This study presents an evaluation of mobile communication network performance carried out in Nigeria; it aims to evaluate the performance of mobile communication network based on Quality of Service parameters. The Quality of Service considered blocked calls, dropped calls, speech quality, and Received Signal Strength (RSS) in three different mobile communication networks with the following nomenclature NET A, NET B and NET C in Nigeria. The RSS data were obtained using TECNO mobile equipment, with model number TECNO N3 and Andriod Version 2.3.5, with RF network tracker software used to evaluate the Mobile communication network providers operating on the 900/1800 MHz spectrum in Nigeria. Also, subjective measurement methods using Mean Opinion Score (MOS) were deployed to determine the speech quality level. The subjective technique became accomplished in a quiet location at diverse instances, with endorsed sentences at one-of-a-kind durations respectively for the three cell networks below consideration. The investigation was from June 2014 to July 2015 at Ambrose Alli University campus, Ekpoma. The male and female speech quality was examined; also, comparison analysis became finished from various networks to decide their speech fine, using different statistical techniques such as Root Mean Square Error (RMSE) and Correlation Coefficient. Additionally, different reasons for drop calls and low (poor) speech high-quality degrees were highlighted. It is determined that the full average RMSE value from the male speech excellent received from the three networks taken into consideration is 0.601. In contrast, the correlation coefficient value got between the male and female quality of speech was invariance. Also, Net A has higher (better) speech quality than Net B and Net C. In addition, speech distortions have led to a high number of drop calls in mobile communication networks. These findings will help in improving mobile communication in Nigeria.

1. O. A. Osahenvemwen and J. Emagbetere, “Traffic analysis in mobile communication in Nigeria,” Journal of Emerging Trends in Engineering and Applied Sciences, vol. 3, no. 2, pp. 239-243, 2012.
   
2. O. A. Osahenvemwen, F. O. Edeko and J. Emagbetere, “The effects of ostentatious calls in GSM networks,” International Journal of Computer Applications, vol. 48, no. 9, pp. 33-37, 2012.
   
3. B. Haider, M. Zafrullah and M. K. Islam, “Radio frequency optimization & QoS evaluation in operational gsm network,” in Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 1-6, 2009.
   
4. J. G. Beerends, A. P. Hekstra, A. W. Rix and M. P. Hollier, “Perceptual Evaluation of Speech Quality (PESQ) the new ITU standard for end-to-end speech quality assessment part II: Psychoacoustic model,” Journal of the Audio Engineering
   Society, vol. 50, no. 10, pp. 765-778, 2002.
   
5. A. Srinivasan, “Speech recognition using Hidden Markov model,” Applied Mathematical Sciences, vol. 5, no. 79, pp. 3943-3948, 2011.
   
6. N. Bhatt and Y. Kosta, “Proposed modifications in ETSI GSM 06.10 full rate speech codec and its overall evaluation of performance using MATLAB,” International Journal of Speech Technology, vol. 14, no. 3, pp. 157-165, 2011. https://doi.org/10.1007/s10772-011-9093-5
   
7. M. Fajkus, M. Mikulec, M. Voznak, M. Tomis and P. Fazio, “Speech quality measurement of GSM infrastructure built on USRP N210 and open BTS project,” Advances in Electrical and Electronic Engineering, vol. 12, no. 4, pp. 341-347, 2014. https://doi.org/10.15598/aeee.v12i4.1215
   
8. M. Voznak and J. Rozhon, “Automated speech quality monitoring tool based on perceptual evaluation,” in 6th International Conference on Communications and Information Technology. Wisconsin: World Scientific and Engineering Academy and Society (WSEAS), pp. 95-105, 2012.
   
9. R. Verma, S. Mandal and A. Kumar, “Improved voice quality of GSM network through voice enhancement device,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 7, pp. 77-80, 2012.
   
10. VoIP Mechanic. (2015). MOS- Mean Opinion Score for VoIP Testing [online]. Available: https://goo.gl/1BZuv7
    
11. S. Bhandare and M. R. Dixit, “Positioning of mobile in GSM network using received signal strength,” International Journal of Computer & Communication Engineering Research, vol. 1, no. 2, pp. 56-58, 2013.
    
12. S. Helhel, S. Ozen and H. Goksu, “Investigation of GSM signal variation depending weather conditions,” Progress in Electromagnetics Research B, vol. 1, pp. 147-157, 2008. https://doi.org/10.2528/PIERB07101503
    
13. P. Saveeda, E. Vinothini, V. Swathi and K. Ayyappan, “Received Signal Strength (RSS) calculation for GSM cellular system at BSNL Pondicherry using modified HATA model,” International Journal of Science, Engineering and Technology
    Research, vol. 2, no. 1, pp. 43-7, 2013.
    
14. O. O. Shoewu and L. A. O. Akinyemi, “The effect of climatic change on GSM signal propagation,” Research Journal of Computer Systems Engineering, vol. 4, no. 2, pp. 471-478, 2013.
    
15. Wikipedia. (n.d). Signal strength in telecommunications [online]. Available: https://goo.gl/IqGa5E
    
16. S. Sanjay, Computer Networks, 1st ed. New Delhi, India: S. K. Kataria and Sons, 2010, pp. 621-629.
    
17. S. Kyriazakos, N. Papaoulakis, D. Nikitopoulos, E. Gkroustiotis, C. Kechagias, C. Karambalis and G. Karetsos, “A comprehensive study and performance evaluation of operational GSM and GPRS systems under varying traffic conditions,” in IST Mobile Summit, Thessaloniki, Greece, June 17-19, 2002.
    
18. International Telecommunication Union (ITU). (2010). Teletraffic Engineering [Online]. Available: https://goo.gl/lsZEm
    
19. C. I. De Mattos, E. P. Ribeiro and C. M. Pedroso, “A new model for VoIP traffic generation,” in The 7th International Telecommunications Symposium, 2010.
    
20. L. Sharma, Operation Research Theory and Application. India: Macmillan Publishers India Ltd, 2010.
    
21. C. Balint, G. Budura and A. Budura, “Mixed traffic models for dimensioning radio resources in GSM/GPRS networks,” WSEAS Transaction on Communications, vol. 9, no. 3, pp 233-242, 2010.
    
22. S. Belhaj and M. Tagina, “Modeling and prediction of the Internet end-to-end delay using recurrent neural networks,” Journal of Networks, vol. 4, no. 6, pp. 528-535, 2009. https://doi.org/10.4304/jnw.4.6.528-535
    
23. W. A. Massey, “The analysis of queues with time-varying rates for telecommunication models,” Telecommunication Systems, vol. 21, no. 2, pp. 173-204, 2002. https://doi.org/10.1023/A:1020990313587
    
24. M. Barile, P. Camarda, R. Dell’Aquila and N. Vitti, “Parametric models for speech quality estimation in GSM networks,” in International Conference on Software in Telecommunications and Computer Networks, pp. 204-208, Sept. 29- Oct. 1, 2006. https://doi.org/10.1109/softcom.2006.329748

O. A. Osahenvemwen “Evaluation of mobile communication network performance,” International Journal of Technology and Engineering Studies, vol. 3, no. 1, pp. 09-19, 2017