Energy-efficient simultaneous wireless information and power transfer-enabled orthogonal multiple access under distributed antenna systems
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The application of distributed antenna systems (DAS) to simultaneous wireless information and power transfer (SWIPT)-enabled orthogonal multiple access (OMA) systems is a potential solution for improving the energy efficiency (EE) of next-generation wireless networks, including wireless sensor networks of the Internet of Things (IoT). In this paper, we propose a framework for applying single-selection DAS to the SWIPT-enabled OMA system and analyze the synergetic effects of SWIPT and DAS collabora-tion. Moreover, we jointly optimize the power allocation for OMA signals and power splitting (PS) ratio assignments for SWIPT in the DAS to maximize EE while satisfying the minimum harvested energy and data rate constraints. However, the EE maximization problem is nonconvex; thus, it is challenging to find the optimal solution analytically. To resolve this difficulty, we divide the optimi-zation problem into subproblems and apply an iterative algorithm to find the optimal solution. Numerical results validate the theoretical findings and demonstrate that the proposed framework using SWIPT-enabled OMA in DAS achieves a higher EE than the existing SWIPT-OMA and sum-rate-optimized SWIPT-OMA-DAS.
Abstract
The application of distributed antenna systems (DAS) to simultaneous wireless information and power transfer (SWIPT)-enabled orthogonal multiple access (OMA) systems is a potential solution for improving the energy efficiency (EE) of next-generation wireless networks, including wireless sensor networks of the Internet of Things (IoT). In this paper, we propose a framework for applying single-selection DAS to the SWIPT-enabled OMA system and analyze the synergetic effects of SWIPT and DAS collabora-tion. Moreover, we jointly optimize the power allocation for OMA signals and power splitting (PS) ratio assignments for SWIPT in the DAS to maximize EE while satisfying the minimum harvested energy and data rate constraints. However, the EE maximization problem is nonconvex; thus, it is challenging to find the optimal solution analytically. To resolve this difficulty, we divide the optimi-zation problem into subproblems and apply an iterative algorithm to find the optimal solution. Numerical results validate the theoretical findings and demonstrate that the proposed framework using SWIPT-enabled OMA in DAS achieves a higher EE than the existing SWIPT-OMA and sum-rate-optimized SWIPT-OMA-DAS.
참고문헌 (17)
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