International Journal of Advanced Computer Science, Vol. 1, No. 6, Pp. 220-223, Dec. 2011.

Manuscript Received:

1, Oct, 2011

Revised: 6, Nov., 2011

Accepted:

15, Dec., 2011

Published: 15, Jan., 2012

Keywords Call

Quality

Measurement,

Signal

Strength,Call

drop,Landmarks,

LAC (Location

Area Code),

ME(Mobile

Equipment)

Abstract Call Quality parameters

extraction from end-users perspective

and analyzing at the operator end is

new area of research. The parameters

that are considered in this research

are intuitive and are very much

helpful for the operator in deciding

the quality as perceived by the end

user. The parameters are average

signal strength during the active call

and call drop information. A model to

address the call quality escalation and

locating them on the map enabling the

user-groups and operators to

benchmark the network has been

proposed and implemented. A novel

model to collect the call quality

information for further utilization by

the operator is proposed.

1. Introduction

Traditional speech quality measurement techniques

use the subjective listening tests called Mean Opinion

Score (MOS). It’s based on the human perceived speech

quality based on the scale of 1 to 5, where 1 is the

lowest perceived quality and 5 is the highest perceived

quality. Subjective listening tests are expensive, time

consuming and tedious.

So, currently most of the systems use objective

evaluation of speech quality using some mobile

computing techniques. Objective testing systems are use

automated speech quality measurement techniques. The

three well known objective measurement techniques are

Perceptual Speech Quality Measure (PSQM), Perceptual

Analysis Measurement System (PAMS) and Perceptual

Evaluation of Speech Quality (PESQ).

Objective speech quality measurement techniques

mostly are based on input-output approach [1]. In input-

output objective measurement techniques basically

works by measuring the distortion between the input and

the output signal. The input signal would be a reference

signal and output signal would be a received signal.

Input-output based speech quality assessment in

objective speech quality measurement gave good

correlations with reaches up to 99% in some cases [2].

Estimating the speech quality without the presence of

Akram Aburas (email:akram.@aces-co.com) and Prof.Khalid

Al-Mashouq(.Mashouq@ksu.edu.sa)

input signal or reference signal is latest area of research.

Input-output based speech quality assessment in

objective speech quality measurement gave good

correlations with reaches up to 99% in some cases [3].

The performance of objective measurement is basically

achieved by correlating their results with the subjective

quality measure. Estimating the speech quality without

the presence of input signal or reference signal is latest

area of research.

Jin Liang and R. Kubichek [4] published a first paper

on output-based objective speech quality using

perceptually based parameters as features. The results

were quiet appreciable with 90% correlation.

R. Kubichek and Chiyi Jin [5] used the vector

quantization method with 83% correlation achievement.

An output based speech quality measurement technique

using visual effect of a spectrogram is proposed in [6].

An output-based speech quality evaluation algorithm

based on characterizing the statistical properties of

speech spectral density distribution in the temporal and

perceptual domains is proposed in [7]. The correlations

results achieved with subjective quality scores were

0.897 and 0.824 for the training data and testing data set

respectively.

A time-delay multilayer neural network model for

measuring the output based speech quality was proposed

by Khalid Al-Mashouq and Mohammed Al-Shayee in

[8]. The correlation achieved for speaker and text

independent was 0.87.

In this Paper, we presented our work for determining

the call quality parameters such as average signal

strength, Call drop information. Then final call quality is

computed from the extracted parameters.

This research is continuation of the work that has

been proposed in [9]-[17]. Call Quality measurement,

escalation and analyzation is the research being

conducted from past few years and the QMeter® is

registered software developed as a part of this research.

2. Call Quality Computation

and Escalation

The proposed research parameters and the proposed

methodology address the call quality issues by the

mobile operator’s from end-users perspective. The

system logs the signal strength information for every

5ms if there is change in the signal strength information

A Model for Call Quality Computation and

Collection in Mobile Telecommunication Networks Akram Aburas & Prof. Khalid Al-Mashouq

Aburas et al.: A Model for Call Quality Computation and Collection in Mobile Telecommunication Networks.

International Journal Publishers Group (IJPG) ©

221

and calculates the score at every 5ms based on the

below Table 1 and calculates the average signal strength

at the end of the call. The call drop information such as

normally dropped from either of the party or dropped

due to network issues or during the cell change is also

recorded and is reported in Table 2.

TABLE 1:

SIGNAL STRENGTH SCORE

Signal Range (dBm) Classification

-120 to -95 Extremely Bad

-95.00 to -85.00 Bad

-85.00 to -75.00 Average

-75.00 to -65.00 Good

-65.00 to -55.00 Excellent

TABLE 2:

CALL DROP SCORE

Call Drop Score

Drop due to Network issue 1 (Extremely Bad)

Normally Dropped 5 (Excellent)

The call quality is derived from the scores

computed from the above parameters as below:

Call Quality = (Signal Strength Score+Call drop

Score)/2

The below Table 3 shows final call quality

classification.

TABLE 3:

CALL QUALITY SCORE

Score Classification

<1 Extremely Bad

1 - 2 Bad

2- 3 Average

3- 4 Good

4 - 5 Excellent

The basic flowchart for the Call Quality computation is

depicted in Fig. 1.

The application uses the GPS coordinates to mark

the landmarks and also uses the LAC (Location Area

Code) and cell id related to that particular operator. The

LAC and cell-id’s are more meaningful to the operator,

if he wants to view the call quality on the maps at their

end. The landmark for each call on the mobile

equipment (ME) is marked on the map with colors. The

landmarks that were marked with red colors are the calls

dropped due to network issues and the landmarks that

were marked with green colors are normally dropped

calls. The different colors landmarks help one to easily

visualize and analyze the calls.

Start

Active call?

Process the signal when changed or for

every5ms and log the information

depending on the option selected

At the end of the active call:

1. capture the call drop information

2. calculate the call quality

3. Mark the landmarks on the map

4. log the call quality parameters.

Yes

No

Incoming or outgoing call

notification

Fig. 1. Call Quality Computation

The system has the ability to send the call quality

information to a particular number as a call quality

escalation. It has the provision of setting the mobile

number, to which the sms would be sent automatically at

the end of call. The system has the option of setting to

send the sms always, less than bad etc. It has also the

provision to use the call quality based on call statistics,

where the call quality is computed at the end of 10 calls

and the call statistics can also be sent as sms. This

flexibility allows the operator to fix the parameter

values that can be escalated for immediate action. The

sms call quality module is depicted in Fig. 2.

3. Collection Model for Call

Quality

The parameters average signal strength score and

call drop information scores are calculated and the final

call quality score is derived as per the average scores

proposed in previous section at the end-user mobile.

The application at the end of the call, all the parameters,

cell id and GPS cords will be sms’ed to some predefined

short code at the operator end. All the information that

International Journal of Advanced Computer Science, Vol. 1, No. 6, Pp. 220-223, Dec. 2011.

International Journal Publishers Group (IJPG) ©

222

has been collected at the operator will be retrieved and

stored in the database and would be available to the

operator which would be very helpful to analyze the call

quality for benchmarking and addressing other issues

related to call quality. Various reports can be derived

based on different parameters at different cell id’s and

GPS coordinates, which would be very useful for the

operators. Further, the call quality below threshold as

perceived by the end-user can be used by the operator to

apply the tariff redemption or adding bonus amount or

minutes to the subscribers based on the policies defined

by them. A block diagram showing the flow of call

quality from mobile equipment to QMeter Application at

the operator has been shown in Fig. 3. Also the tariff

redemption and bonus minutes can be used as a

marketing tool by the operators. Different tariff

redemption methods were proposed in [14] based on call

quality parameters.

Start

Want to SMS ?

Send the latest call quality score depending on

the condition set

Send the latest call bundle score depending on

the options set on the call bundle score

Stop

Yes

No

SMS last call quality score

Auto ?

Want to send latest

score?

SMS last call bundle score

Want to send last

call bundle score?

No

Yes

Yes

YesNo

Fig. 2. SMS Call Quality

QMeter® is set of tools for call quality

measurement developed as an enterprise application

which collects the call quality parameters from the

end-user mobile equipments to analyze and process the

call quality.

ME

ME

ME

BSC

MSC(Mobile Switching

Center)

SMSC(SMS

Center)

QMeter Enterprise

Application

Data

Fig. 3. Call Quality Collection Model

Acknowledgment

This research is acknowledged to the ACES

(Advanced Communications & Electronics) research

group.

References

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speech quality assessment of narrowband telephone

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[2] A. Bayya & M. Vis, "Objective measure for speech

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International Journal of Computer Networks &

Communications, Wireless & Mobile Networks.

Akram A. Aburas holds Masters and Bachelor's degree

in Electrical engineering. Posses over fifteen years

experience including executive manager in Telecom

industries. Founder, Partner and Board Member in

several ME regional companies. He has several

publications in the area of Telecommunications, revenue

assurance, call and speech quality. He is currently the

CEO of ACES (Advanced Communications &

Electronics Systems), Riyadh, Saudi Arabia

Prof. Khalid Al-Mashouq holds Ph.D. in Electrical

Engineering and active researcher in Call and Speech

Quality in Telecommunication Network. Currently he is

professor in king Saud university, Riyadh, KSA.