access icon free Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks

This study presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non-redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning-based CSS model (proposed by Karaputugala et al.) in terms of security, energy consumption, and sensing efficiency.

Inspec keywords: cognitive radio; learning (artificial intelligence); cooperative communication; telecommunication computing; support vector machines

Other keywords: SVM model; improved cooperative spectrum sensing model; optimised user group; nonredundant users; conventional machine learning-based CSS model; severely fading users; optimisation model; cognitive radio users; optimised groups; user grouping; cognitive radio networks; abnormal users; support vector machine; malicious users; sensing efficiency; sensing procedures

Subjects: Communications computing; Knowledge engineering techniques; Radio links and equipment

References

    1. 1)
      • 15. Bhatti, D., Nam, H.: ‘Spatial correlation based analysis of soft combination and user selection algorithm for cooperative spectrum sensing’, IET Commun., 2017, 11, (1), pp. 3944.
    2. 2)
      • 24. Zain, I.F., Shin, S.Y.: ‘Distributed localization for wireless sensor networks using binary particle swarm optimization (BPSO)’. Proc. IEEE Vehicular Technology Conf., Seoul, South Korea, May 2014, pp. 15.
    3. 3)
      • 11. Bkassiny, M., Li, Y., Jayaweera, K.S.: ‘A survey on machine-learning techniques in cognitive radios’, IEEE Commun. Surv. Tutor., 2013, 15, (3), pp. 11361159.
    4. 4)
      • 7. Abbas, N., Nasser, Y., Ahmad, K.E.: ‘Recent advances on artificial intelligence and learning techniques in cognitive radio networks’, EURASIP J. Wirel. Commun., 2015, 5, (1), pp. 174194.
    5. 5)
      • 12. Karaputugala, M.T., Choi, K.W., Saquib, N., et al: ‘Machine learning techniques for cooperative spectrum sensing in cognitive radio’, IEEE J. Sel. Areas Commun., 2013, 31, (11), pp. 22092221.
    6. 6)
      • 14. Akyildiz, I.F., Lo, B.F., Balakrishnan, R.: ‘Cooperative spectrum sensing in cognitive radio networks: a survey’, Phys. Commun-Amst., 2011, 4, (1), pp. 4062.
    7. 7)
      • 5. Patel, A., Ram, H., Jagannatham, A.K., et al: ‘Robust cooperative spectrum sensing for MIMO cognitive radio networks under CSI uncertainty’, IEEE Trans. Signal Process., 2018, 66, (1), pp. 1833.
    8. 8)
      • 9. Huang, Y.D., Liang, Y.C., Yang, G.: ‘A fuzzy support vector machine algorithm for cooperative spectrum sensing with noise uncertainty’. Proc. IEEE GLOBECOM, Singapore, December 2017, pp. 16.
    9. 9)
      • 1. Mitola, J.: ‘Cognitive radio for flexible mobile multimedia communications’, Mob. Netw. Appl., 2001, 6, (5), pp. 435441.
    10. 10)
      • 16. Kundargi, N., Tewfik, A.: ‘A performance study of novel sequential energy detection methods for spectrum sensing’. Proc. IEEE Int. Conf. Acoustics Speech & Signal Processing, Dallas, USA, March 2010, pp. 30903093.
    11. 11)
      • 17. Atapattu, S., Tellambura, C., Jiang, H.: ‘Energy detection based cooperative spectrum sensing in cognitive radio networks’, IEEE Trans. Wirel. Commun., 2011, 10, (4), pp. 12321241.
    12. 12)
      • 3. MacDonald, S., Popescu, D.C., Popescu, O.: ‘Analyzing the performance of spectrum sensing in cognitive radio systems with dynamic PU activity’, IEEE Commun. Lett., 2017, 21, (9), pp. 11.
    13. 13)
      • 10. He, A., Bae, K.K., Newman, T.R., et al: ‘A survey of artificial intelligence for cognitive radios’, IEEE Trans. Veh. Technol., 2010, 59, (4), pp. 15781592.
    14. 14)
      • 22. Cherkassky, V., Ma, Y.Q.: ‘Practical selection of SVM parameters and noise estimation for SVM regression’, Neural Netw., 2004, 17, (1), pp. 113126.
    15. 15)
      • 6. Cichoń, K., Kliks, A., Bogucka, H.: ‘Energy-efficient cooperative spectrum sensing: a survey’, IEEE Commun. Surv. Tutor., 2016, 18, (3), pp. 18611886.
    16. 16)
      • 18. Gahane, L., Sharma, P.: ‘Performance of improved energy detector with cognitive radio mobility and imperfect-CSI’, IET Commun., 2017, 11, (12), pp. 18571863.
    17. 17)
      • 13. Karaputugala, M.T., Choi, K.W., Saquib, N., et al: ‘Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN’. Proc. IEEE GLOBECOM, Atlanta, USA, December 2013, pp. 12601265.
    18. 18)
      • 4. Laghate, M., Cabric, D.: ‘Cooperative spectrum sensing in the presence of correlated and malicious cognitive radios’, IEEE Trans. Commun., 2015, 63, (12), pp. 46664681.
    19. 19)
      • 19. Sobron, I., Diniz, P.S., Martins, W.A., et al: ‘Energy detection technique for adaptive spectrum sensing’, IEEE Trans. Commun., 2015, 63, (3), pp. 617627.
    20. 20)
      • 21. Xie, J.Y., Wang, C.X.: ‘Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases’, Expert Syst. Appl., 2011, 38, (5), pp. 58095815.
    21. 21)
      • 8. Alshawaqfeh, M., Wang, X., Ekti, A.R., et al: ‘A survey of machine learning algorithms and their applications in cognitive radio’. Proc. Int. Conf. Cognitive Radio Oriented Wireless Network, Doha, Qatar, April 2015, pp. 790801.
    22. 22)
      • 2. Haykin, S.: ‘Cognitive radio: brain-empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 23, (2), pp. 201220.
    23. 23)
      • 20. Lu, Y.Q.: ‘Cooperative sensing algorithm and machine learning technique in cognitive radio Network’. Master thesis, University of Calgary, 2015.
    24. 24)
      • 23. El-Maleha, A.H., Sheikhb, A.T., Sadiq, M.: ‘Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits’, Appl. Soft Comput., 2013, 13, (12), pp. 48324840.
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