Cooperative Spectrum Sensing Performance Assessment using Machine Learning in Cognitive Radio Sensor Networks
Received: 17 November 2023 | Revised: 13 December 2023 | Accepted: 16 December 2023 | Online: 8 February 2024
Corresponding author: Govardhani Immadi
Abstract
The Cognitive Radio (CR) is an imminent technology, intended to make more effective use of the available spectrum by giving access to licensed frequency bands by unlicensed Secondary Users (SUs) without affecting Primary licensed Users (PUs). Depending on the region where the energy is being observed, each CR communicates local decisions or the seen energy to the Fusion Center (FC). This study presents the many plots that discuss an enhanced double threshold through the Cooperative Spectrum Sensing (CSS) approach. The FC then combines local decisions with the measured energy values to reach a final decision. The usage of several machine learning methods in spectrum decision with the myopic decision is estimated. The system seeks to enhance the long-term overall performance of the SU.
Keywords:
support vector machine, double threshold, cognitive radio, energy detection, GMM, Smith-Waterman algorithm, spectrum sensingDownloads
References
"Spectrum policy task force report," FCC, Washington DC, USA, FCC 02-155, May 2002.
"Notice of Proposed Rule Making and Order," FCC, Washington DC, USA, FCC 03-322, Dec. 2003.
Y.-E. Lin, K.-H. Liu, and H.-Y. Hsieh, "On Using Interference-Aware Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio Networks," IEEE Transactions on Mobile Computing, vol. 12, no. 3, pp. 461-474, March 2013. DOI: https://doi.org/10.1109/TMC.2012.16
P. Venkatapathi, H. Khan, and S. Srinivasa Rao, "Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor," International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 2655–2661, Dec. 2019. DOI: https://doi.org/10.35940/ijeat.F8703.129219
A. O. Isikman, M. Yuksel and D. Gündüz, "A Low-Complexity Policy for Outage Probability Minimization With an Energy Harvesting Transmitter," IEEE Communications Letters, vol. 21, no. 4, pp. 917-920, April 2017. DOI: https://doi.org/10.1109/LCOMM.2016.2641418
D. Han et al., "Spectrum sensing for cognitive radio based on convolution neural network," in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, Jul. 2017, pp. 1–6. DOI: https://doi.org/10.1109/CISP-BMEI.2017.8302117
M. Tang, Z. Zheng, G. Ding, and Z. Xue, "Efficient TV white space database construction via spectrum sensing and spatial inference," in 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), Sep. 2015.
A. M. Mikaeil, B. Guo, and Z. Wang, "Machine Learning to Data Fusion Approach for Cooperative Spectrum Sensing," in 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Shanghai, China, 2014, pp. 429-434. DOI: https://doi.org/10.1109/CyberC.2014.80
K. M. Thilina, K. W. Choi, N. Saquib, and E. Hossain, "Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks," IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2209-2221, Nov. 2013. DOI: https://doi.org/10.1109/JSAC.2013.131120
W. Gabran, P. Pawełczak, and D. Čabrić, "Multi-channel multi-stage spectrum sensing: Link layer performance and energy consumption," in 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Aachen, Germany, 2011, pp. 164-172. DOI: https://doi.org/10.1109/DYSPAN.2011.5936203
P. Venkatapathi, H. Khan, and S. Srinivasa Rao, "Performance of Threshold Detection in Cognitive Radio with Improved Otsu’s and Recursive One-Sided Hypothesis Testing Technique," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9S3, pp. 343–346, Aug. 2019. DOI: https://doi.org/10.35940/ijitee.I3063.0789S319
H. Yu, W. Tang, and S. Li, "Optimization of multiple-channel cooperative spectrum sensing with data fusion rule in cognitive radio networks," Journal of Electronics (China), vol. 29, no. 6, pp. 515–522, Nov. 2012. DOI: https://doi.org/10.1007/s11767-012-0863-2
P. Verma and B. Singh, "Simulation study of double threshold energy detection method for cognitive radios," in 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Oct. 2015, pp. 232–236. DOI: https://doi.org/10.1109/SPIN.2015.7095276
W. Xu, X. Zhou, C. -H. Lee, Z. Feng, and J. Lin, "Energy-Efficient Joint Sensing Duration, Detection Threshold, and Power Allocation Optimization in Cognitive OFDM Systems," IEEE Transactions on Wireless Communications, vol. 15, no. 12, pp. 8339-8352, Dec. 2016. DOI: https://doi.org/10.1109/TWC.2016.2613920
E. Ghazizadeh, B. Nikpour, D. A. Moghadam, and H. Nezamabadi-pour, "A PSO-based weighting method to enhance machine learning techniques for cooperative spectrum sensing in CR networks," in 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Bam, Iran, Mar. 2016, pp. 113–118. DOI: https://doi.org/10.1109/CSIEC.2016.7482127
M. A. Alsuwaiket, "Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorthims," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9247–9251, Oct. 2022. DOI: https://doi.org/10.48084/etasr.5208
H. Alamoudi et al., "Arabic Sentiment Analysis for Student Evaluation using Machine Learning and the AraBERT Transformer," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11945–11952, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6347
W. M. S. Yafooz, E. A. Hizam, and W. A. Alromema, "Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6845–6848, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4026
N. Ambati, G. Immadi, M. V. Narayana, K. R. Bareddy, M. S. Prapurna, and J. Yanapu, "Parametric Analysis of the Defected Ground Structure-Based Hairpin Band Pass Filter for VSAT System on Chip Applications," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7892–7896, Dec. 2021. DOI: https://doi.org/10.48084/etasr.4495
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Copyright (c) 2024 Pella Venkatapathi, Habibulla Khan, S. Srinivasa Rao, Govardhani Immadi
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