3900-11116-1-pb.pdf

Intelligent GSM Cell Coverage Analysis System

Based on GIS

Lina Lan Beijing University of Posts and Telecommunications/School of Network Education, Beijing, China

Email: [email protected]

Xuerong Gou, Yunhan Xie and Meng Wu

Beijing University of Posts and Telecommunications/School of Network Education, Beijing, China

Email: [email protected]

Abstract—In mobile network, a deviation of cell coverage area influences many network performance indexes. Cell

coverage analyses are vital to network optimization. The

traditional check method is DT (Drive Test) or FSP (Field

Strength Prediction) by manpower which costs much time

and resources. This paper presents an intelligent multiple factors analysis method on cell coverage, and designs the

relevant software system based on GIS platform. This

system derives a cell coverage analysis chart and identifies

the cells with cross-boundary coverage or poor coverage problem by collecting a huge number of mobile phone

measure data in OMC and analyzing multiple factors based

on the measure data and the basic data of cells. The measure

data analysis aims to compute signal level distribution,

sample point distribution, category of interferences. The basic data of cells includes neighborhood relationship,

azimuth ward, location and distance between two cells. The

base station site level can be computed from the basic data

of cells by the triangulation method. The calculation and

analysis results are presented in the map based on GIS platform to improve visualization. This method and system

are validated by a large number of actual datasets from an

in-service GSM network. Contrast with the traditional cell

analysis method, this method and system demonstrate advantages in intelligence, accuracy, timeliness, and

visualization.

Index Terms—mobile network optimization, cell coverage

analysis, mobile measure data, signal level distribution, sample point distribution, cell coverage metrics, base station

level, OMC (Operation & Maintenance Center), GIS

(Geographic Information System)

I. INTRODUCTION

Mobile network is a dynamic network. The contents of

mobile network optimization are optimizing the

allocation of the resources, adjusting the network‟s

parameters reasonably, and making the network run in the

best state [1]. Every cell has its own coverage area. One

of the important factors which in fluence the quality of the

mobile communication is cell coverage deviation such as

cross-boundary coverage, critical coverage, poor

coverage etc. Therefore, how to determine the cell

coverage area in the mobile network is v ital to mobile

network optimization [2].

The mobile communicat ion coverage area commonly

refers to the region: call quality is assured in 90% scope

of the reg ion. The field boundary is the mobile station

receiving the min imum signal level. Among the existing

technologies, there are mainly two ways to determine the

cell coverage area.

1. Drive Test [3]. That is the survey crew go along a

certain route in the serving cell and the target cells by car

or on foot, to measure the field intensity point by point by

a coverage test machine when they march forward. In this

way, they acquire the field strength distribution of the

serving cell. Constrained by factors like ground,

topography and cost, it‟s difficult to get the field intensity

at every point in the measurement area. Only some

discrete values of the field strength distribution are

retrieved. Besides, drive test consumes large amount of

manpower and material resources and is not able to

provide real t ime data.

2. Field Strength Prediction [4]. That is using the field

strength prediction algorithm and the known field

strength distribution to predict the field strength

distribution of the area where it is hard to get the field

strength. There are some classical algorithms like

Okumura Model. In these algorithms, not only the

propagation model must conform to the actual situation

of the current network, but during the prediction process,

a large amount of measured data are also needed to be

analyzed, fitted, counted. So large amounts of data,

matching geography-data are needed if using this method,

let alone that the computational complexity is high but

the effective time is short.

Both the two methods are deficiencies, the former can

not provide a comprehensive real-time analysis, and

spend a lot of human resources and material resources;

the latter model requires high precision, and can not be

well adapted to frequent changes in the cells and various

land types.

The voice quality in dig ital mobile communication

system is not only determined by signal strength, but also

by the same frequency interference and mult i-path effects.

Somet imes, even if the signal strength is high, the voice

quality is still poor [5, 6]. Therefore, the cell coverage

analysis not only need to analyze the level d is tribution,

JOURNAL OF COMPUTERS, VOL. 6, NO. 5, MAY 2011 897

© 2011 ACADEMY PUBLISHERdoi:10.4304/jcp.6.5.897-904

but also to take into account inter-cell interference,

sample point d istribution to discover various cell

coverage issues, such as cross-boundary coverage, poor

coverage and so on.

The cross-boundary coverage is prone to cause island

effect, switch failure, d ropped calls and other serious

impacts on communication quality. Therefore, the

identification of the cross-boundary coverage is the focus

of coverage problems. The trad itional method requires

much human effo rts on data check and choosing which

depends on the experiences of the network optimizat ion

engineer to confirm the problem cell. The approach has

low efficiency, consuming a lot of human resources.

This paper presents a method to analyze mult iple

factors to verify the cell coverage area and problem based

on the measure data in OMC system and basic

informat ion of cells. The measure data includes signal

level distribution, sample point distribution, and category

of interferences. The basic cell informat ion includes

neighborhood relationship, antenna azimuth and distance

between two cells etc. The method is implemented in a

cell coverage analysis system based on GIS platfo rm.

GIS p latform is used to develop the applications which

are associated with geographic informat ion. The coverage

analysis system based on GIS can improve the visibility

of coverage area [7]. Network optimization engineer can

analyze the cell coverage conveniently and precisely.

This paper describes the system design and validation.

The system is tested by real data of an in-service GSM

network. The analysis result is compared with the

traditional approach. The advantages of new method are

summarized based on validation and comparison result.

II. BASIC PRINCIPLE

In mobile network, MS ( Mobile Station ) upload some

measure data ( For example, the identification of the

serving cell and the interference cells, the downlink

signal level of the serving cell and the interference cells

etc.) to the network-side of the mobile network, and the

data are stored in OMC system. One record uploaded by

MS is called a sample point. The acquired field strength

distribution based on these information statistics is

complete and more accurate than that is acquired through

DT (Drive Test). Fig. 1 is the schematic diagram of the

field strength distribution of the serving cell.

Theoretically, the level value of the serving cell will

decrease when the distance to the serving cell increase [8].

For example, in Fig. 1, the level value measured in Cell A

should smaller than that measured in Cell B. The level

values in all the cells are collected through cell phones. If

the level value in Cell A is bigger than or equal to that in

Cell B, then there must be some problem in cell coverage.

.

Figure 1. The schematic diagram of the field strength distribution

However, the field strength distribution can not reflect

the cell coverage situation thoroughly. The information of

the sample points reflects the contact frequency and the

disturbance intensity between the serving cell and the

interference cell. The more the sample points, the closer

the relationship between the two cells, which means the

contact frequency is higher. The disturbance intensity is

presented through CIR (Carry Interference Rate). On the

network-side, for example, BSC (Base Station Control)

can get all the CIR between the serving cell and the

interference cells based on the measured data of all the

sample points. The formula of the CIR is:

C/I (dB) = downlink level serving cell (dBm) -

downlink level interference cell (dBm)

In theory, the farther away from the serving cell, the

remoter the relationship between the two cells, the sample

points should be less and the disturbance intensity should

be weaker. In this case, in Fig. 1, even though the level

value of the serving cell measured in Cell A isn‟t unusual,

but there are lots of sample points, which means the

contact frequency between this cell and the serving cell or

the disturbance intensity is high, this phenomenon is also

show that there is cell coverage problem. Similarly, if the

sample points measured in Cell B are few or the

relationship with the serving cell is remote also tells the

same thing.

III. ANALYSIS METHOD OF CELL COVERAGE

A. Method flowchart

This article presents an analysis method of cell

coverage based on the principle mentioned above. The

method flowchart is shown as Fig. 2.

This method consists of several parts: data collection,

data analysis and getting the analysis result. At first,

collect the raw data from OMC system. Secondly,

analyze the measure data. Measure data analysis includes

two parts: the field strength distribution analysis and

sample point number analysis . Thirdly, analyze the cell

basic data including base station level, antenna azimuth

and neighborhood relationship. At the end, draw the cell

coverage chart and identify the problem cells.

898 JOURNAL OF COMPUTERS, VOL. 6, NO. 5, MAY 2011

© 2011 ACADEMY PUBLISHER

Data collection from OMC

Drawing the cell coverage chart and

determining the problem cell

Signal level

distribution

Sample points

distribution

Measure data analysis

Base station

level

Antenna

azimuth

Cell basic data analysis

Neighborhood

relationship

Figure 2. Flow of the analysis method of cell coverage

The measure data analysis is the most important part in

the method. The analysis of signal level distribution and

sample point d istribution is complex. The following Fig.

3 and Fig. 4 show the flows of field strength analysis and

sample point analysis.

Calculate the probability of all the

level value

Draw the level probability plot

Get the sub-point of the level interval

Classify all the cells according to the

level intervals

Figure 3. Flow of the signal level distribution analysis

Field strength distribution analysis include four steps:

calculate the probability of all the level value, draw the

level probability plot, get the sub-point of the level

interval and classify all the cells accord ing to the level

intervals.

The sample points number analysis flow is shown as

Fig. 4.

Calculate the average value of Class1,

Class2, Class3 of all the cell pairs

Compare the sample points number of

each cell pair with the average value,

get the sample points information

Look up the sample points

classification table, get the sample

classification code

Figure 4. Flow of the sample points distribution analysis

Sample points number analysis include three steps:

calculate the average value of 3 classes of all the cell

pairs, compare the sample po ints number of each cell pair

with the average value then get the sample points

informat ion and look up the sample points classification

table then get the sample classification code.

Based on the analysis of the two parts of the measure

data, the cell coverage chart can be shown on GIS map.

The planning coverage can be analyzed through cell basic

informat ion such as site level, azimuth and relationship.

Finally comparing with the planning coverage area, the

problem of cell coverage can be identified.

B. Measure data analysis

1) Signal level distribution analysis

In GSM network, the range of the average downlink

level is -47dBm～-110dBm. There are many ways to

classify the average downlink level o f the source cell

(serving cell) which is included in the collected raw data.

The average downlink level of the source cell can be

divided into several ranges. In this method, the levels are

divided into ranges based on level value statistical

probability.

The probability of every level value is calculated by

examining the number of t imes every source cell

downlink level value occurrence. With the average

downlink level of the interference cell as the horizontal

axis, the probability as the vertical axis, connect all the

points into a line, and then use the moving average

algorithm to make the line into a smooth curve. Draw the

level probability p lot as Fig. 5.

Figure 5. Level probability plots

The level ranges (A, B, C, D….) are defined by taking

the wave troughs of the probability plot as the sub-points

of the source cell average downlink level. The probability

plot in each range approximates a normal distribution

with this partit ion. That is to say, in each source cell

average downlink level range, the probability of every

source cell downlink level value occurrence approximates

a normal distribution. This conforms to the ideal

distribution of the received downlink level o f a cell in the

whole coverage area.

Then, all the interference cells are classified into level

A,B,C,D…. according to the average downlink level of

the source cell received in the interference cells acquired

from the OMC. For example, in a record where the

serving cell ID is S and the interference cell ID is T, if the



average downlink level of the cell S is level A where

received in cell T, cell T belongs to level A. Like this, all

the interference cells (the analyzing cells) can be

classified into level A, B, C, and D…around cell S. In

other words, the level strength distribution of cell S is

presented.

2) Sample point distribution analysis

I/C which is the reciprocal of C/I, expresses the

disturbance intensity. In engineering, -12dB is used as the

same frequency interference protection ratio. If I/C is

smaller than -12dB, then the interference is weak

interference. If I/C is bigger than 0dB, then the

interference level is higher than the signal level and the

interference is strong interference. Therefore, (-∞,-12dB],

(-12dB, 0dB) and [0dB, ∞) represent weak interference,

critical interference and strong interference respectively.

The number of sampling points of the three ranges in a

certain time from the OMC data is represented as Class1,

Class2, and Class3 respectively. As Class1, Class2,

Class3 represent three levels of interference, each cell is

divided into d ifferent interference intensity by the

statistical analysis of the sampling points of the three

ranges.

First of all, work out the average number of all the

recording numbers of the sampling points. Avg1

represents the number of sampling number of Class1,

Avg2 of Class2, and Avg3 of Class3. That is:

Avg1 = sum (the number of sampling points of

Class1)/total number of points

In the formula above, sum (the number of sampling

points of Class1) represents all the recorded numbers of

sampling points of Class1. Avg2 and Avg3 are calculated

by the same function.

Then comparing the sample points number of Class1,

Class2, Class3 of each record with Avg1, Avg2, Avg3, if

the number is b igger than mean value, mark it as Big. On

the contrary situation, mark it as Small. In this way get

the classificat ion combination, and then look up the

TABLE I, get the interference intensity and the sample

point‟s classification code. For example, after classify the

sample points number of Class1, Class2, Class3 of a

record (the serving cell ID is S, the interference cell ID is

T), the classification combination is (Big, Big, Big).

According to TABLE I, the classification code from cell

T to cell S is 1 and the interference intensity is strong

interference.

TABLE I. SAMPLE POINT CLASSIFICATION TABLE

Sample point

Classification code

Class1 (weak interference)

Class2 (critical interference)

Class3 (strong interference)

Interference intensity

1 Big Big Big Strong interference **

2 Big Big Small Moderate interference *

3 Big Small Big Strong interference **

4 Big Small Small Weak interference

5 Small Big Big Strong interference **

6 Small Big Small Moderate interference *

7 Small Small Big Strong interference **

8 Small Small Small Weak interference

TABLE I shows the sample point classification. In

actual network, because the situation when the

interference intensity is „strong interference‟ is rare, the

sample points number where the interference intensity is

„weak interference‟ and „moderate interference‟ is much

bigger than that where the interference intensity is „strong

interference‟, in the table, „Big‟ means the number is

bigger than mean value, „Small‟ means the number is

smaller than mean value. As a result, in the table the

priority is „weak interference‟, „moderate interference‟,

„strong interference‟, „Big‟ is in front of „Small‟, this

reflect the sample points number decrease from class 1 to

8.

The more the sample points number, it means the

relationship between the serving cell and the interference

cell is closer. So in the table, when the sample points of

„strong interference‟ is „Big‟, the interference intensity is

defined as „strong interference‟, among the rest, if the

sample points of „critical interference‟ is „Big‟, the

interference intensity is defined as „moderate

interference‟, the rest is defined as „weak interference‟, so

in the table, the interference intensity of class 1,3,5,7 is

„strong interference‟, class 2,6 is „moderate interference‟,

class 4,8 is „weak interference‟.

In this method, the number classificat ion (Class1 to

Class 8) of the sampling points of the interference cells

and the interference level („strong interference‟ „moderate

interference‟ „weak interference‟) are deduced according

to the statistical analysis of the number of the sampling

points in the OMC data and TABLE I .

C. Cell basic information analysis

Cell basic data includes base station locate level,

antenna azimuth and neighborhood relat ionship. These

data are valuable for cell coverage analysis.

1) Base station level stratification

There are large amounts of base stations in the large

mobile network. The base station intensity varies much in

different regions. In countryside, the distances among

base stations are much farer than in city. The cell

coverage radius in countryside is much bigger than in city.

The coverage radius can‟t be defined as a constant value.

Thus, the cell coverage is usually indicated by base

station level and azimuth side.

The forward o f the azimuth is the area of 120°around

the center line of the azimuth, and the other side means

backward. In general, the coverage area of a cell should

be in the forward site level 3 and the backward site level



1. The area of inside of forward level 1 to 3 and backward

level 1 is the right coverage region. The area of outside of

forward level 3 and outside of backward level 1 should

not be covered.

To divide the site level of the base station is the key

problem to confirm the cell coverage area. Commonly the

famous Delaunay triangulation principle is used to

partition the base station nodes intelligently. Delaunnay

triangulation is special. It requires that every circumcircle

of each triangle does not contain any other point in the

triangle network, this ensures a triangle is formed by the

most three adjacent nodes [9-11]. Fig. 6 is the chat of cell

stratification by the triangulation method.

Figure 6. Cells stratification by triangulation method

In Fig. 6, the base stations in mobile network are

partitioned into triangle network with the triangulation

method. In the triangle network, the site level can be

defined as the length of the shortest road between two

cells [12]. For example, cell A is the serving cell. The

shortest road from cell B to cell A is 1, so cell B is site

level 1. The shortest road from cell C to cell A is 2, so

cell C is site level 2, and the cell D is site level 2 too. The

partition result is satisfied with the real engineering

experience.

D. Result showing on GIS

Based on the result of cell coverage analysis, combine

the longitude and latitude information of the serving cell

and interference cells in the OMC data, draw the cell

coverage chart on GIS, and then find the cells which have

problems intuitively, as shown in Fig. 7.

S

A2*

A2*A1

**

D6*

E4

E6

*

B3

**C4

C6* D3

**

E4

E6*

D3

**

E4

A2

*

C1

**

BS1 BS2

BS3

BS6

E4

BS4

BS5 BS8

BS7

Figure 7. Coverage analysis chart

Fig. 7 shows the coverage analysis of serving cell S. A,

B, C, D, E represents the signal level rank, the level

decrease in alphabetical order. Number 1, 2 to 8 represent

the sample classificat ion code, the interference level of

the cells marked by „**‟ is „strong interference‟, the

interference level of the cells marked by „*‟ is „moderate

interference‟, the interference level of the cells without

mark is „weak interference‟.

In Fig. 7, in BS7 and BS 8 the received level strength

is high (level C, A), the sample classificat ion code is

small (class 1, 2), that means their contact frequency with

the serving cell is high, the interference intensity to the

two cells are strong interference (**) and moderate

interference (*). But the distance between the two cells

and the serving cell S are far, that means possibly that the

configuration of the two cells has some problems. Cell S

maybe have the cross-boundary coverage problem to BS7

and BS8. The reasons which can cause the problems are

the configuration parameters of the related BS, for

example, there is a deviation of antenna azimuth,

longitude or latitude, or maybe the actual environmental

factors like the weather, for example, a strong wind can

make the antenna height, azimuth and the pitch angle

change.

E. Identification of the problem cells

Considering of the above factors, the results of cell

coverage analysis are shown as following TABLE II.

The coverage analysis result is conducted according to

multip le factors such as the sample point class, the signal

level, the site level, the azimuth, and the relat ionship. In

the table, the deviation coverage problems are listed only,

and the right coverage is not listed because of

unnecessary. The cell site which is cross-boundary

coverage or poor coverage is clearly shown in the table.

For example, No 1 in the table means that if the sample

point class of interference cell T is 1 or 3(it means Strong

inference with much sample points), the site level of cell

T is forward outside level 3(it means the distance is far

away from the coverage) from the serving cell S, and cell

T is not the neighborhood cell of cell S, so cell S might

has the cross-boundary coverage problem in cell T.

No 5 in the table means that if the inference cell T is 8

(it means little sample points), the signal level is E (it

means lowest signal level), and site level is forward

inside level 3(it should be in the coverage area), but

relationship is neighborhood; therefore, cell S should has

poor coverage in cell T.

No 7 in the table means that if the inference cell T is

the neighborhood cell of the serving cell S, but there is no

sample point in cell T, so cell S should has the poor

coverage problem in cell T.



TABLE II. RESULTS OF MULTI-FACTORS ANALYSIS OF CELL COVERAGE

No Sample point class Signal level Site level Ward of azimuth Relationship Result

1 1,3 Outside level 3 forward No cross-boundary coverage

2 1,3 Outside level 1 backward No cross-boundary coverage

3 2,4,5,6,7 A,B Outside level 3 forward No cross-boundary coverage

4 2,4,5,6,7 A,B Outside level 1 backward No cross-boundary coverage

5 8 E Inside level 3 forward Yes poor coverage

6 8 E Inside level 1 backward Yes poor coverage

7 No sample point Yes poor coverage

IV. CELL COVERAGE SYSTEM DESIGN

A. Function module structure

The relevant system to implement the above multiple

factors analysis of cell coverage method is designed as

three layers structure following as Fig. 8. User Interfaces Layer

GIS GUI Coverage analysis GUI

Result dataData importMap basic functions Cell coverage map

Data Processing Layer

Data Import

Data Analysis

Antenna

azimuth

analysis

Base station

level analysis

Sample points

number analysis

Signal level

distribution

analysis

Neighborhood

relation

analysis

Raw Data Layer

Map File

OMC data

Neighborhood.txtRxLevels.txtDac.txt Cf.txt Sectors.txt

Figure 8. System structure of multi-factors analysis of cell coverage

Raw data collection layer and data processing layer are

designed as backend services. Raw data collection layer

is responsible for collection of map data and OMC data.

OMC data can be categorized per file format as shown in

Fig. 8.

Data processing layer can import raw data as

intermediate data objects for application consume. Data

processing modules implement various analysis functions:

sample points number analysis , signal level distribution

analysis, neighborhood relation analysis, Base station

level analysis, Antenna azimuth analysis .

User interfaces layer provides 2 kinds of data

presentation method: coverage display based on GIS and

coverage analysis display by data grid.

B. System development and deployment environment

The development platfo rm is Visual Studio 2005 C#

on Windows XP system.

The implementation of GIS features (e.g. the base

station‟s location and color) employs MapInfo

MapXtreme components.

The system is deployed on Windows XP system.

V. DISCUSSION OF COVERAGE ANALYSIS RESULT

A. Validation with real datasets from in-service network

This method and system has been tested using the real

data from the GSM network in a Chinese province. The

testing data is the mobile phone measure data collected

from the OMC system in Apr, 2009.

For example, the cell RENMINRD1 is analyzed

overall. The coverage chart is shown as following Fig. 9.

Figure 9. Multi-factors analysis of cell coverage graph

In Fig. 9, the red cell RENMINRD1 is serving cell, and

the other cells are interfere cells. The different colors

mean the interfere class 1 to 8 of sample points.

ABCEDE indicate the signal level range. A :[-47，-54], B:

(-54，-70], C: (-70，-82], D: (-82，-98], E: (-98，-110]

dbm。

From the Fig. 9, the cell coverage status is good. In the

nearer cells with s maller station site level to the serving

cell, the interference intensity is stronger and the signal

level is higher. In the farer cells with bigger station site

level to the serving cell, the interference intensity is

weaker and the signal level is lower.

In Fig. 9, the upper left interference cell TUDIJU2 is

far from the serving cell RENMINRD1 and the site level

is outside the forward site level 3, so the TUDIJU2 cell

should be out of the coverage boundary of the

RENMINRD1 cell. But the analysis data show that the

sample point number is high (class 1) and signal level is

high (level C). The TUDIJU2 cell is not the

neighborhood cell of the RENMINRD1 cell. It means that



the RENMINRD1 cell probably has cross-boundary

coverage in the TUDIJU2 cell.

The left interference cell WANGBAOSHOUJI is near

to the serving cell RENMINRD1 and the site level is in

forward site level 1. The WANGBAOSHOUJI cell shall

be in the coverage of the RENMINRD1 cell. But the

analysis data show that there are no sample points in

WANGBAOSHOUJI cell. The WANGBAOSHOUJI cell

is the neighborhood cell of the RENMINRD1 cell. It

means that the RENMINRD1 cell probably has poor

coverage in the WANGBAOSHOUJI cell.

B. Comparison with traditional analysis approach

In engineering pract ices, there is a tradit ional analysis

approach to analyze the cross-boundary coverage

problems. The engineering practices prove that this

traditional analysis approach is reliab le. Therefore, the

comparison with the traditional approach can validate the

accurateness of multip le factors analysis method of cell

coverage.

The analysis result by mult iple factors analysis method

of cell coverage is shown in TABLE III.

The interference level is the list of sample points class

and signal level. From TABLE III, the serving cell

RENMINRD1 has cross-boundary coverage problem in

the cell TUDIJU2 and YINDU2. The serving cell

RENMINRD1 has poor coverage problem in the cell

TAISHANZL2 and WANBAOSHOUJI.

TABLE III. RESULTS OF MULTI-FACTORS ANALYSIS OF CELL COVERAGE

Serving cell Interference cell Interference level Site level Ward of

azimuth

Relationship Result

RENMINRD1 TUDIJU2 1C Outside level 3 forward No cross-boundary coverage

RENMINRD1 YINDU2 7B Outside level 3 forward No cross-boundary coverage

RENMINRD1 HONGRIXIQU1 1C Inside level 1 backward Yes RENMINRD1 LHDANNISI1 8C Inside level 1 forward Yes

RENMINRD1 LHDANNISI2 8C Inside level 1 forward Yes RENMINRD1 TAISHANLU2 1C inside level 2 forward Yes RENMINRD1 TAISHANZL2 No sample point inside level 2 forward Yes Poor coverage RENMINRD1 WANBAOSHOUJI No sample point Inside level 1 forward Yes Poor coverage

RENMINRD1 LHWENHUAGONG2 2C Inside level 3 forward Yes ……

TABLE IV. RESULTS OF TRADITIONAL METHOD OF CELL CROSS-BOUNDARY COVERAGE ANALYSIS

Serving cell Interference cell Interference factor Distance Is cross-boundary coverage

RENMINRD1 HONGRIXIQU1 19.95 10.09 No

RENMINRD1 LHDANNISI1 44.84 15.68 No

RENMINRD1 LHDANNISI2 48.89 17.1 No

RENMINRD1 LHWENHUAGONG2 41.89 13.59 No

RENMINRD1 TAISHANLU2 35.49 14.03 No

RENMINRD1 TAISHANZL2 27.88 11.07 No

RENMINRD1 TUDIJU2 25.57 20 Yes

RENMINRD1 WANBAOSHOUJI 73.17 20 No

RENMINRD1 YINDU2 30.14 20 Yes

RENMINRD1 XINDAXIN3 28.47 11.32 Yes

……

In traditional approach, the distance between cells and

neighborhood relation and C/I are considered and the

analysis result is shown in TABLE IV. The interference

factor item value varies inversely with C/I value. The

small interference factor means that the interference from

serving cell is strong to interference cell.

When the cross-boundary coverage occurrence in non-

neighborhood interference cells exceeds the threshold, the

serving cell is deemed as cross-boundary coverage cell.

The threshold is 3 in engineering practices. From TABLE

IV, the serving cell RENMINRD1 has cross-boundary

coverage in the cell TUDIJU2 and YINDU2.

From the comparison of TABLE III and TABLE IV,

the serving cell RENMINRD1 has cross-boundary

coverage in the cell TUDIJU2 and YINDU2. The real on-

site testing result proved this analysis conclusion.

TABLE III identified the poor coverage problems in the

cell TAISHANZL2 and WANBAOSHOUJI.

Validation on large amount of real data shows that the

accurateness of these 2 approaches exceeds 80%. The

traditional approach requires much analysis on redundant

informat ion and manually verification efforts is

significant. The new approach in this paper can save

much efforts on manually verification and has the ability

to identify the poor coverage problems.



VI. CONCLUSION

This paper presented a novel multip le factors analysis

method for analyzing the cell coverage in GSM network.

The approach consists of 1) collecting mobile measure

data from OMC system, 2) analyzing the measure data

and the basic data of cells, and 3) drawing the cell

coverage chart on GIS and identify ing the problem cells

which have deviat ion coverage. Unlike the prev ious

approaches based on DT or pred iction, this new approach

is based on the real mobile phone measure data collected

from OMC, thus the raw data are comprehensive, real-

time and costless. The analysis considers mult iple factors

including the signal level d istribution and sample point

distribution etc. Th is paper first proposed sample point

distribution analysis to describe the contact tightness of

cells in cell coverage identificat ion. So the approach is

more comprehensive. The approach also provides the

visualizat ion chart of cell coverage analysis based GIS

which has practical advantages over the many existing

techniques whose UI only display results as data table. It

improves the availability and efficiency of the analysis

tool for optimization engineers.

The approach is evaluated by large amounts of

measure data from a real GSM network. Over 80%

average problems can be discovered and the accurate is

satisfied.

There are two immediate goals for future work. First,

the accuracy should be increased higher by improving the

analysis algorithm. Second, what reason caused the

deviation occurred and how to resolve the deviat ion can

not be pointed out in the method. These are the direct ions

for further research.

ACKNOWLEDGMENT

School of Network Education, Beijing University of

Posts and Telecommunications (BUPT) support this work

in funding and research environment. Any opinions

expressed in this paper are those of the author and do not

necessarily reflect the views of School of Network

Education, BUPT. The anonymous reviewers provided

useful feedback that helped improve the quality of this

paper.

REFERENCES

[1] Deng Yuren, “Wireless Cover and Network Optimization”,

Shanxi Electronics Technology, 2005(03), pp.45-46.

[2] Jin Xiao-jia, Pan Yang-fa, and Song Jun-de. “The status and development trend of the mobile communication network optimization technology”, Telecom Technology, 2003(12), pp.1-3.

[3] Ma Ming-ming, Xu Xi, and Wang An-yu, “A Analysis

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Shengbing, “The Research for Field Intensity Prediction

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[5] Zhao Ting-bing, and Liu Shang-hong. “GSM wireless

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Lina Lan received the BS degree in computer communication

from BUPT (Beijing University of Posts and Telecommunications) in 1994, and the MS degree in Computer

application from BUPT in 1997. She is currently working as a

lecturer in BUPT. Her main research interest is software

architecture, network service, and mobile network optimization.

Xuerong Gou received the BS degree in communication

engineering from BUPT in 1976, and the MS degree in

Management Science from Norwegian University in 2001. She

was a visiting researcher in SFU (Simon Fraser University) in Canada in 2005. She is currently working as a professor in

BUPT. Her main research interest is NGN, network education,

education technology, and mobile network optimization.

Yunhan Xie received the MS degree in communication and information system from BUPT in 2009. Her research work is

mobile network optimization and GIS application development.

Meng Wu is currently working toward the MS degree in

communication and information system in BUPT. His research work is mobile network optimization and GIS application

development.



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