Adaptive based machine learning approach for cooperative energy detection in cognitive radio networks

Rania A. Mokhtar

doi:10.2478/jee-2025-0011

References

F. K. Jondral (2007). “Cognitive Radio: A Communications Engineering View.” In IEEE Wireless Communications, vol. 14, no. 4, pp. 28-33. doi: 10.1109/MWC.2007.4300980.
Search in Google Scholar Back to article
S. Filin, H. Murakami, H. Harada, H. Yoshino, K. Kashiki, & T. Shibata (2011). “ITU-R standardization activities on Cognitive Radio Systems.” In 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Yokohama, Japan, 2011, pp. 116-120. doi: 10.4108/icst.crowncom.2011.245828.
Search in Google Scholar Back to article
M. U. Muzaffar & R. Sharqi (2024). A review of spectrum sensing in modern cognitive radio networks. Telecommun Syst, 85, 347–363. https://doi.org/10.1007/s11235-023-01079-1.
Search in Google Scholar Back to article
J. Luo, G. Zhang, & C. Yan (2022). “An Energy Detection-Based Spectrum-Sensing Method for Cognitive Radio.” Wireless Commu. & Mobile Comp. https://doi.org/10.1155/2022/3933336.
Search in Google Scholar Back to article
Y. C. Liang (2020). Spectrum Sensing Theories and Methods. In E. Hossain & V. Bhargava (Eds.), Dynamic Spectrum Management. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-0776-2_3.
Search in Google Scholar Back to article
K. B. Letaief & W. Zhang (2007). Cooperative Spectrum Sensing. In E. Hossain & V. Bhargava (Eds.), Cognitive Wireless Communication Networks. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-68832-9_4.
Search in Google Scholar Back to article
R.A. Mokhtar, R.A. Saeed, H. Alhumyani, et al. Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing. Cluster Comput 25, 2541–2556 (2022). https://doi.org/10.1007/s10586-021-03363-8
Search in Google Scholar Back to article
R.A. Mokhtar, R.A. Saeed, H. Alhumyani, Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel. Wireless Pers Commun 124, 839–865 (2022). https://doi.org/10.1007/s11277-021-09386-z
Search in Google Scholar Back to article
R. Mokhtar, N. Noordin, B. Ali, A. Ramli, & Y. Abdalla (2010). “Cooperative Spectrum Sensing with Distributed Detection Threshold.” In IEEE Second International Conference on Network Applications, Protocols and Services, Alor Setar, Malaysia, 2010, pp. 176-181. doi: 10.1109/NETAPPS.2010.38.
Search in Google Scholar Back to article
Y. Li, Y. Zhang, & Y. Liu (2020). “Spectrum sensing and allocation in cognitive networks: a survey.” IEEE Communications Surveys & Tutorials, 22(2), 955-986.
Search in Google Scholar Back to article
J. Zhao, K. Hwang, & C. Walton (2019). “Energy detection-based spectrum sensing in cognitive radio: A comprehensive review.” IEEE Access, 7, 59919-59932.
Search in Google Scholar Back to article
H. Gao, Q. Li, & K. Wong (2021). “Combining matched filtering and energy detection for spectrum sensing in cognitive networks.” IEEE Transactions on Wireless Communications, 20(5), 3172-3185.
Search in Google Scholar Back to article
K. Kumar, S. Singh, & N. Gupta (2021). “Comparative analysis of spectrum sensing techniques in cognitive radio.” Journal of Wireless Communications and Networking, 2021(1), 354.
Search in Google Scholar Back to article
M. M. Saeed et al., “Enhancing Energy Efficiency in UAV Cognitive Radio Networks: A Machine Learning-Based Optimization Approach,” 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI), Sana’a, Yemen, 2024, pp. 1-5, doi: 10.1109/ICETI63946.2024.10777273.
Search in Google Scholar Back to article
X. Li, K. He, & H. Zheng (2020). “Review of energy detection spectrum sensing schemes for cognitive radio networks.” Wireless Networks, 26(3), 1783-1798.
Search in Google Scholar Back to article
T. Yucek & H. Arslan (2009). “A survey of spectrum sensing algorithms for cognitive radio applications.” IEEE Communications Surveys & Tutorials, 11(1), 116-130.
Search in Google Scholar Back to article
R. Mokhtar, M. Bouchabou, & T. Khattab (2019). “Improving cooperative spectrum sensing in cognitive radio networks using distributed decision algorithms.” IEEE Transactions on Cognitive Communications and Networking, 5(4), 1011-1022.
Search in Google Scholar Back to article
R. Mokhtar & A. Moustafa (2020). “An overview of recent advancements in spectrum sensing techniques in cognitive radio networks.” Current Wireless Communications, 4(1), 23-35.
Search in Google Scholar Back to article
U. Aydin, M. R. Dhibar, & R. Mokhtar (2022). “Cooperative spectrum sensing with hard and soft decision fusion techniques: A comprehensive survey.” Wireless Communications and Mobile Computing, 2022, 1-20.
Search in Google Scholar Back to article
M. Naderpour, O. Erdinç, & E. E. Kuruoglu (2018). “A survey on cooperative spectrum sensing: Requirements and challenges.” IEEE Communications Surveys & Tutorials, 20(3), 2275-2298.
Search in Google Scholar Back to article
K. Gharbaoui, M. Bouchabou, & R. Mokhtar (2023). “Distributed detection algorithms for effective spectrum sensing in cooperative cognitive radio networks.” Journal of Communications and Networks, 25(1), 25-35.
Search in Google Scholar Back to article
M. A. Ali, W. Ahmed, & R. Mokhtar (2023). “Efficient fusion strategies for cooperative spectrum sensing in cognitive radio networks.” Journal of Network and Computer Applications, 223, 103570.
Search in Google Scholar Back to article
Z. Ding & G. Wang (2020). “Cooperative spectrum sensing in cognitive radio networks: A survey.” IEEE Access, 8, 198901-198917.
Search in Google Scholar Back to article
A. Pathak, R. K. Yadav, & P. K. Shrivastava (2022). “Machine learning approaches for spectrum sensing in cognitive radio: A review.” Wireless Communications and Mobile Computing, 2022, 1-18.
Search in Google Scholar Back to article
E. Bhoj & S. Manohar (2021). “A deep learning approach for spectrum sensing in cognitive radio networks.” IEEE Transactions on Cognitive Communications and Networking, 7(4), 1381-1391.
Search in Google Scholar Back to article
Y. Pan, W. Wang, & L. Zhang (2023). “Machine learning for spectrum sensing in cognitive radio: A review.” IEEE Communications Surveys & Tutorials, 25(1), 436-460.
Search in Google Scholar Back to article
R. Mokhtar, L. Khatib, & A. Youssri (2022). “Adaptive spectrum sensing using machine learning algorithms in cognitive radio networks.” Future Generation Computer Systems, 130, 505-514.
Search in Google Scholar Back to article
A. Alghamdi, M. Abukhashim, & M. Adnan (2021). “A comparative analysis of machine learning techniques for spectrum sensing in cognitive radio networks.” IEEE Access, 9, 40514-40525.
Search in Google Scholar Back to article
M. M. Saeed, R. A. Saeed, E. S. Ali, R. A. Mokhtar and O. O. Khalifa, “Algorithm for Resource Allocation and Computing Offloading in 6G Networks: Deep Reinforcement Learning-based,” 2024 9th International Conference on Mechatronics Engineering (ICOM), Kuala Lumpur, Malaysia, 2024, pp. 188-193, doi: 10.1109/ICOM61675.2024.10652281.
Search in Google Scholar Back to article
M. Hassan et al., “NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network,” 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Benghazi, Libya, 2023, pp. 653-658, doi: 10.1109/MI-STA57575.2023.10169694.
Search in Google Scholar Back to article
M. M. Saeed, R. A. Mokhtar, M. S. Elbasheir, E. Sayed Ali, Z. E. Ahmed and M. A. Ahmed, “Efficient Deep Post-Decision State Learning for Privacy-Conscious Offloading in MEC-Enabled 6G Networks,” 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI), Sana’a, Yemen, 2024, pp. 1-7, doi: 10.1109/ICETI63946.2024.10777183.
Search in Google Scholar Back to article

Adaptive based machine learning approach for cooperative energy detection in cognitive radio networks

References

Paradigm

My account