A Cat Swarm Optimization based transmission power minimization for an aerial NOMA communication system
This article underlines the inclusion of Non-Orthogonal Multiple Access (NOMA) aerial network nodes to rapidly serve a mass deployment of devices in the next-generation networks. The analysis of an aerial NOMA deployment is conducted considering the objective of minimization of the required transmis...
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| Main Authors: | , , |
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| Format: | Article |
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Elsevier Inc.
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/104700/ http://dx.doi.org/10.1016/j.vehcom.2021.100426 |
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| Summary: | This article underlines the inclusion of Non-Orthogonal Multiple Access (NOMA) aerial network nodes to rapidly serve a mass deployment of devices in the next-generation networks. The analysis of an aerial NOMA deployment is conducted considering the objective of minimization of the required transmission power in comparison to an aerial deployment employing Orthogonal Multiple Access (OMA). The findings of the study highlight the inter-dependency among the considered optimization variables of user pairing, altitude, and power allocation as well as stress the implication of their joint optimization for improved performance of the aerial NOMA system. The formulated mixed-integer non-linear programming problem is solved utilizing a joint optimization technique. Meanwhile, the employment of Cat Swarm Optimization (CSO) framework for NOMA user pairing optimization marks the first work of its kind in the literature. Subsequently, the altitude of the NOMA Unmanned Aerial Vehicle Base Station (UAV-BS) is computed using tools of convex optimization. The obtained results of the proposed methodology solved iteratively substantiate the better performance of NOMA compared to an equivalent OMA UAV-BS deployment. Subsequently, the presented results demonstrate the efficacy of the proposed CSO approach in reducing the required transmission power as well as the operating altitude attributed to lower flying energy consumption of the UAV-BS compared to random and particle swarm optimization based user pairing techniques. |
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