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Mobile Primary User in Cognitive Radio State of the Arts and Recent Advances

, and . International Journal of Next-Generation Networks (IJNGN), 12, No., 2020 (1/2/3): 01 - 14 (September 2020)
DOI: 10.5121/ijngn.2020.12301

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On applying firefly algorithm for cognitive radio networks., , and . SCVT, page 75-79. IEEE, (2014)Task Scheduling Optimization in Cloud Computing: A Comparative Study Between Flower Pollination and Butterfly Optimization Algorithms., , and . PAIS, page 1-7. IEEE, (2023)Efficient Branch-and-Bound Algorithm for Task Scheduling in Cloud Computing., and . ACIT, page 440-443. IEEE, (2023)Recent Advances on Artificial Intelligence in Cognitive Radio Networks.. Int. J. Wirel. Networks Broadband Technol., 9 (1): 27-42 (2020)Quality of service optimization in OFDM-based cognitive radio network., , , and . SSD, page 1731-1736. IEEE, (2022)Optimization of a Multi-criteria Cognitive Radio User Through Autonomous Learning., , , and . NISS (Springer), volume 237 of Smart Innovation, Systems and Technologies, page 117-132. Springer, (2021)Quality of Service Optimization for Green Cognitive Radio Network: a Comparative Study of Flower Pollination and AHP-TOPSIS Algorithm., , and . ACIT, page 600-603. IEEE, (2023)Towards a Three Layer Architecture for Dynamic Spectrum Access in Cognitive Radio Networks., and . ArabWIC, page 6:1-6:6. ACM, (2019)Using cognitive radio to deliver green communications: a reinforcement learning approach., , and . Int. J. Knowl. Eng. Data Min., 6 (4): 331-355 (2019)Distributed, dynamic and trustworthy access control for telehealth systems., , and . Concurr. Comput. Pract. Exp., (2022)