Caso de estudio Nacional Refugio José Ribas del Parque Nacional Cotopa

159 LocationAreaCode = 352 CellId = 12211 2011/04/21 - 13:20:36 : : Signal strength is = 80 dBm, 7 bars 2011/04/21 - 13:20:58 : : Signal strength is = 83 dBm, 7 bars 2011/04/21 - 13:20:59 : : Signal strength is = 82 dBm, 7 bars 2011/04/21 - 13:21:07 : : Signal strength is = 77 dBm, 7 bars 2011/04/21 - 13:21:12 : : Signal strength is = 81 dBm, 7 bars 2011/04/21 - 13:21:44 : : Signal strength is = 79 dBm, 7 bars 2011/04/21 - 13:21:46 : : Signal strength is = 82 dBm, 7 bars 2011/04/21 - 13:21:47 : : Signal strength is = 78 dBm, 7 bars

2011/04/21 - 13:21:49 : : Call drop observer -> Event : Call state is changed. Phone status: Idle

2011/04/21 - 13:21:49 : : Average signal strength is 80 dBm (Average)

7. Sample call statistics

2011/04/24 - 07:45:33 : : 0 call attempts failed

2011/04/24 - 07:45:33 : : 10 call attempts successful :: Score: 3 (Average)

2011/04/24 - 07:45:33 : : 10 calls was normally dropped :: Score: 3 (Average)

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2011/04/24 - 07:45:33 : : **********************

Figures 2 and 3 depict the landmarks of successful calls, with colours in green and red showing the normally and hand-over dropped calls.

Figure 2: Landmarks for normally dropped calls

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8. Conclusion

The research presents a comprehensive amalgamation of research from different call quality measurement parameters, with final average call quality measurement, correlating the call quality scores with subjective scores, call quality escalation, landmarking the call quality, and tariff propositions based on call quality parameters. The research is useful for the telecom industry to understand call quality from the end- user’s perspective and take the necessary measures to reduce customer “churn” and increase average revenue per client. The research could also be used by the telecom regulatory authorities to monitor whether operators are meeting required licence criteria of quality of network from end-user’s perspective. Further, it can be used as a consumer protection tool to ensure that tariffs correlate with call quality.

References

[1] ITU-T Rec., “Perceptual Evaluation of Speech Quality (PESQ): an Objective Method for End-to-End Speech Quality Assessment of Narrowband Telephone Networks and Speech Codecs”, P. 862, 2001.

[2] Aruna Bayya and Marvin Vis, "Objective Measure for Speech Quality Assessment in Wireless Communications", Acoustics, Speech and Signal Processing, ICASSP- 96, IEEE International Conference 1996, Vol. 1, pp. 495-498.

[3] Jin Liang and Robert Kubichek, “Output-Based Objective Speech Quality",

Vehicular Technology Conference, 1994 IEEE Conference, Vol. 3, pp. 1719-1723. [4] Chiyi Jin and Robert Kubichek, "Output-Based Objective Speech Quality Using

Vector Quantization Techniques", Signals, Systems and Computers, Conference Record of the Twenty-Ninth Asilomar Conference, IEEE 1995, Vol. 2, pp. 1291- 1294.

[5] G. Chen and V. Parsa, "Output-Based Speech Quality Evaluation by Measuring Perceptual Spectral Density Distribution", IEE Electronics Letters 40, pp. 783-785, 2004.

[6] D. Picovici and A.E. Mahdi, “Output-Based Objective Speech Quality Measure Using Self-Organizing Map”, IEEE Proceedings of ICASSP-2003, Vol. 1, pp. 476– 479, 2003.

[7] Khalid A. Al-Mashouq and Mohammed S. Al-Shaye, “Output-Based Speech Quality Assessment with Application to CTIMIT Database”, Seventeenth International Conference on Computers and Their Applications, CATA 2002. [8] Akram Aburas, J. G. Gardiner and Z. Al-Hokail, “Symbian Based Perceptual

Evaluation of Speech Quality for Telecommunication Networks”, Sixth International Conference on Computing, Communications and Control Technologies: CCCT 2008, Orlando, Florida, USA.

[9] Akram Aburas, J. G. Gardiner and Z. Al-Hokail, “Perceptual Evaluation of Speech Quality-Implementation Using a Non-Traditional Symbian Operating System”,

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published in Proceedings of the Fifth IEEE-GCC Conference on Communication and Signal Processing: IEEE-GCC, 17-19 March 2009, Kuwait City, Kuwait. [10] Akram Aburas, J.G. Gardiner and Zeyad Al-Hokail, “Emerging Results on

Symbian Based Perceptual Evaluation of Speech Quality for Telecommunication Networks”, CCCT 2009, Orlando, Florida, USA.

[11] Akram Aburas, Professor J.G. Gardiner and Dr Zeyad Al-Hokail, “Transitional Results on Symbian Based Call Quality Measurement for Telecommunication Network”, International Cable Protection Committee 2009, Taiwan.

[12] Akram Aburas, Professor J.G. Gardiner, Professor Khalid Al-Mashouq and Dr Zeyad Al-Hokail, “Results of Ongoing Symbian Based Call Quality Measurement for Telecommunication Network”, Telfor 2009, Belgrade.

[13] Akram Aburas, Professor J.G. Gardiner and Dr Zeyad Al-Hokail, “Call Quality Measurement for Telecommunication Network and Proposition of Tariff Rates”, CCCT 2010, Orlando, Florida, USA.

[14] Akram Aburas, Professor J.G. Gardiner, Professor Khalid Al-Mashouq and Dr Zeyad Al-Hokail, “Perceptual Evaluation of Call Quality and Evaluation of Telecom Networks”, STS-2010, Riyadh, Kingdom of Saudi Arabia.

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From the International Journal of Advanced Computer Science, Vol. 1 No. 1, pp. 1-8, January 2011.

A Model for Call Quality Computation and Collection in Mobile Telecommunication Networks

Akram Aburas and Professor Khalid Al-Mashouq

1. Introduction

Traditional speech quality measurement techniques use a subjective listening test called mean opinion score (MOS). It is based on human perceived speech quality rated on a scale of 1 to 5, where 1 is the lowest perceived quality and 5 is the highest.

Subjective listening tests are expensive, time consuming and tedious. Currently, therefore, most systems use objective evaluation of speech quality using mobile

computing techniques. Objective testing systems use automated speech quality measurement techniques. Three well known objective measurement techniques are perceptual speech quality measure (PSQM), the perceptual analysis measurement system (PAMS) and perceptual evaluation of speech quality (PESQ).

Objective speech quality measurement techniques are mostly based on the input- output approach [1]. In input-output, objective measurement techniques basically work by measuring the distortion between the input and the output signal. The input signal would be a reference signal and output signal a received signal.

Input-output based speech quality assessment in objective speech quality measurement gave good correlations, with reaches up to 99 per cent in some cases [2]. Estimating the speech quality without the presence of the input or reference signal is the latest area of research.

Jin Liang and R. Kubichek [3] published a paper on output-based objective speech quality using perceptually-based parameters as features. The results showed 90 per cent correlation. R. Kubichek and Chiyi Jin [4] used the vector quantization method, which showed 83 per cent correlation.

An output based speech quality measurement technique using the visual effect of a spectrogram is proposed in [5]. 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 [6]. The correlations achieved with subjective quality scores were 0.897 and 0.824 for the training data and testing data set respectively.

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A time-delay multilayer neural network model for measuring output based speech quality was proposed by Khalid Al-Mashouq and Mohammed Al-Shaye in [7]. The correlation achieved for speaker and text independently was 0.87.

In this paper we present our work for determining call quality parameters such as average signal strength, successful call rate and successful hand-over rate with respect to signal strength and successful call rate. Final call quality is computed from the extracted parameters.

This research is the continuation of the work that has been described in [8-16].