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International Journal of Engineering and Techniques(IJET) Paper Title : Frequency Spectrum Sensing and SNR Threshold Detection

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I1P3
Authors: Mr. Imran Sayyed1, Mr. M.H. Naikwadi, Mrs.S.P.Sagat

         



MLA Style: -Mr. Imran Sayyed1, Mr. M.H. Naikwadi, Mrs.S.P.Sagat "Frequency Spectrum Sensing and SNR Threshold Detection " Volume 7 - Issue 1(21-30) January - February,2021 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org

APA Style: -Mr. Imran Sayyed1, Mr. M.H. Naikwadi, Mrs.S.P.Sagat "Frequency Spectrum Sensing and SNR Threshold Detection " Volume 7 - Issue 1(21-30) January - February,2021 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org

Abstract
- In order to observe the occupancy status and to locate spectrum holes, the energy spectrum sensing feature plays a key role in the efficiency of cognitive radio networks. Usually, spectrum allocation occurs through a licensing process. Users of Cognitive Radio are unlicensed users who dynamically locate unused licensed spectrum for their own use without interfering with licensed users. Spectrum sensing, spectrum decision-making, spectrum sharing and spectrum mobility are four key functions of cognitive radio systems. In this paper we study the spectrum occupancy measurements and SNR threshold in a new perspective. The thresholds of the spectrum are detected using low SNR phenomenon. The decision threshold plays a crucial role in assessing the presence or absence of the primary users signal. The configuration of the measuring setup and other issues relating to the measurements of frequency and Detection of SNR threshold were addressed.

Reference
[[1] Y. Arjoune, Z. El Mrabet, H. El Ghazi, and A. Tamtaoui, “Spectrum sensing: Enhanced energy detection technique based on noise measurement,” 2018 IEEE 8th Annu. Comput. Commun. Work. Conf. CCWC 2018, vol. 2018-Janua, pp. 828–834, 2018, doi: 10.1109/CCWC.2018.8301619. [2] A. A. Cheema and S. Salous, “Spectrum occupancy measurements and analysis in 2.4 GHZ WLAN,” Electron., vol. 8, no. 9, pp. 1–14, 2019, doi: 10.3390/electronics8091011. [3] M. Wellens and M. Petri, “Lessons Learned from an Extensive Spectrum Occupancy Measurement Campaign and a Stochastic Duty Cycle Model.” [4] G. Ding, S. Member, Y. Jiao, J. Wang, and S. Member, “Spectrum Inference in Cognitive Radio Networks : Algorithms and Applications,” no. c, pp. 1–34, 2017, doi: 10.1109/COMST.2017.2751058. [5] Y. Chen, S. Member, H. Oh, and Y. Chen, “A Survey of Measurement-based Spectrum Occupancy Modelling for Cognitive Radios,” vol. 747, no. c, pp. 0–36, 2014, doi: 10.1109/COMST.2014.2364316. [6] F. Azmat, Y. Chen, S. Member, N. Stocks, and N. I. Mar, “Analysis of Spectrum Occupancy Using Machine Learning Algorithms,” pp. 0–22. [7] K. Ko, “Spectrum Sensing in Cognitive Radio Networks : Threshold Optimization and Analysis,” pp. 1–22. [8] Y. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-Throughput Tradeoff for Cognitive Radio Networks,” pp. 5330–5335, 2007. [9] M. Lopez-benftez and F. Casadevall, “Methodological Aspects of Spectrum Occupancy Evaluation in the Context of Cognitive Radio,” pp. 199–204, 2009. [10] https://helpcenter.engeniustech.com/hc/en-us/articles/234761008-What-is-RSSI-and-its-acceptable-signal-strength-#:~:text=RSSI%20stands%20for%20Received%20Signal,a%20lower%20overall%20data%20throughput.

Keywords
Spectrum Sensing, spectrum occupancy, SNR threshold, Dynamic threshold, Noise estimation.

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