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Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum

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dc.contributor.author Hussien, HM
dc.contributor.author Katzis, K
dc.contributor.author Mfupe, Luzango P
dc.date.accessioned 2022-08-15T07:32:12Z
dc.date.available 2022-08-15T07:32:12Z
dc.date.issued 2022-12
dc.identifier.citation Hussien, H., Katzis, K. & Mfupe, L.P. 2022. Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum. http://hdl.handle.net/10204/12468 . en_ZA
dc.identifier.isbn 978-1-6654-4231-2
dc.identifier.isbn 978-1-6654-4232-9
dc.identifier.uri DOI: 10.1109/ICECET52533.2021.9698778
dc.identifier.uri http://hdl.handle.net/10204/12468
dc.description.abstract Aiming at the problem of downlink power allocation in cognitive high-altitude platform wireless networks exploiting TV white space spectrum, it is mathematically formulated as a constrained optimization problem, and then an improved immune clonal optimization algorithm is proposed. The mathematical optimization model, algorithm realization process, and key technologies for power allocation are given, and coding, cloning, and mutation suitable operator for algorithm solving are designed. The findings of the simulation experiment indicate that, under the constraints of total transmit power, bit error rate, and interference tolerable to the main user, the algorithm can obtain a greater total data throughput, faster convergence speed, and better power allocation can be obtained. Finally, the proposed algorithm outperforms the Particle Swarm Optimization algorithm. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9698778 en_US
dc.source Proceedings of the International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 9-10 December 2021 en_US
dc.subject Cognitive HAP wireless networks en_US
dc.subject Television white spaces en_US
dc.subject TVWS en_US
dc.title Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum en_US
dc.type Conference Presentation en_US
dc.description.pages 6pp en_US
dc.description.note ©2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/9698778 en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea Spectrum Access Mgmt Innov en_US
dc.identifier.apacitation Hussien, H., Katzis, K., & Mfupe, L. P. (2022). Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum. http://hdl.handle.net/10204/12468 en_ZA
dc.identifier.chicagocitation Hussien, HM, K Katzis, and Luzango P Mfupe. "Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum." <i>Proceedings of the International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 9-10 December 2021</i> (2022): http://hdl.handle.net/10204/12468 en_ZA
dc.identifier.vancouvercitation Hussien H, Katzis K, Mfupe LP, Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum; 2022. http://hdl.handle.net/10204/12468 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Hussien, HM AU - Katzis, K AU - Mfupe, Luzango P AB - Aiming at the problem of downlink power allocation in cognitive high-altitude platform wireless networks exploiting TV white space spectrum, it is mathematically formulated as a constrained optimization problem, and then an improved immune clonal optimization algorithm is proposed. The mathematical optimization model, algorithm realization process, and key technologies for power allocation are given, and coding, cloning, and mutation suitable operator for algorithm solving are designed. The findings of the simulation experiment indicate that, under the constraints of total transmit power, bit error rate, and interference tolerable to the main user, the algorithm can obtain a greater total data throughput, faster convergence speed, and better power allocation can be obtained. Finally, the proposed algorithm outperforms the Particle Swarm Optimization algorithm. DA - 2022-12 DB - ResearchSpace DP - CSIR J1 - Proceedings of the International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 9-10 December 2021 KW - Cognitive HAP wireless networks KW - Television white spaces KW - TVWS LK - https://researchspace.csir.co.za PY - 2022 SM - 978-1-6654-4231-2 SM - 978-1-6654-4232-9 T1 - Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum TI - Intelligent power allocation for cognitive HAP wireless networks using TVWS spectrum UR - http://hdl.handle.net/10204/12468 ER - en_ZA
dc.identifier.worklist 25460 en_US


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