A Review of Miscellaneous Spectrum Sensing Algorithms in 5G Ultra-dense Networks
DOI:
https://doi.org/10.13052/jmm1550-4646.19510Keywords:
5G, densification, cognitive radio, miscellaneous spectrum sensing, network coexistence, spectrum sharing, ultra-dense networksAbstract
The continual development of advanced networks within the Fifth Generation (5G) of wireless systems, and beyond, has seen the rise of multiple important research directions. These include cognitive radio (CR) and ultra-dense networks (UDNs), which are the focus of this article. The CR systems rely on an accurate assessment of the radio environment, which is provided by the spectrum sensing functionality. A review of such algorithms that are characterized by the detection of miscellaneous features of the received signal, together with their performance comparison, is presented. In addition, the application of a simple and adequate solution is assessed through its probability of detection, for a relevant UDN system model under the critical density limitation for the access point (AP) deployment
Downloads
References
C.X. Wang, X. You, X. Gao, X. Zhu, Z. Li, C. Zhang, H. Wang, Y. Huang, Y. Chen, H. Haas, and J.S. Thompson, “On the road to 6G: Visions, requirements, key technologies and testbeds,” IEEE Communications Surveys & Tutorials, 2023.
W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, vol. 34, no. 3, pp. 134–142, 2019.
H. Tataria, M. Shafi, A.F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, “6G wireless systems: Vision, requirements, challenges, insights, and opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021.
M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6G networks: Use cases and technologies,” IEEE Communications Magazine, vol. 58, no. 3, pp. 55–61, 2020.
A. Sufyan, K.B. Khan, O.A. Khashan, T. Mir, and U. Mir, “From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies,” Electronics, vol. 12, no. 10, p. 2200, 2023.
S. Chen, F. Qin, B. Hu, X. Li, and Z. Chen, “User-centric ultra-dense networks for 5G: Challenges, methodologies, and directions,” IEEE Wireless Communications, vol. 23, no. 2, pp. 78–85, 2016.
F.H. Tseng, H.C. Chao, and J. Wang, “Ultra-dense small cell planning using cognitive radio network toward 5G,” IEEE Wireless Communications, vol. 22, no. 6, pp. 76–83, 2015.
F. Awin, E. Abdel-Raheem, and K. Tepe, “Blind spectrum sensing approaches for interweaved cognitive radio system: A tutorial and short course,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 238–259, 2018.
A. Nafkha, M. Naoues, K. Cichony, A. Kliks, and B. Aziz, “Hybrid spectrum sensing experimental analysis using gnu radio and usrp for cognitive radio,” in 2015 International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2015, pp. 506–510.
K. Srisomboon, A. Prayote, and W. Lee, “Two-stage spectrum sensing for cognitive radio under noise uncertainty,” in 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU). IEEE, 2015, pp. 19–24.
A. Bagwari and G. S. Tomar, “Two-stage detectors with multiple energy detectors and adaptive double threshold in cognitive radio networks,” International Journal of Distributed Sensor Networks, vol. 9, no. 8, p. 656495, 2013.
P. P. Anaand and C. Charan, “Two stage spectrum sensing for cognitive radio networks using ed and aic under noise uncertainty,” in 2016 international conference on recent trends in information technology (ICRTIT). IEEE, 2016, pp. 1–6.
A. R. Mohamed, A. A. A. El-Banna, and H. A. Mansour, “Multi-path hybrid spectrum sensing in cognitive radio,” Arabian Journal for Science and Engineering, pp. 1–8, 2021.
W. Ejaz, N. ul Hasan, S. Aslam, and H. S. Kim, “Fuzzy logic based spectrum sensing for cognitive radio networks,” in 2011 Fifth International Conference on Next Generation Mobile Applications, Services and Technologies. IEEE, 2011, pp. 185–189.
A. Ivanov, A. Mihovska, V. Poulkov, and R. Prasad, “Hybrid accuracy time trade-off solution for spectrum sensing in cognitive radio networks,” International Journal of Mobile Network Design and Innovation, vol. 9, no. 1, pp. 1–13, 2019.
A. Subekti, S. N. Rachmana, A. B. Suksmono et al., “A HOS based spectrum sensing for cognitive radio in noise of uncertain power,” in 2014 2nd International Conference on Information and Communication Technology (ICoICT). IEEE, 2014, pp. 511–514.
H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, no. 4, pp. 523–531, Apr. 1967.
A. Subekti, N. S. Rachmana, A. B. Suksmono et al., “A Jarque-Bera test based spectrum sensing for cognitive radio,” in 2014 8th International Conference on Telecommunication Systems Services and Applications (TSSA). IEEE, 2014, pp. 1–4.
D. Wang, N. Zhang, Z. Li, F. Gao, and X. Shen, “Leveraging high order cumulants for spectrum sensing and power recognition in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 17, no. 2, pp. 1298–1310, 2017.
S. Gurugopinath, R. Muralishankar, and H. Shankar, “Spectrum sensing in the presence of cauchy noise through differential entropy,” in 2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2016, pp. 201–204.
A. Ivanov, K. Tonchev, P. Koleva, and V. Poulkov, (2022). “Influence of Density on Throughput Performance in Cognitive Ultra-dense Networks,” Journal of Mobile Multimedia, 19(01), 29–46. https://doi.org/10.13052/jmm1550-4646.1912.
Sharma, S.K., Bogale, T.E., Chatzinotas, S., Ottersten, B., Le, L.B. and Wang, X., 2015. Cognitive radio techniques under practical imperfections: A survey. IEEE Communications Surveys & Tutorials, 17(4), pp. 1858–1884.
M.C. Park and D.S. Han, “Deep learning-based automatic modulation classification with blind OFDM parameter estimation,” IEEE Access, vol. 9, pp. 108305–108317, 2021.
L. J. Wong, W.H. Clark IV, B. Flowers, R.M. Buehrer, A.J. Michaels, and W.C. Headley, “The RFML ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications,” arXiv preprint arXiv:2010.00432, 2020.
