Comparative Analysis of Signal Level of FM Radio Spectrum
Abstract
This paper presents a comparative analysis of signal level of Frequency Modulation (FM) radio spectrum measured by cognitive radio and signal level calculated from some existing models. Cognitive radio was used to measure the received signal level of frequency modulation radio spectrum for four selected locations (University of Benin, Benson Idahosa University, Well Spring University and Igbinedion University) in Edo State, Nigeria. Also, three path loss models were used to determine the prediction for the path loss, which was used to determine the received signal level of FM radio spectrum respectively. Results showed that the received signal power levels measured using cognitive radio were lower than that of the existing models. This is because received signal power levels predicted from existing models do not account for variations in terrain, structural blockage and other factors that are peculiar to the investigated environment, which affects the frequency modulation radio stations transmitting signal. Hence, the results of the signal level of FM radio spectrum for the four locations derived using cognitive radio provide a better result than that obtained from existing. Consequently, the measured results could be used to formulate a model for estimating the signal level of FM radio spectrum for locations within Nigeria with similar environmental conditions to the investigated environment.
Downloads
References
Bolli, S., Mohammed, Z., and Ali, K.. (2014). RMSE comparison of path loss models for UHF/VHF bands in India, IEEE REGION 10 SYMPOSIUM, 330 – 335.
Haider, K. H., Intisar, Al-M., and Abbas, I. J. (2018). Analysing Study of Path loss Propagation Models in Wireless Communications at 0.8 GHz, Journal of Physics: Conference Series, volume 2018, Article ID 9142367
Otermat, D. T., Kostanic, I., and Otero, C. E. (2016). Analysis of the FM Radio Spectrum for Secondary Licensing of Low-Power Short- Range Cognitive Internet of Things Devices", IEEE Access.
Perez-Vega, C., and Zamanillo, J. M. (2002). Path-Loss Model for Broadcasting Applications and Outdoor Communication Systems in the VHF and UHF Bands," IEEE Transactions on Broadcasting, 48(2): 91-96.
Roberson D. A., Hood, C. S., LoCicero J. L., and MacDonald, J. T. (2006). Spectral Occupandy and Interference Studies in support of Cognitive Radio Technology Deployment, in IEEE Workshop on Network Technologies for Software Defined Radio Networks, Reston, 167 - 175.
Santosh, S., Saubhagya, D., Shekar, N., and Shet, V. (2014). Dynamic Spectrum Allocation in Wireless sensor Networks, International Journal of Modern Engineering Research, 4: 18 – 25.
Sascha, K., Meledin, D., Desmaris, V., Pavolotsky, A., Rashid, H., and Belitsky, V. (2018). Noise and IF Gain Bandwidth of a Balanced Waveguide NbN/GaN Hot Electron Bolometer Mixer Operating at 1.3 THz, IEEE Transactions on Terahertz Science and Technology, 3: 365 – 371.
Smita, S. A., and Rakhi, D. A. (2018). Bluetooth Low Energy-Based Applications, IGI Global, 95 - 112.
Stewart, R. W., Crockett, L., Atkinson, D., Barlee, K., Crawford, D., Chalmers, I., McLernon, M., Sozer, E. (2015). Software Defined Radio using Matlab & Simulink and the RTL-SDR, Strathclyde Academic Media, California, 53: 64 – 71.
Copyright (c) 2018 O. S. Omorogiuwa, F. I. Anyasi, M. S. Okundamiya

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.