Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection

• Autorzy: Wasilewska Małgorzata , Bogucka Hanna

Identyfikatory

DOI

10.24425/ijet.2020.131866

• Języki publikacji: EN

Abstrakty

EN

In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbor sand Random Forest show that these methods significantly improves the detection probability.

Słowa kluczowe

EN

spectrum sensing; cognitive radio; machine learning; energy detection; 4G; LTE; 5G; k-nearest neighbors; random forest

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2020
• Tom: Vol. 66, No. 1
• Strony: 217--223
• Opis fizyczny: Bibliogr. 23 poz., wykr.

Twórcy

autor

Wasilewska Małgorzata

• Department of Wireless Communications, Poznan University of Technology, Poznan, Poland

autor

Bogucka Hanna

• Department of Wireless Communications, Poznan University of Technology, Poznan, Poland

Bibliografia

• [1] ”The Ericsson Mobility Report”, Available online: https://www.ericsson.com/en/mobility-report/reports/june-2019 (accessed on 19 August 2019).
• [2] J.I. Mitola, and G. Q. Maguire, ”Cognitive radio: making software radios more personal”, IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, Aug. 1999, doi:10.1109/98.788210
• [3] Z. Xuping, and P. Jianguo ”Energy-detection based spectrum sensing for cognitive radio”, in Proceedings of IET Conference on Wireless, Mobile and Sensor Networks, Dec. 2007, pp. 944-947, doi:10.1049/cp:20070306
• [4] X. Zhang, and F. Gao, and R. Chai, and T. Jiang ”Matched filter based spectrum sensing when primary user has multiple power levels”, China Communications, vol. 12, no. 2, pp. 21-31, Feb. 2015 doi:10.1109/CC.2015.7084399
• [5] K. Kim, and I. A. Akbar, and K. K. Bae, and J. Um, and C. M. Spooner, and J. H. Reed, ”Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio”, in Proceedings of 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Apr. 2007, pp. 212-215, doi:10.1109/DYSPAN.2007.35
• [6] S. M. Mishra, and S. ten Brink, and R. Mahadevappa, and R.W. Brodersen ”Cognitive Technology for Ultra-Wideband/WiMax Coexistence”, in Proceedings of 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Apr. 2007, pp. 179-186, doi:10.1109/DYSPAN.2007.30
• [7] H. Li, and J. Li, ”Wavelet transforms detection of spectrum sensing in the space network”, in Proceedings of 2015 Science and Information Conference (SAI), Jul. 2015, pp. 978-984, doi:10.1109/SAI.2015.7237261
• [8] A. Mariani, and A. Giorgetti, and M. Chiani, ”Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications”, IEEE Transactions on Communications, vol. 59, no. 12, pp. 3410-3420, Dec. 2011. doi:10.1109/TCOMM.2011.102011.100708
• [9] Y. Xu, and P. Cheng, and Z. Chen, and Y. Li, and B. Vucetic, ”Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach”, IEEE Transactions on Signal Processing, vol. 66, no. 21, pp. 5634-5647, Sept. 2018. doi:10.1109/TSP.2018.2870379
• [10] K. M. Thilina, and K. W. Choi, and N. Saquib, and E. Hossain, ”Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN approaches”, in Proceedings of 2012 IEEE Global Communications Conference (GLOBECOM), Dec. 2012, pp. 1260-1265. doi:10.1109/GLOCOM.2012.6503286
• [11] Z. Li, and W. Wu, and X. Liu, and P. Qi, ”Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks”, IET Communications, vol. 12, no. 19, pp. 2485-2492, Nov. 2018. doi:10.1049/iet-com.2018.5245
• [12] K. Zhang, and J. Li, and F. Gao, ”Machine learning techniques for spectrum sensing when primary user has multiple transmit powers”, in Proceedings of 2014 IEEE International Conference on Communication Systems, Nov. 2014, pp. 137-141, doi:10.1109/ICCS.2014.7024781
• [13] Y. Lu, and P. Zhu, and D. Wang, and M. Fattouche, ”Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks”, in Proceedings of 2016 IEEE Wireless Communications and Networking Conference, Apr. 2016, pp. 1-6, doi:10.1109/WCNC.2016.7564840
• [14] H. Xue, and F. Gao, ”A machine learning based spectrum-sensing algorithm using sample covariance matrix”, in Proceedings of 2015 10th International Conference on Communications and Networking in China (ChinaCom), Aug. 2015, pp. 476-480, doi:10.1109/CHINACOM.2015.7497987
• [15] M. Wasilewska, and H. Bogucka, ”Machine Learning for LTE Energy Detection Performance Improvement”, Sensors 19.19 (2019): 4348.
• [16] K. Cichoń, and A. Kliks, and H. Bogucka, ”Energy-Efficient Cooperative Spectrum Sensing: A Survey”, IEEE Communications Surveys Tutorials, vol. 18, no. 3, pp. 1861-1886, Apr. 2016 doi:10.1109/COMST.2016.2553178
• [17] K. Kim, and Y. Xin, and S. Rangarajan, ”Energy Detection Based Spectrum Sensing for Cognitive Radio: An Experimental Study”, in Proceedings of 2010 IEEE Global Telecommunications Conference GLOBECOM, Dec. 2010, pp. 1-5, doi:10.1109/GLOCOM.2010.5683560
• [18] S. Shalev-Shwartz, and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms Cambridge University Press: New York, NY, USA, 2014, pp. 258–259
• [19] T. Cover, and P. Hart, ”Nearest neighbor pattern classification”, IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967, doi:10.1109/TIT.1967.1053964
• [20] A. C. Muller, and S. Guido, S. Introduction to Machine Learning with Python: A Guide for Data Scientists, O’ReillyMedia: Sebastopol, CA, USA, 2016, pp. 85-90
• [21] L. Song, and J. Shen, Evolved Cellular Network Planning and Optimization for UMTS and LTE CRC Press: BocaRaton, FL, USA, 2010, pp. 56–58
• [22] Agilent Technologies, “Advanced Design System—LTE Channel Model—R4-070872 3GPP TR 36.803 v0.3.0”, 2008.
• [23] F. Pedregosa, and G. Varoquaux, and A. Gramfort, and V. Michel, and B. Thirion, and O. Grisel, and M. Blondel, and P. Prettenhofer, and R. Weiss, and V. Dubourg, et al. ”Scikit-learn: Machine Learning in Python” Journal of Machine Learning Research 12, pp. 2825–2830, 2011