Monitoring of Spectrum Usage and Signal Identification Using Cognitive Radio

The monitoring of spectrum usage and signal identification, using cognitive radio, is done to identify frequencies that are vacant for reuse. It has been established that ‘internet of things’ device uses secondary frequency which is free, thereby facing the challenge of interference from other users, where some primary frequencies are not being utilised. The design was done by analysing a specific frequency spectrum, checking if all the frequency stations that range from 87.5-108 MHz are presently being used in Benin City, Edo State, Nigeria. From the results, it was noticed that by using Software Defined Radio/Simulink, we were able to identify vacant frequencies in the range of frequency under consideration. Also, we were able to use the significance of energy detection threshold to reuse this vacant frequency spectrum, when the cognitive radio displays a zero output (that is decision H0), meaning that the channel is unoccupied. Hence, the analysis was able to find the spectrum hole and identify how it can be reused.

• O. S. Omorogiuwa
• E. J. Omozusi

• Spectrum
• interference
• telecommunication
• cognitive radio
• frequency.

[1] Miftahabdullahi and Mohammed (2015). Estimation of Detection Threshold For Spectrum Sensing In Cognitive Radio Using Adaptive Neuro Fuzzy Inference System and Monte Carlo Techniques, PhD, Ahmadu Bello University, Zaria.
[2] Feng Hu, Bing Chen, XiangpingZhai and Chunsheng Zhu (2017), Channel selection policy in Multi-SU and Multi – PU Cognitive Radio Networks with Energy Harvesting for Internet of Everything. Journal of mobile information systems 2 (4): 13-22
[3] Otermat D. T., I. Kostanic and C. E. Otero (2016), Analysis of the FM Radio Spectrum for Secondary Licensing of Low-Power Short-Range Cognitive Internet of Things Devices, IEEE Access, 99: 11-19
[4] Tan Zhang, Ashish Patro, NingLeng and Suman Banerjee (2015), A Wireless Spectrum Analyzer in Your Pocket, snoopy_hotmobile.
[5] Li J., H. Zhao, J. Wei (2016), ‘Sender-jump receiver-wait: a blind randezvous algorithm for distributed cognitive radio networks,’ in proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’ 16), Valencia, Spain.
[6] National Frequency Management Council of Nigeria (2014), National Frequency Allocation Table.
[7] https://www.morenaija.ng/2017/02/list-of-all-radio-stations-in-nigeria, retrieved 14/02/2017.
[8] RTL-SDR.com (2017), using RTL-SDR in Cognitive Radio Energy detector in MATLAB experiments. Available at rtl- sdr.com.html.