UAV-Assisted Cooperative & Cognitive NOMA: Deployment, Clustering, and Resource Allocation

Sultangali Arzykulov, Abdulkadir Celik, Galymzhan Nauryzbayev, Ahmed Eltawil

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been recognized as a promising technique to overcome spectrum scarcity and massive connectivity issues envisioned in next-generation wireless networks. This paper investigates the deployment of an unmanned aerial vehicle (UAV) as a relay that fairly serves many secondary users in a hot-spot region. The UAV deployment algorithm must jointly account for user clustering, channel assignment, and resource allocation sub-problems. We propose a solution methodology that obtains user clustering and channel assignment based on the optimal resource allocations for a given UAV location. This paper is the first to jointly derive closed-form optimal power and time allocations for generic cluster sizes of CCR-NOMA networks. Derivations consider many practical limitations, such as hardware impairments, imperfect channel estimates, and interference temperature constraints. Compared to numerical benchmarks, proposed solutions reach optimal max-min fair data rate by consuming and spending much less transmission power and computational time. The proposed clustering uses the optimal data rates and channel assignment approaches based on a linear bottleneck assignment (LBA) algorithm. Numerical results show that the LBA achieves 100% accuracy in more than five orders of magnitude less time than the optimal integer linear programming benchmark.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Cognitive Communications and Networking
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-08-19
Acknowledgements: The authors gratefully acknowledge partial funding from King Abdullah University of Science and Technology. This work was also supported by the Nazarbayev University Faculty Development Competitive Research Program under Grant no. 240919FD3935.


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