dc.contributor.author |
Hussien, HM
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dc.contributor.author |
Katzis, K
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dc.contributor.author |
Mfupe, Luzango P
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dc.date.accessioned |
2022-08-15T07:32:12Z |
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dc.date.available |
2022-08-15T07:32:12Z |
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dc.date.issued |
2022-12 |
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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 |
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dc.identifier.isbn |
978-1-6654-4232-9 |
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dc.identifier.uri |
DOI: 10.1109/ICECET52533.2021.9698778
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dc.identifier.uri |
http://hdl.handle.net/10204/12468
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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 -
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en_ZA |
dc.identifier.worklist |
25460 |
en_US |