Fast and Accurate Primary User Detection with Machine Learning Techniques for Cognitive Radio Networks

G.A. Pethunachiyar

Abstract


Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum , the cost for computation and difficult in finding the user in low Signal-to Noise Ratio(SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like Decision Tree, Support Vector Machines, Naive Bayes, Ensemble based Trees, Nearest Neighbour’s and Logistic Regression are used for testing the algorithm. As a first step, the spectrum sensing in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction . The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, K-Nearest Neighbour(KNN) algorithm produces the better performance in a minimized time. 


Keywords


Decision Tree;K-Nearest Neighbour;Orthogonal Frequency Division Multiplexing;Primary User;Support Vector Machines

References


Kolodzy, “Spectrum policy task force,” Rep. ET Docket 02-135, Federal Communications Commission, Washington, DC, USA, 2002.

Akyildiz MVIF, Lee WY, Mohanty S ,”Next generation dynamic spectrum access cognitive radio wireless networks: a survey”, Computer Networks Journal (Elsevier) ,2006.

Ghosh, S.K., Mehedi, J. & Samal, U.C , “Sensing performance of energy detector in cognitive radio Networks”, International journal of information Technology 11, 773–778 ,2019.

Patel, Milan & Patel, Kirtan & Patel, Sagar, “Spectrum Sensing with Energy Detection in Cognitive Radio Networks”,International Research Journal of Engineering and Technology (IRJET) ,2017.

Pandya, A. Durvesh and N. Parekh, "Energy Detection Based Spectrum Sensing for Cognitive Radio Network," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, 2015,pp.201-206.doi: 10.1109/CSNT.2015.264

M. Z. Alom, T. K. Godder, M. N. Morshed and A. Maali, "Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold," 2017 International Conference on Networking, Systems and Security (NSysS), Dhaka, 2017, pp. 138-143.

Mahmood A. Abdulsattar and Zahir A. Hussein “Energy Detection Technique For Spectrum Sensing In Cognitive Radio: a Survey”,International Journal of Computer Networks & Communications (IJCNC) Vol.4, No.5, 2012

Wang, Y. Gao and L. Cuthbert, "Spectrum sensing using adaptive threshold based energy detection for OFDM signals," 2014 IEEE International Conference on Communication Systems, Macau, 2014, pp. 359-363.

hi, Z., McLernon, D., Ghogho, M. et al.,”Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset.”,Journal of Wireless Communications Networks ,2014.

Pethunachiyar G.A & Sangaragomathi B, “An Improved Energy Detection algorithm for Sepctrum Sensing in Cogniitve Radio Networks”,International Journal of analytical and experimental modal analysis,2020

Awe, Z. Zhu, and S. Lambotharan, “Eigenvalue and Support Vector Machine Techniques for Spectrum Sensing in Cognitive Radio Networks,” in Proceedings of the Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 223–227, Taipei, Taiwan, December 2013.

Wasilewska, M., & Bogucka, H.,”Machine Learning for LTE Energy Detection Performance Improvement.”, Sensors (Basel, Switzerland), 19(19), 4348,2019

D. Zhang and X. Zhai, “SVM-Based Spectrum Sensing in Cognitive Radio,” in Proceedings of the 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–4, Wuhan, China, September 2011.

Y. J. Tang, Q. Y. Zhang, and W. Lin, “Artificial neural network based spectrum sensing method for cognitive radio,” in Proceeding of the 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–4, Chengdu, China, Sep 2010.

Feng Q, Y Zhihui and S Keqin ,”Spectrum Environment Machine Learning in Cognitive Radio”,International workshop on Information and Electronics Engineering,2012.

Hassaan Bin Ahmad ,”Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks”,Wireless Communications and Mobile Computing,2019.

H. Xue and F. Gao, “A machine learning based spectrum-sensing algorithm using sample covariance matrix,” in Proceedings of the 10th International Conference on Communications and Networking in China, Chinacom '15, pp. 476–480, Shanghai, China, August 2015.




DOI: http://doi.org/10.11591/ijeecs.v21.i1.pp%25p
Total views : 2 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics