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Recently, Cooperative Spectrum Sensing (CSS) for Cognitive Radio Networks (CRN) plays a significant role in efficient 5G wireless communication. Spectrum sensing is a significant technology in CRN to identify underutilized spectrums. The CSS technique is highly applicable due to its fast and efficient performance. 5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things (IoT) networks. 5G wireless communication will potentially lead the way for next generation IoT communication. CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT. In this paper, an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access (OQAM/UFMC/NOMA) methodologies. Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication. The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS, low latency, Signal Noise Ratio (SNR) improvement, maximum capacity, offset synchronization, and Peak Average Power Ratio (PAPR) reduction. The Energy Efficient All-Pass Filter (EEAPF) algorithm is used to eliminate PAPR. The deployment approach improves Quality of Service (QoS) in terms of system reliability, throughput, and energy efficiency. Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.
A. Nasser, H. Al Haj Hassan, J. Abou Chaaya, A. Mansour, and K. C. Yao, Spectrum sensing for cognitive radio: Recent advances and future challenge, Sensors, vol. 21, no. 7, p. 2408, 2021.
H. Wu, D. Mo, and H. Li, Analysis and simulation of the dynamic spectrum allocation based on parallel immune optimization in cognitive wireless networks, Sci. World J., vol. 2014, p. 623670, 2014.
W. Lee, M. Kim, and D. H. Cho, Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks, IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 3005–3009, 2019.
J. Feng, M. Zhang, Y. Xiao, and H. Yue, Securing cooperative spectrum sensing against collusive SSDF attack using XOR distance analysis in cognitive radio networks, Sensors, vol. 18, p. 370, 2018.
F. Awin, N. Salout, and E. Abdel-Raheem, Combined fusion rules in cognitive radio networks using different threshold strategies, Appl. Sci., vol. 9, no. 23, p. 5080, 2019.
M. Liu, L. Liu, H. Song, Y. Hu, Y. Yi, and F. Gong, Signal estimation in underlay cognitive networks for industrial Internet of Things, IEEE Trans. Ind. Inform., vol. 16, no. 8, pp. 5478–5488, 2020.
M. A. Hossain, R. Md Noor, K. L. A. Yau, S. R. Azzuhri, M. R. Z’aba, I. Ahmedy, and M. R. Jabbarpour, Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network, Energies, vol. 14, no. 4, p. 1169, 2021.
M. Ali and H. Nam, Optimization of spectrum utilization in cooperative spectrum sensing, Sensors, vol. 19, no. 8, p. 1922, 2019.
I. Kakalou and K. E. Psannis, Sustainable and efficient data collection in cognitive radio sensor networks, IEEE Trans. Sustain. Comput., vol. 4, no. 1, pp. 29–38, 2019.
D. S. Gurjar, H. H. Nguyen, and P. Pattanayak, Performance of wireless powered cognitive radio sensor networks with nonlinear energy harvester, IEEE Sens. Lett., vol. 3, no. 8, pp. 1–4, 2019.
B. Lyu, H. Guo, Z. Yang, and G. Gui, Throughput maximization for hybrid backscatter assisted cognitive wireless powered radio networks, IEEE Internet Things J., vol. 5, no. 3, pp. 2015–2024, 2018.
Z. Zhang, X. Wen, H. Xu, and L. Yuan, Sensing nodes selective fusion scheme of spectrum sensing in spectrum-heterogeneous cognitive wireless sensor networks, IEEE Sens. J., vol. 18, no. 1, pp. 436–445, 2018.
J. Ren, Y. Zhang, R. Deng, N. Zhang, D. Zhang, and X. Shen, Joint channel access and sampling rate control in energy harvesting cognitive radio sensor networks, IEEE Trans. Emerg. Top. Comput., vol. 7, no. 1, pp. 149–161, 2019.
T. Van Nguyen, T. N. Do, V. N. Q. Bao, D. B. da Costa, and B. An, On the performance of multihop cognitive wireless powered D2D communications in WSNs, IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 2684–2699, 2020.
A. Bagheri and A. Ebrahimzadeh, Statistical analysis of lifetime in wireless cognitive sensor network for multi-channel cooperative spectrum sensing, IEEE Sens. J., vol. 21, no. 2, pp. 2412–2421, 2021.
M. Liu, G. Liao, N. Zhao, H. Song, and F. Gong, Data-driven deep learning for signal classification in industrial cognitive radio networks, IEEE Trans. Ind. Inform., vol. 17, no. 5, pp. 3412–3421, 2021.
M. Shafiee and V. T. Vakili, Comparative evaluation approach for spectrum sensing in cognitive wireless sensor networks (C-WSNs), Can. J. Electr. Comput. Eng., vol. 41, no. 2, pp. 77–86, 2018.
D. Wang and Y. Song, ECCO: A novel end-to-end congestion control scheme in multi-hop cognitive radio ad hoc networks, IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 1, pp. 93–102, 2019.
Y. Aborahama and M. S. Hassan, On the stochastic modeling of the holding time of SUs to PU channels in cognitive radio networks, IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 1, pp. 282–295, 2020.
F. Li, K. Y. Lam, X. Li, Z. Sheng, J. Hua, and L. Wang, Advances and emerging challenges in cognitive internet-of-things, IEEE Trans. Ind. Inform., vol. 16, no. 8, pp. 5489–5496, 2020.
R. A. Osman and A. I. Zaki, Energy-efficient and reliable Internet of Things for 5G: A framework for interference control, Electronics, vol. 9, no. 12, p. 2165, 2020.
M. Baniata, H. T. Reda, N. Chilamkurti, and A. Abuadbba, Energy-efficient hybrid routing protocol for IoT communication systems in 5G and beyond, Sensors, vol. 21, no. 2, p. 537, 2021.
M. S. Al-kahtani, L. Ferdouse, and L. Karim, Energy efficient power domain non-orthogonal multiple access based cellular device-to-device communication for 5G networks, Electronics, vol. 9, no. 2, p. 237, 2020.
P. K. Sahoo, S. Mohapatra, and J. P. Sheu, Dynamic spectrum allocation algorithms for industrial cognitive radio networks, IEEE Trans. Ind. Inform., vol. 14, no. 7, pp. 3031–3043, 2018.
G. A. Gallardo, G. Jakllari, L. Canourgues, and A. L. Beylot, Statistical admission control in multi-hop cognitive radio networks, IEEE/ACM Trans. Netw., vol. 26, no. 3, pp. 1390–1403, 2018.
A. Patel, H. Ram, A. K. Jagannatham, and P. K. Varshney, Robust cooperative spectrum sensing for MIMO cognitive radio networks under CSI uncertainty, IEEE Trans. Signal Process., vol. 66, no. 1, pp. 18–33, 2018.
L. Xu, L. Cai, Y. Gao, J. Xia, Y. Yang, and T. Chai, Security-aware proportional fairness resource allocation for cognitive heterogeneous networks, IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 11694–11704, 2018.
T. Balachander and M. B. M. Krishnan, Carrier frequency offset (CFO) synchronization and peak average power ratio (PAPR) minimization for energy efficient cognitive radio network (CRN) for 5G wireless communication, Wirel. Pers. Commun., vol. 127, no. 3, pp. 1847–1867, 2022.
S. Wu, S. Shen, X. Xu, Y. Chen, X. Zhou, D. Liu, X. Xue, and L. Qi, Popularity-aware and diverse web APIs recommendation based on correlation graph, IEEE Trans. Comput. Soc. Syst., vol. 10, no. 2, pp. 771–782, 2023.
L. Qi, Y. Yang, X. Zhou, W. Rafique, and J. Ma, Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0, IEEE Trans. Ind. Inform., vol. 18, no. 9, pp. 6503–6511, 2022.
C. Yang, X. Xu, X. Zhou, and L. Qi, Deep Q network-driven task offloading for efficient multimedia data analysis in edge computing-assisted IoV, ACM Trans. Multimedia Comput. Commun. Appl., vol. 18, no. 2, pp. 1–24, 2022.
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