Joint relay selection and energy‐efficient power allocation strategies in energy‐harvesting cooperative NOMA networks

Periodicals(2019)

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摘要
AbstractAbstractIn this paper, the problem of joint relay selection and energy‐efficient power allocation (J‐RS‐EE‐PA) in energy‐harvesting cooperative nonorthogonal multiple‐access networks is considered. In particular, a base station communicates with multiple users via a selected energy‐harvesting relay, such that energy efficiency (EE) is maximized, subject to quality‐of‐service constraints. Four different EE power allocation strategies are considered, namely, global energy efficiency, minimum energy efficiency, product energy efficiency, and sum energy efficiency. For all the aforementioned strategies, the J‐RS‐EE‐PA problem happens to be nonconvex and, thus, is computationally prohibitive. In turn, the J‐RS‐EE‐PA problem is decoupled into two subproblems: (1) relay selection and (2) energy‐efficient power allocation. Different relay selection strategies are explored, and then, computationally efficient algorithms are devised to optimally solve the different EE power allocation strategies for the selected relay. In addition, a low‐complexity solution procedure is proposed to solve the J‐RS‐EE‐PA problem. Simulation results are presented to evaluate the resulting EE of the different relay selection and power allocation strategies in comparison to the formulated J‐RS‐EE‐PA problem. More importantly, the proposed solution procedure is shown to be superior to the different explored relay selection strategies as well as yielding the global optimal solution of the J‐RS‐EE‐PA problem for the global energy efficiency, minimum energy efficiency, product energy efficiency, and sum energy efficiency strategies, while satisfying quality‐of‐service requirements. View Figure The problem of joint relay selection and energy‐efficient power allocation (J‐RS‐EE‐PA) in energy‐harvesting cooperative NOMA networks has been studied, and a low‐complexity solution procedure has been proposed to optimally solve the J‐RS‐EE‐PA problem for global energy efficiency, minimum energy efficiency, product energy efficiency, and sum energy efficiency, while satisfying quality‐of‐service requirements.
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