177
Views
10
CrossRef citations to date
0
Altmetric
Article

Cooperative Spectrum Sensing Using Extreme Learning Machines for Cognitive Radio Networks

ORCID Icon & ORCID Icon

References

  • Y. Chen, and H.-S. Oh, “A survey of measurement-based spectrum occupancy modeling for cognitive radios,” IEEE Commun. Surv. Tutorials, Vol. 18, no. 1, pp. 848–59, Oct 2014.
  • J. Mitola, and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., Vol. 6, no. 4, pp. 13–8, Aug 1999.
  • F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: a survey,” Phys. Commun., Vol. 4, no. 1, pp. 40–62, Mar. 2011.
  • W. Zhang, R. K. Mallik, and K. B. Letaief, “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE Trans. Wireless Commun., Vol. 8, no. 12, pp. 5761–6, Dec 2009.
  • G. Sharma, and R. Sharma, “Cluster-based distributed cooperative spectrum sensing over nakagami fading using diversity reception,” IET Networks, Vol. 8, no. 3, pp. 211–7, Jan 2019.
  • B. Shen, and K. S. Kwak, “Soft combination schemes for cooperative spectrum sensing in cognitive radio networks,” ETRI J., Vol. 31, no. 3, pp. 263–70, Jun 2009.
  • Y. Zeng, and Y. C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Trans. Commun., Vol. 57, no. 6, pp. 1784–93, Jun 2009.
  • R. Sarikhani, and F. Keynia, “Cooperative spectrum sensing meets Machine learning: Deep Reinforcement Learning approach,” IEEE Commun. Lett., Vol. 24, no. 7, pp. 1459–62, July 2020. Doi:https://doi.org/10.1109/LCOMM.2020.2984430.
  • W. Ning, X. Huang, K. Yang, F. Wu, and S. Leng, “Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks,” J. Commun. Netw., Vol. 22, no. 1, pp. 12–22, Feb. 2020. Doi:https://doi.org/10.1109/JCN.2019.000052.
  • Z. Shi, W. Gao, S. Zhang, J. Liu, and N. Kato, “Machine Learning-Enabled cooperative spectrum sensing for Non-orthogonal multiple access,” IEEE Trans. Wireless Commun., Vol. 19, no. 9, pp. 5692–702, Sept. 2020. Doi:https://doi.org/10.1109/TWC.2020.2995594.
  • P. Zhu, J. Li, D. Wang, and X. You, “Machine-Learning-Based opportunistic spectrum Access in cognitive radio networks,” IEEE Wirel. Commun., Vol. 27, no. 1, pp. 38–44, February 2020. Doi:https://doi.org/10.1109/MWC.001.1900234.
  • B. Soni, D. K. Patel, and M. López-Benítez, “Long Short-Term memory based spectrum sensing scheme for cognitive radio using primary Activity statistics,” IEEE. Access., Vol. 8, pp. 97437–51, 2020. Doi:https://doi.org/10.1109/ACCESS.2020.2995633.
  • J. Ghosh, “Energy efficiency Analysis by Game-Theoretic approach in the next generation network,” IETE Tech. Rev., Vol. 37, no. 4, pp. 329–38, 2020. Doi:https://doi.org/10.1080/02564602.2019.1620139.
  • G. Prieto, Á. G. Andrade, and D. M. Martínez, “Numerical Analysis of histogram-based estimation techniques for entropy-based spectrum sensing,” IETE Tech. Rev., Vol. 37, no. 1, pp. 91–7, 2020. Doi:https://doi.org/10.1080/02564602.2019.1566029.
  • S. Sabat, P. K. Sharma, and A. Gandhi, “Full-Duplex cooperative spectrum sensing with primary user Activity in cognitive radio networks,” IETE Tech. Rev., Vol. 34, no. sup 1, pp. 4–14, 2017. Doi:https://doi.org/10.1080/02564602.2017.1396937.
  • M. S. Miah, M. Schukat, and E. Barrett, “Sensing and throughput analysis of a MU-MIMO based cognitive radio scheme for the internet of things,” Comput. Commun., Vol. 154, pp. 442–54, 2020.
  • B. Nadler, F. Penna, and R. Garello, “Performance of eigenvalue-based signal detectors with known and unknown noise level,” in Proceedings IEEE International Conference on Communications (ICC), 2011, pp. 1–5.
  • D. Ramirez, G. Vazquez-Vilar, R. Lopez-Valcarce, J. Via, and I. Santamaria, “Detection of rank-P signals in cognitive radio networks with uncalibrated multiple antennas,” IEEE Trans. Signal Process, Vol. 59, no. 8, pp. 3764–74, Aug. 2011.
  • L. Huang, Y. Xiao, H. C. So, and J. Fang, “Accurate performance analysis of Hadamard ratio test for robust spectrum sensing,” IEEE Trans. Wireless Commun., Vol. 14, no. 2, pp. 750–8, Feb. 2015.
  • L. Huang, H. C. So, and C. Qian, “Volume-based method for spectrum sensing,” Digit. Signal. Process., Vol. 28, pp. 48–56, May 2014.
  • R. Li, L. Huang, Y. Shi, and H. C. So, “Gerschgorin disk-based robust spectrum sensing for cognitive radio,” in Proccedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 7278–82.
  • Dayan adionel Guimarães, “Robust test statistic for cooperative spectrum sensing based on the Gerschgorin circle theorem,” IEEE. Access., Vol. 6, pp. 2445–56, Dec 2017.
  • Dayan adionel Guimarães, “Gini index inspired robust detector for spectrum sensing over ricean channels,” Electron. Lett., Vol. 55, no. 12, pp. 713–4, Dec 2018.
  • K. M. Thilina, K. W. Choi, N. Saquib, and E. Hossain, “Machine learning techniques for cooperative spectrum sensing in cognitive radio networks,” IEEE J. Sel. Areas Commun., Vol. 31, no. 11, pp. 2209–21, Oct 2013.
  • F. Azmat, Y. Chen, and N. Stocks, “Analysis of spectrum occupancy using machine learning algorithms,” IEEE Trans. Veh. Technol., Vol. 65, no. 9, pp. 6853–60, Oct 2015.
  • C. H. A. Tavares, and T. Abrão, “Bayesian estimators for cooperative spectrum sensing in cognitive radio networks,” in Proccedings IEEE URUCON, 2017, pp. 1–4.
  • C. H. A. Tavares, J. C. Marinello, M. L. Proenca Jr, and T. Abrao, “Machine learning-based models for spectrum sensing in cooperative radio networks,” IET Commun., Vol. 14, no. 18, pp. 3102–3109, Mar 2020. Doi:https://doi.org/10.1049/iet-com.2019.0941.
  • M. Wasilewska, and H. Bogucka, “Machine learning for LTE energy detection performance improvement,” Sensors, Vol. 19, no. 19, pp. 1–19, Oct 2019. Doi:https://doi.org/10.3390/s19194348.
  • J. Bao, J. Nie, C. Liu, B. Jiang, F. Zhu, and J. He, “Improved blind spectrum sensing by covariance matrix cholesky decomposition and RBF-SVM decision classification at low SNRs,” IEEE. Access., Vol. 7, pp. 97117–29, Jul 2019.
  • A. Paul, and S. P. Maity, “Outage Analysis in cognitive radio networks With energy harvesting and Q-routing,” IEEE Trans. Veh. Technol., Vol. 69, no. 6, pp. 6755–65, June 2020. Doi:https://doi.org/10.1109/TVT.2020.2987751.
  • A. Paul, and S. P. Maity, “Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing,” Digital Commun. Networks, Vol. 2, no. 4, pp. 196–205, 2016.
  • A. Paul, A. Banerjee, and S. P. Maity, “Residual energy maximization in cognitive radio networks With Q-routing,” IEEE Syst. J., Vol. 14, no. 3, pp. 3791–800, Sept. 2020. Doi:https://doi.org/10.1109/JSYST.2019.2926120.
  • A. Paul, P. Kunarapu, A. Banerjee, and S. P. Maity, “Spectrum sensing in cognitive vehicular networks for uniform mobility model,” IET Commun., Vol. 13, no. 19, pp. 3127–34, 2019.
  • M. K. Giri, and S. Majumder, “Extreme learning Machine based cooperative spectrum sensing in cognitive radio networks,” in Proccedings 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020, pp. 636–41.
  • E. Alpaydin, Introduction to machine learning. Cambridge, MA: MIT Press, 2020.
  • S. Haykin, Neural networks and learning machines. Noida, Uttar Pradesh: 3/E. Pearson Education India, 2010.
  • G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, Vol. 70, no. 1-3, pp. 489–501, Dec 2006.
  • L. K. Hansen, and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, no. 10, pp. 993–1001, Oct. 1990.
  • X. Ma, S. Ning, X. Liu, H. Kuang, and Y. Hong, “Cooperative spectrum sensing using extreme learning machine for cognitive radio networks with multiple primary users,” in Proceedings 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018, pp. 536–40.
  • L. Yang, N. Lv, and Z. X. Xu, “Spectrum prediction for cognitive radio system based on optimally pruned extreme learning machine,” in Applied mechanics and materials, Vol. 536, Trans Tech Publications Ltd, 2014, pp. 430-6.
  • S. Atapattu, C. Tellambura, and H. Jiang, “Energy detection based cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., Vol. 10, no. 4, pp. 1232–41, Jan 2011.
  • D. Teguig, B. Scheers, and V. Le Nir, “Data fusion schemes for cooperative spectrum sensing in cognitive radio networks,” in Proceedings 2012 military Communications and information Systems Conference (MCC), IEEE, pp. 1–7.
  • E. Axell, G. Leus, E. G. Larsson, and H. V. Poor, “Spectrum sensing for cognitive radio: state-of-the-art and recent advances,” IEEE Signal Process Mag., Vol. 29, no. 3, pp. 101–16, Apr 2012.
  • J. Ma, G. Zhao, and Y. Li, “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., Vol. 7, no. 11, pp. 4502–7, Dec 2008.
  • G. B. Huang, D. Wang, and Y. Lan, “Extreme learning machines: a survey,” Int. J. Mach. Learn. Cybern., Vol. 2, no. 2, pp. 107–22, Jun 2011.
  • G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Vol. 2, pp. 985–90.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.