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• W3106152053 abstract "Non-orthogonal multiple access (NOMA) is a rapidly emerging paradigm with the capability to improve the spectral efficiency of data-driven, intelligence inspired, and highly digitized sixth-generation (6G) wireless networks. In the backdrop of ever-evolving NOMA techniques, this article presents a novel resource optimization framework for maximizing the spectral efficiency (SE) of the Internet-of-things (IoT) networks using power domain NOMA. The proposed framework considers a limited number of frequency blocks in the IoT network and provides an optimal power and frequency block allocation method. Different practical constraints like successive interference cancellation (SIC) complexity, ensuring the minimum gap of received power among different IoT equipment over the same frequency block for successful SIC operation, quality of services requirements, and IoT equipment's transmit powers have also been taken into account. Accordingly, a non-convex optimization problem has been formulated for resource management where the objective of spectral efficiency is coupled by both frequency block and power allocation. To effectively solve this problem, the resource optimization problem is decoupled into two subproblems for frequency block assignment and power allocation. A suboptimal algorithm has been designed for frequency block assignment and a new optimal sequential quadratic programming (SQP) approach is employed to solve the non-convex power control subproblem. For the sake of fair comparison, a low complexity suboptimal NOMA power allocation scheme, based on Karush-Kuhn-Tucker (KKT) conditions, and conventional orthogonal multiple access (OMA) scheme are also provided. The results demonstrate that the proposed optimal resource management scheme significantly improves the system performance compared to other schemes." @default.
• W3106152053 created "2020-11-23" @default.
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• W3106152053 date "2020-12-01" @default.
• W3106152053 modified "2023-10-17" @default.
• W3106152053 title "Spectral Efficiency Optimization for Next Generation NOMA-Enabled IoT Networks" @default.
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• W3106152053 doi "https://doi.org/10.1109/tvt.2020.3038387" @default.
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