A Mathematical Formulation for Frequency Spectrum in Cognitive Network
Adegbenjo A.1, Adekunle Y2, Agbaje, M.3
1Adegbenjo A8., Department of Computer Science & Information Technology, Babcock University, Ilishan-Remo, Ogun State.
2Adekunle Y, Department of Computer Science & Information Technology, Babcock University, Ilishan-Remo, Ogun State
3Agbaje, M. Department of Computer Science & Information Technology, Babcock University, Ilishan-Remo, Ogun State
Manuscript received on July 02, 2020. | Revised Manuscript received on July 05, 2020. | Manuscript published on July 30, 2020. | PP: 14-21 | Volume-10 Issue-1, July 2020. | Retrieval Number: F3395039620/2020©BEIESP | DOI: 10.35940/ijsce.F3395.0710120
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cognitive networks were developed to solve the current challenges in wireless networking by using established wireless spectrum because of the limited bandwidth available and the inefficiency of spectrum use. The CN with the fundamental ability of cognitive radio provides the spectrum-conscious model of wireless connections. This research provides a concept in a functional web environment to incorporate CRNs. The algorithm for the cluster head facilitates communication between secondary users, improves spectrum hole identification and thus allows spectrum use more effective. The developed spectrum analysis scheme was evaluated using the current measuring method on four radio technology (FM Broadcast, GSM-900 DL, DCS-1800 DL and UHF TV). The efficiency of radio technology networks was found to be very close. The CR signal strength varies while the signal power and the SIR are monitored continuously. The computation results show a substantial improvement of the PU’s throughput from 19.6 to 61.1%. The efficiency of power has been increased from 76.66% to 86.82% for FM Broadcast, 76.91% to 86.82% for GSM-900, 78.19% to 89.04% for DCS-1800 and 78% to 88.55% for UHF TV. The lower the interference, the better reception of the signal of the secondary users. The best signal response was at 12decibel, and the interference was able to reduce from 95% to 25%.
Keywords: Algorithm, Cognitive Network, HMM, MCPA and Spectrum