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The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes.
Rocznik
Tom
Strony
36--46
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
autor
- College of Information Technology and Computer Engineering, Palestine Polytechnic University
- Intelligent Systems Research Center, Palestine Polytechnic University
autor
- College of Information Technology and Computer Engineering, Palestine Polytechnic University
Bibliografia
- [1] V. Ramani and S. K. Sharma, „Cognitive radios: A survey on spectrum sensing, security and spectrum handoff", China Commun., vol. 14, no. 11, pp. 185-208, 2017 (DOI: 10.1109/CC.2017.8233660).
- [2] A. Ali and W. Hamouda, „Advances on spectrum sensing for cognitive radio networks: Theory and applications", IEEE Commun. Surveys & Tutor., vol. 19, no. 2, pp. 1277-1304, 2017 (DOI: 10.1109/COMST.2016.2631080).
- [3] Y. Arjoune and N. Kaabouch, „A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions", Sensors, vol. 19, no. 1, pp. 1277-1304, 2019 (DOI: 10.3390/s19010126).
- [4] E. Ghazizadeh, D. Abbasi-moghadam, and H. Nezamabadi-pour, „An enhanced two-phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks", Int. J. of Commun. Syst., vol. 32, no. 2, 2019 (DOI: 10.1002/dac.3856).
- [5] Y. Lu, P. Zhu, D. Wang, and M. Fattouche, „Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks", in Proc. 2016 IEEE Wirel. Commun. And Network. Conf., Doha, Qatar, 2016 (DOI: 10.1109/WCNC.2016.7564840).
- [6] W. Lee, M. Kim, and D.-H. Cho, „Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks", IEEE Trans. on Veh. Technol., vol. 68, no. 3, pp. 3005-3009, 2019 (DOI: 10.1109/TVT.2019.2891291).
- [7] M. A. Aref, S. Machuzak, S. K. Jayaweera, and S. Lane, „Replicated q-learning based sub-band selection for wideband spectrum sensing in cognitive radios", in Proc. 2016 IEEE/CIC Int. Conf. on Commun. in China ICCC 2016, Chengdu, China, 2016 (DOI: 10.1109/ICCChina.2016.7636732).
- [8] Y. Zhang, J. Zheng, and H.-H. Chen, Cognitive Radio Networks: Architectures, Protocols, and Standards. Boca Raton: CRC Press, 2016 (ISBN: 9781420077759).
- [9] Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, „Cognitive radio networking and communications: An overview", IEEE Trans. on Veh. Technol., vol. 60, no. 7, pp. 3386-3407, 2011 (DOI: 10.1109/TVT.2011.2158673).
- [10] V. Kumar, D. C. Kandpal, M. Jain, R. Gangopadhyay, and S. Debnath, „K-mean clustering based cooperative spectrum sensing in generalized k -m fading channels", in Proc. 2016 22nd National Conf. on Commun. NCC 2016, Guwahati, India, 2016 (DOI: 10.1109/NCC.2016.7561130).
- [11] G. C. Sobabe, Y. Song, X. Bai, and B. Guo, „A cooperative spectrum sensing algorithm based on unsupervised learning", in Proc. 10th Int. Congr. on Image and Sig. Process., BioMedical Engin. and Inform. CISP-BMEI 2017, Shanghai, China, 2017 (DOI: 10.1109/CISP-BMEI.2017.8302156).
- [12] C. Sun, Y. Wang, P. Wan, and Y. Du, „A cooperative spectrum sensing algorithm based on principal component analysis and k-medoids clustering", in Proc. 33rd Youth Academic Ann. Conf. of Chinese Assoc. of Autom. YAC 2018, Nanjing, China, 2018, pp. 835-839 (DOI: 10.1109/YAC.2018.8406487).
- [13] S. Zhang, Y. Wang, J. Li, P. Wan, Y. Zhang, and N. Li, „A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm", EURASIP J. on Wirel. Commun. and Network., vol. 2019, no. 1, 2019 (DOI: 10.1186/s13638-019-1338-z).
- [14] Y. Hassan, M. El-Tarhuni, and K. Assaleh, „Learning-based spectrum sensing for cognitive radio systems", J. of Comp. Netw. And Commun., vol. 2012, 2012 (DOI: 10.1155/2012/259824).
- [15] K.-j. Lei, Y.-h. Tan, X. Yang, and H.-r. Wang, „A k-means clustering based blind multiband spectrum sensing algorithm for cognitive radio", J. of Central South Univer., vol. 25, no. 10, pp. 2451-2461, 2018 (DOI: 10.1007/s11771-018-3928-z).
- [16] H. B. Ahmad, „Ensemble classiffier based spectrum sensing in cognitive radio networks", Wirel. Commun. and Mob. Comput., vol. 2019, Article ID 9250562, 2019 (DOI: 10.1155/2019/9250562).
- [17] A. Paul and S. P. Maity, „Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing", Digit. Commun. And Netw., vol. 2, no. 4, pp. 196-205, 2016 (DOI: 10.1016/j.dcan.2016.09.002).
- [18] J. Oksanen, J. Lund_en, and V. Koivunen, „Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks", Neurocomput., vol. 80, pp. 102-110, 2012 (DOI: 10.1016/j.neucom.2011.07.027).
- [19] X.-L. Huang et al., „Intelligent cooperative spectrum sensing via hierarchical dirichlet process in cognitive radio networks", IEEE J. on Selec. Areas in Commun., vol. 33, no. 5, pp. 771-787, 2015 (DOI: 10.1109/JSAC.2014.2361075).
- [20] O. P. Awe and S. Lambotharan, „Cooperative spectrum sensing in cognitive radio networks using multi-class support vector machine algorithms", in Proc. 9th Int. Conf. on Sig. Process. and Commun. Syst. ICSPCS 2015, Cairns, QLD, Australia, 2015 (DOI: 10.1109/ICSPCS.2015.7391780).
- [21] Y. Xu, P. Cheng, Z. Chen, Y. Li, and B. Vucetic, „Mobile collaborative spectrum sensing for heterogeneous networks: A Bayesian machine learning approach", IEEE Trans. on Sig. Process., vol. 66, no. 21, pp. 5634-5647, 2018 (DOI: 10.1109/TSP.2018.2870379).
- [22] A. M. Wyglinski, M. Nekovee, and T. Hou, Cognitive Radio Communications and Networks: Principles and Practice. Academic Press, 2009 (ISBN: 9780123747150).
- [23] C. Cordeiro, K. Challapali, D. Birru, and S. Shankar, „IEEE 802.22: the first worldwide wireless standard based on cognitive radios", in Proc. 1st IEEE Int. Symp. on New Front. in Dynam. Spec. Access Netw. DySPAN 2005, Baltimore, MD, USA, 2005, pp. 328-337 (DOI: 10.1109/DYSPAN.2005.1542649).
- [24] Y. Wang, Y. Zhang, P. Wan, S. Zhang, and J. Yang, „A spectrum sensing method based on empirical mode decomposition and k-means clustering algorithm", Wirel. Commun. and Mob. Comput., vol. 2018, Article ID 6104502, 2018 (DOI: 10.1155/2018/6104502).
- [25] B. Liu, Z. Li, J. Si, and F. Zhou, „Blind continuous hidden Markov model-based spectrum sensing and recognition for primary user with multiple power levels", IET Commun., vol. 9, no. 11, pp. 1396-1403, 2015 (DOI: 10.1049/iet-com.2015.0090).
- [26] M. R. Vyas, D. Patel, and M. Lopez-Benitez, „Artificial neural network based hybrid spectrum sensing scheme for cognitive radio", in Proc. IEEE 28th Ann. Int. Symp. on Pers., Indoor, and Mob. Radio Commun. PIMRC 2017, Montreal, QC, Canada, 2017 (DOI: 10.1109/PIMRC.2017.8292449).
- [27] S. Jan, V.-H. Vu, and I. Koo, „Throughput maximization using an SVM for multi-class hypothesis-based spectrum sensing in cognitive radio", Appl. Sci., vol. 8, no. 3, 2018 (DOI: 10.3390/app8030421).
- [28] O. P. Awe, A. Deligiannis, and S. Lambotharan, „Spatio-temporal spectrum sensing in cognitive radio networks using beamformer-aided SVM algorithms", IEEE Access, vol. 6, pp. 25377-25388, 2018 (DOI: 10.1109/ACCESS.2018.2825603).
- [29] T. Yucek and H. Arslan, „A survey of spectrum sensing algorithms for cognitive radio applications", IEEE Commun. Surv. & Tutor., vol. 11, no. 1, pp. 116-130, 2009 (DOI: 10.1109/SURV.2009.090109).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d3722bf0-351d-40e5-8a0b-1d14fe3442d3