQUIRED PARAMETERS

Description Length of the rectangular area Width of the rectangular area minimum traffic demand maximum traffic demand threshold accepted received power (sensitivity) minimum transmitted power maximum transmitted power Threshold terrain for coverage maximum height of the terrain in cell C(i,j) terrain variance variance of each cell C(i,j) side length of terrain grid element side length of demand grid element side length of Cell planning grid element number of categories in one direction cell radius number of categories

and all the elements of this matrix become equal to the value of the initial element divided by f2 (D(Xd, Yd) / f2). By this method we can distribute the average demand equally over every terrain unit area, keeping the same value of the total demand. Consequently, the demand is equally distributed over each cell (Fig 2(c)).

The main three parameters that should be considered in cell planning are coverage, capacity, and cost. As stated before, the main purpose is to minimize the planning cost. This is done using a trade-off between coverage and capacity. In the regions of high demand, capacity is the main concern. It is required to assure the capacity that can serve all the users within the cell. On the other hand, wherever the demand is low, the concern is to provide reliable coverage for users to start and maintain their calls. The signal should be strong enough to maintain handover while moving from one cell to another (soft handover in 3G

networks) and to overcome the interference from neighboring cells (mainly co-channel interference CCI in these networks).

III. PROPOSED ALGORITHM

First of all, it is required to define three matrices as stated before: terrain, demand, and cell planning matrices. Then

the demand matrix D(Xd, Yd) is averaged and expanded into Dext(Xed, Yed). The next step is to classify the grid into sets of non-adjacent square elements. The approach of choosing the category enables the network operator to locate the 8S's in the regions with the highest total demand. The number of categories is specified according to the nature of the planning area. For this reason, the algorithm finds the terrain variance for this area. As the variance increases, this means that the terrain is varying rapidly, and hence it is required to make the grid elements smaller to get more accurate values, which means more categories. These elements are distributed as squares and are repeated all over the area. Lets assume g is the number of categories in vertical as well as horizontal directions. Then the total number of categories is Tlc = g2. Hence, the distance between the centers two consecutive grid elements in the same category is g times the side a of the square element (ga). This distance should be twice the cell radius (2r). It follows that ga = 2r. Therefore, knowing the radius of the cell, the side of each square grid element can be estimated according to the chosen number of categories to be used.

The radius r of each cell can be adjusted by the transmitted power. Lets define the received power threshold Pth to be the minimum power to arrive at the receiver to obtain the area within the coverage or equal to minimum receiver sensitivity level. If the received power at the test point is less than Pth, then it is said to be out of coverage. By setting the received power to the value of the sensitivity, corresponding value of the maximum radius of the cell r can be estimated.

Another way for modifying the cell radius is by adjusting the antenna tilt angle in the vertical plane. The horizontal polarization of the antenna corresponds to the maximum coverage. As the antenna is rotated downwards, the cell radius becomes smaller. The total demand of each category is calculated by summing up the demands of all the cells within the category, and then the category with the highest total demand is selected.

After the choice is made, the algorithm searches for a suitable position in the cell with respect to the height to offer the required coverage. Assuming that any terrain at any point

361


of interest is T (xt, yd, the first location to be considered is

the center of the grid element

If the condition (T (xt, Yt) + ht) - Tmax (i,j) > Tth is

satisfied at the center, then it is the unique location of a BS

within that element Otherwise, move the BS a distance (6'r) away form the center, and in each iteration, move with an

azimuth angle (6() = 2r. / N) around the center and with a

constant distance from it, where N is the number of iterations

to be made in each circle, Repeat this test for all the values

of ()t such that (0 < ()t < 2r.) to satisfy the condition

« (T (xt,yd + hd - Tmax (i,j) > Tth), If the condition was

satisfied with unique coordinates, a base station is assigned,

However, for the case of multiple coordinates, consider the

candidate locations, For each location, measure the distances

to all the neighboring BS's and calculate their average, The

candidate location with minimum average is chosen. If the

condition is not satisfied for all the values of ()t (0 < ()t < 2r.), we move again by the same distance away from the center

and repeat the same process until the condition is satisfied

('rt = 'rt-l + 6'r). The range of the distance 'rt away from

the base station is 0 < 'rt < a/2. Then we skip to the next

grid element in the selected set and perform the same test We

repeat this process for all the elements of the set This process

determines the first set of BS's in the maximal independent

set that contains the highest demand to be deployed.

The next step is to provide the required coverage for the

blind spots and capacity for the cells with higher demand

in the selected category as well as other categories. This is

done by using two of the neighborhoods listed in [2]. The first

neighborhood is called the 'hole filler' neighborhood, which

involves searching for the regions without signal coverage

known as blind spots. These blind spots can be known from

the measured received power which is less than the receiver

sensitivity. This coverage hole also appears within the designed

coverage area due to the terrain strueture of the area of interest

Lets assume the coverage probability requirement Cth within

the area of interest is to meet the desired grade of service

(GoS). The GoS requirement is variable in rural, suburban,

and urban areas, and depends on the user distribution, traffic

demand of user group, and growth rate. The proposed algo

rithm is also required to find the terrain variance for each cell.

The values of the variance give an indication of the variation

of the terrain in the geographical area of the cell. Based on

these values, the algorithm can predict the existence of blind

spots. Then it has to decide the amount of physical resource

to be considered at each blind spot according to its demand.

If the traffic demand of a certain blind spot region is greater

than a given minimum dmin , then a base station should be

deployed at that point to cover the blind spot Otherwise,

a repeater is to be considered. The second neighborhood is

called 'cell splitting' or 'sectorization' of the cells, where the

traffic capacity is violated. The omnidirectional antenna of

the cell is to be replaced by three directional antennas, each

covering an angle of 120° azimuth. This neighborhood is to

offer a higher traffic for the cell with the same transmitted power.

The sequential steps of the proposed algorithm are shown

below:

Step 1: Initialize

Terrain T = [tl,l tl,2 ... tl,n; ... ; tm,l tm,2 ... tm,nj ,

Demand D = [dl,1 dl,2 ... dl,q; ... ; dp,l dp,2 ... dp,q], Cell planning C = [CI,1 CI,2",CI,nj; ... ;Cni,l Cni,2 ... Cni,nj], bSk = [x y]T, BS = [bsl bS2 ... bsk], 'r = ag/2, nc = g2,

Step 2: Extend the demand matrix .f = d/t ; Dext (Xed,Yed) +-- D (Xd,Yd)/P, Dext(Xed, Yed) +-- copy value .f times vertically,

and .f times horizontally,

Step 3: classify cells into categories

Assign category to each cell, given g,

Cat = [CCI CC2 ... ceq CCI CC2 ... CCg ... ; CCg+I CCg+2 ... CC2g CCg+I CCg+I ... CC2g ... ; CCg2_g+1 CCg2_g+2'" CCg2; • • • 1,

Step 4: Choose category with maximum total demand

Repeat: for all categories cc

initialize i and j w.r.t category i.e. Cat ,

Dca t(cc) = 87J,m (Dext (i,j)), end

s = max(Dcat) Step 5: Locate the BSs in the selected category

initialize i and j w. r. t selected category i.e. s,

Repeat: for i and j BS +-- center of (C (i,j)), n +-- n + 1,

end k = 1, Repeat for k

Test

end

if BS + ht - Tmax (i,j) < Tth Repeat for 'rt: step size: tJ..'rt

Repeat for (): step size: tJ..() BS (k) = BS (k) + ['rtcos() 'rtsin()]T DS ('rt, ()) = AV'rg (abs (BS - BS (k)))

end

end

BS (k) +-- min (DS),

Step 6: Search for uncovered points, provide suitable

coverage

search C (i,j) : a� (i,j) > a;h' search for uncovered points: PT' < Pu"

if D (Xd, Yd) > dmin BS +-- select Dc

else

Repeater +-- select Dc, end

Step 7: Sectorize cells with high demand

Repeat: for i = 1, ... , ni; j = 1, ... nj if D (Xij, Yij) > dmax

Place three sector antennas (120°), end

362


This algorithm provides the preliminary design for the

network. However, radio planners are still supposed to go

and take necessary practical measurements before they end

up with the final design, which usually takes some time. In

fact, the process of radio network planning and selecting the

proper sites for the base station locations is considered costly

and time consuming. Therefore, computational time of the

algorithm is not an issue as long as it gives the required

coverage at the minimum cost. Moreover, the algorithm gives

a prior idea about the area according to terrain variation and

user distribution. This leads to a significant reduction in the

time and cost spent in taking the necessary measurements in

the site survey.

IV. SIMULATI ON RESULTS

In order to test the performance of the proposed algorithm,

measurements are taken in four different cases. The results

of these cases are shown in Table II. To compare the results

of the proposed algorithm with previous works, the first

case was taken from 1161, which has been developed for a

regional area of Singapore. The detailed specifications for

this problem are given in [161. The total area considered

consists of 625km2, and is divided in a grid of 100 elements

each of dimension 2.5 x 2.5krn. The problem is solved using

Simulated Annealing algorithm. 57 BS's were required to

cover this area. The same problem is solved again in [lJ using the Branch and Bound Algorithm, and the number of

BS's was reduced to 11. To compare the proposed algorithm

with these two algorithms, the same case has been considered

with the same number and dimensions of the grid elements

and with the same specifications including Hatas propagation

model [17] which has been used in both of them. This model

was also used in the other cases but with adding the correction

factor corresponding to the terrain characteristics of each

case. Since none of the algorithms has considered terrain in

solving the problem, the proposed algorithm is applied with

setting the value of the terrain variance to zero, which refers

to flat terrain. The approach of placing repeaters in the blind

spots with low demand leads to a significant rcduction in thc

total cost. The cost of placing a BS is assumed to be (lu), and that of placing a repeater will be (O.lu). Accordingly,

the problem is solved at the cost of 5711 in [16] and 1111 m [1]. On the other hand, the algorithm proposed in this

TABLE II COMPUTATIONAL RESULTS OBTAINED FOR THE SET 01' NETWORK

DESIGN PROBLEMS SOLVED USING THE PROPOSED ALGORITHM FOR

DIFFERENT CASES

Terrain No of No of No of No of Total Case Variance grid categories BS's repeaters cost

(0-2) elements (nd (lu) (O.Iu)

I 0 100 25 9 7 9.7u

2 0.3 36 4 12 6 12.6u

3 0.5 81 9 14 6 15.6u 4 0.85 144 16 18 7 18.7u

TABLE III COMPARATIVE RESULTS OF THE PROPOSED ALGORITHM WITH

PREVIOUS ALGORITHMS

Terrain No of No of No No

Algorithm Variance grid el-

cate- of of re- Total used (0-2) ements

gories BS's peaters cost (nol (lu) (O.lu)

Simulated Annealing 0 100 - 57 0 57u (SA) [161

Branch and Bound 0 100 25 II 0 llu (B&B) [II

Proposed Algorithm 0 100 25 9 7 9.7u

paper solved the problem with nine base station and seven

repeaters, and so with a cost of 9.7u. This means that the

cost has been reduced by 11.8% of the improved solution in

[I]. The results of the comparison are shown in Table III.

The other three cases were taken for different areas with

increasing values of terrain variance (note that the variance

is normalized and ranging between zero and one). For case

2, the planning process is shown in Fig 2(a-d), including

all the design steps until reaching the final design of the

network, is shown in Fig 2(d). A gray scale image is taken

for the area to represent its terrain variation; the darker parts

of the image represent higher positions. As stated before, the

area with higher variance is divided into a greater number of

grid elements of smaller area to obtain more accurate values,

and thus more categories. It is observed from Table II that

the increase of the terrain variance leads to the increase in

the physical resources. As the variance increases, the area is

said to be changing more and more rapidly, and it can be

expected that it contains more blind spots. For each blind spot,

a decision should be made whether to place a BS or a repeater

according to the demand within the region.

V. CONCLUSION

The algorithm proposed in this paper has succeeded in

specifying the locations and required number of BS's ac

cording to the nature of the terrain within the area. The

approach has considered repeaters in the areas containing

low traffic demand. This leads towards minimizing the design

cost, which is our main objective of the work. The algorithm

gave an improvement for previous algorithms by reducing the

cost of physical resources by 11.8%. Moreover, the proposed

algorithm provide more realistic solutions to CLDP as it considered terrain variation, user distribution, and user demand criteria in optimal nature.

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