Deep embedded based energy efficient user grouping and Kookaburra Goshawk optimization for optimal power allocation in Terahertz MIMO-NOMA systems
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Deepali Kishor Borakhade
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Vikram Sadashiv Gawali
Abstract
In recent years, terahertz (THz) communications have gained significant attention due to their potential for supporting ultra-high data rates in future wireless systems, particularly in multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA). However, efficient power allocation and interference management remain critical challenges. In this work, a power allocation approach is devised for THz MIMO-NOMA systems based on fuzzy integrated user pairing. First, the system model is considered and the devised work is based on grouping the users and hybrid precoding. By considering three constraints including position, signal to interference plus noise ratio (SINR), and initial power the user grouping procedure is done. Additionally, the grouping of users is based on the deep-embedded fuzzy clustering, where the measure of distance is modified by applying the above-mentioned three parameters. Next, the hybrid precoding of the THz MIMO-NOMA scheme is done. Later, the power allocation is done based on the user grouping, which is performed concerning the established Kookaburra goshawk optimization (KGO) by employing energy efficiency (EE), a utility function. Here, the KGO is established by unifying the Kookaburra optimization algorithm (KOA) and northern goshawk optimization (NGO). The KGO for power allocation reached the maximal achievable rate, EE, sum rate, utility, and simulation time of 0.958, 0.909, 0.858, 0.899, and 3.579, respectively, for 400 users.
Acknowledgments
I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.
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Research ethics: Not Applicable.
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Informed consent: Not Applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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