Assessment of Empirical Models and the Impact of Atmospheric Parameters over GSM Channels in Zaria City, Nigeria

Authors

  • Muftahu Suleiman

    Department of Physics, Federal University Dutsin-Ma, Katsina State 821001, Nigeria.

    Department of Electrical/Electronic Engineering, Katsina State Institute of Technology and Management, Katsina, Katsina State 821101, Nigeria.

  • Akinsanmi Akinbolati

    Department of Physics, Federal University Dutsin-Ma, Katsina State 821001, Nigeria.

  • Ibrahim Bashir

    Department of Physics, Federal University Dutsin-Ma, Katsina State 821001, Nigeria.

  • Nehemiah Akamba

    Department of Physics, Federal University Dutsin-Ma, Katsina State 821001, Nigeria.

DOI:

https://doi.org/10.30564/jeis.v6i2.7960
Received: 14 August 2024 | Revised: 31 August 2024 | Accepted: 5 September 2024 | Published Online: 20 October 2024

Abstract

This study evaluates the accuracy of empirical path loss models and examines the influence of atmospheric parameters on 2G and 3G GSM channels for MTN and 9-Mobile networks in Zaria City, Nigeria. The Root Mean Square Error (RMSE) values of four models COST-231, ECC-33, Plane Earth, and Ericsson were analyzed to determine model effectiveness in predicting path loss. Findings reveal that the Ericsson model is most accurate for 2G networks, making it ideal for network planning and optimization, while the COST-231 model performs best for 3G networks, underscoring the importance of selecting models suited to the specific network generation. In addition, the study investigates correlations between path loss and environmental factors, including Line of Sight (LOS), Received Signal Strength (RSS), temperature, humidity, elevation, and pressure. Results indicate a strong negative correlation between path loss and both RSS and pressure, and a strong positive correlation between path loss and both LOS and temperature, while humidity and elevation show moderate and weaker correlations with path loss, respectively. These findings emphasize the significant role of environmental parameters in signal propagation and highlight their critical importance in accurate path loss prediction and effective network planning in urban areas like Zaria City. This study provides a comprehensive reference for future network optimization efforts, offering insights into model selection and environmental considerations essential for enhancing GSM network performance in similar urban environments.

Keywords:

Path Loss Models; GSM Channels; Zaria City; Nigeria; RMSE; Network Planning

References

[1] Oseni, O.F., Popoola, S.I., Abolade, R.O., et al., 2014. Comparative analysis of received signal strength prediction models for radio network planning of GSM 900 MHz in Ilorin, Nigeria. International Journal of Innovation Technology and Exploration Engineering. 4(3), 45–50.

[2] Akinwole, B.O, Esobinenwu., C., 2013. Adjustment of COST 231 Hata path model for cellular transmission in Rivers State. IOSR Journal of Electrical and Electronics Engineering. 6(5), 16–23.

[3] Emeruwa, C., Iwuji, P.C., 2018. Determination of a path loss model for long term evolution (LTE) in Yenagoa. International Journal of Engineering Science. 7(10), 38–44.

[4] Alor, M.O., 2015. Efficient pathloss model for determining mobile radio link design. International Journal of Scientific Research Engineering & Technology. 1(3), 270–276.

[5] Adu, O.I., Idachaba, F.E., Alatishe, A.A., 2014. Refarming 1800MHz GSM spectrum to LTE: The effects on coverage based on pathloss estimation. Proceedings of the World Congress on Engineering; July 2–July 4, 2014; London, UK. pp. 2–4.

[6] Kadiri, K.O, Somoye., O.A., 2014. Computer simulation of path loss characterization of a wireless propagation model in Kwara State, Nigeria. International Journal of Computer Information Technology. 3(3), 610–615.

[7] Adeniji, K.A., Ikpeze, O.F., Ejidokun, T.O., et al., 2017. Analysis of propagation models for base station antenna: A case study of Ado-Ekiti, Nigeria. ABUAD Journal of Engineering Research Development. 1(1), 124–129.

[8] Emeruwa, C., 2015. Comparative analysis of signal strengths of some cellular networks in Umuahia Eastern Nigeria. Journal of Electronics and Communication Engineering Research. 2(10), 1–5.

[9] A.,Tonga, M.A., Danladi, S.N., Dominic, 2015. Investigation of building penetration selected building structures in Kaduna. Journal of Electronics and Communication Engineering. 10(4), 56–60.

[10] Okundamiya, M.S., 2015. Modelling and optimization of a hybrid energy system for GSM Base Transceiver Station Sites in Emerging Cities [Doctoral dissertation]. Benin City, Nigeria: University of Benin. pp. 53–55.

[11] Okwurume, C.N., 2024. Optimizing telecommunication services quality delivery: The impact of competitive intelligence systems. ARCN International Journal of Advanced Academic and Educational Research. 14(1). 1–15

[12] Ogbulezie, J.C., Onuu, M.U., Bassey, D.E, et al., 2013. Site specific measurements and propagation models for GSM in three cities in northern Nigeria. American Journal of Scientific and Industrial Research. 4(2), 238–245.

[13] Simmons, A.J., Willett, K.M., Jones, P.D., et al. 2010. Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets. Journal of Geophysical Research: Atmospheres. 115(D1), 2009JD012442. DOI: https://doi.org/10.1029/2009JD012442

[14] Onuu, M.U., Adeosin, A., 2008. Investigation of propagation characteristics of UHF waves in Akwa Ibom State, Nigeria. Indian Journal of Radio and Space Physics. 37(3), 197–203.

[15] A., Obot, O., Simeon, J., Afolayan, 2011. Comparative analysis of path loss prediction models for urban macrocellular environments. Nigerian Journal of Technology. 30(3), 50–59.

[16] Hossain, M.S., Rahman, M.A., Islam, M.R., 2012. Path Loss Prediction Models for wireless communication in urban and suburban environments. International Journal of Computer Science and Network Security. 12(2), 26–33.

[17] Nwoye, O.U., Idachaba, F.E., 2013. Comparative study of empirical propagation models for GSM 900/1800 networks in Nigeria. International Journal of Engineering and Technology. 3(8), 34–40.

[18] Ayegba, A., Asemota, J., Agbaje, M., 2014. Performance evaluation of path loss models in urban environment at 800 MHz frequency band. Journal of Communications and Network. 6(4), 345–351.

[19] Aliyu, A.S., Onifade, B., Ijeoma, P., 2016. Evaluation of path loss models for effective coverage prediction in urban environments. International Journal of Wireless Communications and Mobile Computing. 4(3), 23–30.

[20] Usman, A.U., Okereke, O.U. and Omizegha, E.E., 2015. Instantaneous GSM signal strength variation with weather and environmental factors. American Journal of Engineering Research. 4(3), 104–115.

[21] Sadiq, M.H., Usman, A., Musa, A., 2018. Study on the path loss and impact of environmental factors on GSM signal strength in Zaria, Nigeria. Journal of Telecommunication, Electronic and Computer Engineering. 7(3), 123–128.

[22] Dib, N., Abou, R.H., El Zein, G., 2018. Path loss models for mobile communication networks in urban areas. International Journal of Wireless and Mobile Networks. 10(3), 29–38.

[23] Akinbolati, A., Agunbiade, O.J., 2020. Assessment of error bounds for path loss prediction models for TV white space usage in Ekiti State, Nigeria. International Journal of Information Engineering and Electronic Business. 3(1), 28–39.

[24] Akinbolati, A., Ajewole, M.O., 2020. Investigation of path loss and modeling for digital terrestrial television over Nigeria. Journal of Heliyon. 6(6), e04101.

[25] Hata, M., 1980. Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology. 29(3), 317–324.

[26] Akinbolati, A., Omotosho, Y.I., Adamu, I., et al., 2024. Pathloss assessment of a terrestrial digital UHF channel over Kano City, Nigeria. Nigerian Journal of Physics. 33(2), 15–22.

[27] Shoewu, O.O., Oborkhale, L.I., Salau, N.O., et al., 2016. Analysis of outdoor path loss measurements for triple frequency spectrum in Lagos State. Pacific Journal of Science and Technology. 18(2), 144–154.

[28] Akinbolati, A., 2020. Unpublished Lecture Notes on VHF Communication. Federal University Dutsinma, Department of Physics: Dutsin-Ma, Katsina State, Nigeria. pp. 5–8.

[29] Popoola, S.I., Atayero, A.A., Arausi, O.D., et al., 2018. Path loss dataset for modeling radio wave propagation in smart campus environment. Data in Brief. 17, 1062–1073.

[30] Ranvier, S., 2004. Path loss Models, S-72.333 Physical Layer Methods in Wireless Communication Systems. Available from: http://www.comlab.hut.fi/opetus/333/2004_2005_slides/Path_loss_models.pdf (cited 31 August 2024).

[31] Sarkar, T.K., Ji, Z., Kim, K., 2003. A survey of various propagation models for mobile communications. IEEE Antennas and propagation Magazine. 45(3), 51–82.

[32] E. J. Ofure, O. O. David, A. M. Oludare, et al., 2017. Impact of some atmospheric parameters on GSM signals. 2017 13th International Conference on Electronics, Computer and Computation (ICECCO); Nov 28–Nov 29, 2017; Abuja, Nigeria. pp.1–7. Doi: https://ieeexplore.ieee.org/document/8333335

[33] Ayeni, A.A., Faruk, N., Olawoyin, L., et al., 2016. Comparative assessments of some selected existing radio propagation models: A study of Kano City, Nigeria. European Journal of Scientific Research. 7(1), 120–127.

[34] Feher, K., 2015. Wireless Digital Communications Modulation & Spread Spectrum Applications. Prentice Hall: Upper Saddle River, NJ, USA. pp. 56–58.

[35] Rakesh, N., Srivasta, S.K., 2015. An investigation on propagation path loss in urban environments for various models at transmitter antenna height of 5om and receiver antenna heights of 10m, 15m and 20m respectively. International Journal of Research and Reviews in Computer Science. 3(4), 1761–1767.

[36] Bruno, S.L., Marcio, R., Gervasio, P., 2019. Comparision between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities. Journal of Microwaves, Optoelectronics and Electromagnetic Applications. 10(1), 106–113.

[37] Mollel, M.S., Michael, K., 2014. Comparison of empirical propagation path loss models for mobile communication. Computer Engineering Intellectual System. 5(9), 1–10.

[38] Erunkulu, O.O., Zungeru, A.M., Lebekwe, C. K., et al., 2020. Cellular communications coverage prediction techniques: A survey and comparison. IEEE Access, 8, 113052–113077.

[39] El Mashade, M.B.,Toeima, A.H. 2018. Performance Characterization of Spatial Diversity Based Optical Wireless Communication over Atmospheric Turbulence Channels. Radioelectronics and Communications Systems. 61(4), 135–152. DOI: https://doi.org/10.3103/S0735272718040015

[40] Alobaidy, H.A., Singh, M.J., Behjati, M., et al., 2022. Wireless transmissions, propagation and channel modelling for IoT technologies: Applications and challenges. IEEE Access, 10, 24095–24131.

[41] Mukaka, M.M., 2012. A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal. 24(3), 69–71.

[42] National Oceanic and Atmospheric Administration, 2020. Data Released on Zaria Climatological Condition. 09–11, 21 July 2020.

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How to Cite

Suleiman, M., Akinbolati, A., Bashir, I., & Akamba, N. (2024). Assessment of Empirical Models and the Impact of Atmospheric Parameters over GSM Channels in Zaria City, Nigeria. Journal of Electronic & Information Systems, 6(2), 49–64. https://doi.org/10.30564/jeis.v6i2.7960

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